ATLAS user analysis on private cloud resources at GoeGrid

NASA Astrophysics Data System (ADS)

Glaser, F.; Nadal Serrano, J.; Grabowski, J.; Quadt, A.

2015-12-01

User analysis job demands can exceed available computing resources, especially before major conferences. ATLAS physics results can potentially be slowed down due to the lack of resources. For these reasons, cloud research and development activities are now included in the skeleton of the ATLAS computing model, which has been extended by using resources from commercial and private cloud providers to satisfy the demands. However, most of these activities are focused on Monte-Carlo production jobs, extending the resources at Tier-2. To evaluate the suitability of the cloud-computing model for user analysis jobs, we developed a framework to launch an ATLAS user analysis cluster in a cloud infrastructure on demand and evaluated two solutions. The first solution is entirely integrated in the Grid infrastructure by using the same mechanism, which is already in use at Tier-2: A designated Panda-Queue is monitored and additional worker nodes are launched in a cloud environment and assigned to a corresponding HTCondor queue according to the demand. Thereby, the use of cloud resources is completely transparent to the user. However, using this approach, submitted user analysis jobs can still suffer from a certain delay introduced by waiting time in the queue and the deployed infrastructure lacks customizability. Therefore, our second solution offers the possibility to easily deploy a totally private, customizable analysis cluster on private cloud resources belonging to the university.

Two-Cloud-Servers-Assisted Secure Outsourcing Multiparty Computation

PubMed Central

Wen, Qiaoyan; Zhang, Hua; Jin, Zhengping; Li, Wenmin

2014-01-01

We focus on how to securely outsource computation task to the cloud and propose a secure outsourcing multiparty computation protocol on lattice-based encrypted data in two-cloud-servers scenario. Our main idea is to transform the outsourced data respectively encrypted by different users' public keys to the ones that are encrypted by the same two private keys of the two assisted servers so that it is feasible to operate on the transformed ciphertexts to compute an encrypted result following the function to be computed. In order to keep the privacy of the result, the two servers cooperatively produce a custom-made result for each user that is authorized to get the result so that all authorized users can recover the desired result while other unauthorized ones including the two servers cannot. Compared with previous research, our protocol is completely noninteractive between any users, and both of the computation and the communication complexities of each user in our solution are independent of the computing function. PMID:24982949

Two-cloud-servers-assisted secure outsourcing multiparty computation.

PubMed

Sun, Yi; Wen, Qiaoyan; Zhang, Yudong; Zhang, Hua; Jin, Zhengping; Li, Wenmin

2014-01-01

We focus on how to securely outsource computation task to the cloud and propose a secure outsourcing multiparty computation protocol on lattice-based encrypted data in two-cloud-servers scenario. Our main idea is to transform the outsourced data respectively encrypted by different users' public keys to the ones that are encrypted by the same two private keys of the two assisted servers so that it is feasible to operate on the transformed ciphertexts to compute an encrypted result following the function to be computed. In order to keep the privacy of the result, the two servers cooperatively produce a custom-made result for each user that is authorized to get the result so that all authorized users can recover the desired result while other unauthorized ones including the two servers cannot. Compared with previous research, our protocol is completely noninteractive between any users, and both of the computation and the communication complexities of each user in our solution are independent of the computing function.

Privacy authentication using key attribute-based encryption in mobile cloud computing

NASA Astrophysics Data System (ADS)

Mohan Kumar, M.; Vijayan, R.

2017-11-01

Mobile Cloud Computing is becoming more popular in nowadays were users of smartphones are getting increased. So, the security level of cloud computing as to be increased. Privacy Authentication using key-attribute based encryption helps the users for business development were the data sharing with the organization using the cloud in a secured manner. In Privacy Authentication the sender of data will have permission to add their receivers to whom the data access provided for others the access denied. In sender application, the user can choose the file which is to be sent to receivers and then that data will be encrypted using Key-attribute based encryption using AES algorithm. In which cipher created, and that stored in Amazon Cloud along with key value and the receiver list.

Challenges in Securing the Interface Between the Cloud and Pervasive Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lagesse, Brent J

2011-01-01

Cloud computing presents an opportunity for pervasive systems to leverage computational and storage resources to accomplish tasks that would not normally be possible on such resource-constrained devices. Cloud computing can enable hardware designers to build lighter systems that last longer and are more mobile. Despite the advantages cloud computing offers to the designers of pervasive systems, there are some limitations of leveraging cloud computing that must be addressed. We take the position that cloud-based pervasive system must be secured holistically and discuss ways this might be accomplished. In this paper, we discuss a pervasive system utilizing cloud computing resources andmore » issues that must be addressed in such a system. In this system, the user's mobile device cannot always have network access to leverage resources from the cloud, so it must make intelligent decisions about what data should be stored locally and what processes should be run locally. As a result of these decisions, the user becomes vulnerable to attacks while interfacing with the pervasive system.« less

Provenance based data integrity checking and verification in cloud environments

PubMed Central

Haq, Inam Ul; Jan, Bilal; Khan, Fakhri Alam; Ahmad, Awais

2017-01-01

Cloud computing is a recent tendency in IT that moves computing and data away from desktop and hand-held devices into large scale processing hubs and data centers respectively. It has been proposed as an effective solution for data outsourcing and on demand computing to control the rising cost of IT setups and management in enterprises. However, with Cloud platforms user’s data is moved into remotely located storages such that users lose control over their data. This unique feature of the Cloud is facing many security and privacy challenges which need to be clearly understood and resolved. One of the important concerns that needs to be addressed is to provide the proof of data integrity, i.e., correctness of the user’s data stored in the Cloud storage. The data in Clouds is physically not accessible to the users. Therefore, a mechanism is required where users can check if the integrity of their valuable data is maintained or compromised. For this purpose some methods are proposed like mirroring, checksumming and using third party auditors amongst others. However, these methods use extra storage space by maintaining multiple copies of data or the presence of a third party verifier is required. In this paper, we address the problem of proving data integrity in Cloud computing by proposing a scheme through which users are able to check the integrity of their data stored in Clouds. In addition, users can track the violation of data integrity if occurred. For this purpose, we utilize a relatively new concept in the Cloud computing called “Data Provenance”. Our scheme is capable to reduce the need of any third party services, additional hardware support and the replication of data items on client side for integrity checking. PMID:28545151

Reviews on Security Issues and Challenges in Cloud Computing

NASA Astrophysics Data System (ADS)

An, Y. Z.; Zaaba, Z. F.; Samsudin, N. F.

2016-11-01

Cloud computing is an Internet-based computing service provided by the third party allowing share of resources and data among devices. It is widely used in many organizations nowadays and becoming more popular because it changes the way of how the Information Technology (IT) of an organization is organized and managed. It provides lots of benefits such as simplicity and lower costs, almost unlimited storage, least maintenance, easy utilization, backup and recovery, continuous availability, quality of service, automated software integration, scalability, flexibility and reliability, easy access to information, elasticity, quick deployment and lower barrier to entry. While there is increasing use of cloud computing service in this new era, the security issues of the cloud computing become a challenges. Cloud computing must be safe and secure enough to ensure the privacy of the users. This paper firstly lists out the architecture of the cloud computing, then discuss the most common security issues of using cloud and some solutions to the security issues since security is one of the most critical aspect in cloud computing due to the sensitivity of user's data.

Acceptance of Cloud Services in Face-to-Face Computer-Supported Collaborative Learning: A Comparison between Single-User Mode and Multi-User Mode

ERIC Educational Resources Information Center

Wang, Chia-Sui; Huang, Yong-Ming

2016-01-01

Face-to-face computer-supported collaborative learning (CSCL) was used extensively to facilitate learning in classrooms. Cloud services not only allow a single user to edit a document, but they also enable multiple users to simultaneously edit a shared document. However, few researchers have compared student acceptance of such services in…

Dynamic virtual machine allocation policy in cloud computing complying with service level agreement using CloudSim

NASA Astrophysics Data System (ADS)

Aneri, Parikh; Sumathy, S.

2017-11-01

Cloud computing provides services over the internet and provides application resources and data to the users based on their demand. Base of the Cloud Computing is consumer provider model. Cloud provider provides resources which consumer can access using cloud computing model in order to build their application based on their demand. Cloud data center is a bulk of resources on shared pool architecture for cloud user to access. Virtualization is the heart of the Cloud computing model, it provides virtual machine as per application specific configuration and those applications are free to choose their own configuration. On one hand, there is huge number of resources and on other hand it has to serve huge number of requests effectively. Therefore, resource allocation policy and scheduling policy play very important role in allocation and managing resources in this cloud computing model. This paper proposes the load balancing policy using Hungarian algorithm. Hungarian Algorithm provides dynamic load balancing policy with a monitor component. Monitor component helps to increase cloud resource utilization by managing the Hungarian algorithm by monitoring its state and altering its state based on artificial intelligent. CloudSim used in this proposal is an extensible toolkit and it simulates cloud computing environment.

An enhanced technique for mobile cloudlet offloading with reduced computation using compression in the cloud

NASA Astrophysics Data System (ADS)

Moro, A. C.; Nadesh, R. K.

2017-11-01

The cloud computing paradigm has transformed the way we do business in today’s world. Services on cloud have come a long way since just providing basic storage or software on demand. One of the fastest growing factor in this is mobile cloud computing. With the option of offloading now available to mobile users, mobile users can offload entire applications onto cloudlets. With the problems regarding availability and limited-storage capacity of these mobile cloudlets, it becomes difficult to decide for the mobile user when to use his local memory or the cloudlets. Hence, we take a look at a fast algorithm that decides whether the mobile user should go for cloudlet or rely on local memory based on an offloading probability. We have partially implemented the algorithm which decides whether the task can be carried out locally or given to a cloudlet. But as it becomes a burden on the mobile devices to perform the complete computation, so we look to offload this on to a cloud in our paper. Also further we use a file compression technique before sending the file onto the cloud to further reduce the load.

AstroCloud, a Cyber-Infrastructure for Astronomy Research: Cloud Computing Environments

NASA Astrophysics Data System (ADS)

Li, C.; Wang, J.; Cui, C.; He, B.; Fan, D.; Yang, Y.; Chen, J.; Zhang, H.; Yu, C.; Xiao, J.; Wang, C.; Cao, Z.; Fan, Y.; Hong, Z.; Li, S.; Mi, L.; Wan, W.; Wang, J.; Yin, S.

2015-09-01

AstroCloud is a cyber-Infrastructure for Astronomy Research initiated by Chinese Virtual Observatory (China-VO) under funding support from NDRC (National Development and Reform commission) and CAS (Chinese Academy of Sciences). Based on CloudStack, an open source software, we set up the cloud computing environment for AstroCloud Project. It consists of five distributed nodes across the mainland of China. Users can use and analysis data in this cloud computing environment. Based on GlusterFS, we built a scalable cloud storage system. Each user has a private space, which can be shared among different virtual machines and desktop systems. With this environments, astronomer can access to astronomical data collected by different telescopes and data centers easily, and data producers can archive their datasets safely.

Job Scheduling with Efficient Resource Monitoring in Cloud Datacenter

PubMed Central

Loganathan, Shyamala; Mukherjee, Saswati

2015-01-01

Cloud computing is an on-demand computing model, which uses virtualization technology to provide cloud resources to users in the form of virtual machines through internet. Being an adaptable technology, cloud computing is an excellent alternative for organizations for forming their own private cloud. Since the resources are limited in these private clouds maximizing the utilization of resources and giving the guaranteed service for the user are the ultimate goal. For that, efficient scheduling is needed. This research reports on an efficient data structure for resource management and resource scheduling technique in a private cloud environment and discusses a cloud model. The proposed scheduling algorithm considers the types of jobs and the resource availability in its scheduling decision. Finally, we conducted simulations using CloudSim and compared our algorithm with other existing methods, like V-MCT and priority scheduling algorithms. PMID:26473166

Job Scheduling with Efficient Resource Monitoring in Cloud Datacenter.

PubMed

Loganathan, Shyamala; Mukherjee, Saswati

2015-01-01

Cloud computing is an on-demand computing model, which uses virtualization technology to provide cloud resources to users in the form of virtual machines through internet. Being an adaptable technology, cloud computing is an excellent alternative for organizations for forming their own private cloud. Since the resources are limited in these private clouds maximizing the utilization of resources and giving the guaranteed service for the user are the ultimate goal. For that, efficient scheduling is needed. This research reports on an efficient data structure for resource management and resource scheduling technique in a private cloud environment and discusses a cloud model. The proposed scheduling algorithm considers the types of jobs and the resource availability in its scheduling decision. Finally, we conducted simulations using CloudSim and compared our algorithm with other existing methods, like V-MCT and priority scheduling algorithms.

Cloud Computing in Support of Applied Learning: A Baseline Study of Infrastructure Design at Southern Polytechnic State University

ERIC Educational Resources Information Center

Conn, Samuel S.; Reichgelt, Han

2013-01-01

Cloud computing represents an architecture and paradigm of computing designed to deliver infrastructure, platforms, and software as constructible computing resources on demand to networked users. As campuses are challenged to better accommodate academic needs for applications and computing environments, cloud computing can provide an accommodating…

Analysis on the security of cloud computing

NASA Astrophysics Data System (ADS)

He, Zhonglin; He, Yuhua

2011-02-01

Cloud computing is a new technology, which is the fusion of computer technology and Internet development. It will lead the revolution of IT and information field. However, in cloud computing data and application software is stored at large data centers, and the management of data and service is not completely trustable, resulting in safety problems, which is the difficult point to improve the quality of cloud service. This paper briefly introduces the concept of cloud computing. Considering the characteristics of cloud computing, it constructs the security architecture of cloud computing. At the same time, with an eye toward the security threats cloud computing faces, several corresponding strategies are provided from the aspect of cloud computing users and service providers.

An Interactive Web-Based Analysis Framework for Remote Sensing Cloud Computing

NASA Astrophysics Data System (ADS)

Wang, X. Z.; Zhang, H. M.; Zhao, J. H.; Lin, Q. H.; Zhou, Y. C.; Li, J. H.

2015-07-01

Spatiotemporal data, especially remote sensing data, are widely used in ecological, geographical, agriculture, and military research and applications. With the development of remote sensing technology, more and more remote sensing data are accumulated and stored in the cloud. An effective way for cloud users to access and analyse these massive spatiotemporal data in the web clients becomes an urgent issue. In this paper, we proposed a new scalable, interactive and web-based cloud computing solution for massive remote sensing data analysis. We build a spatiotemporal analysis platform to provide the end-user with a safe and convenient way to access massive remote sensing data stored in the cloud. The lightweight cloud storage system used to store public data and users' private data is constructed based on open source distributed file system. In it, massive remote sensing data are stored as public data, while the intermediate and input data are stored as private data. The elastic, scalable, and flexible cloud computing environment is built using Docker, which is a technology of open-source lightweight cloud computing container in the Linux operating system. In the Docker container, open-source software such as IPython, NumPy, GDAL, and Grass GIS etc., are deployed. Users can write scripts in the IPython Notebook web page through the web browser to process data, and the scripts will be submitted to IPython kernel to be executed. By comparing the performance of remote sensing data analysis tasks executed in Docker container, KVM virtual machines and physical machines respectively, we can conclude that the cloud computing environment built by Docker makes the greatest use of the host system resources, and can handle more concurrent spatial-temporal computing tasks. Docker technology provides resource isolation mechanism in aspects of IO, CPU, and memory etc., which offers security guarantee when processing remote sensing data in the IPython Notebook. Users can write complex data processing code on the web directly, so they can design their own data processing algorithm.

cryoem-cloud-tools: A software platform to deploy and manage cryo-EM jobs in the cloud.

PubMed

Cianfrocco, Michael A; Lahiri, Indrajit; DiMaio, Frank; Leschziner, Andres E

2018-06-01

Access to streamlined computational resources remains a significant bottleneck for new users of cryo-electron microscopy (cryo-EM). To address this, we have developed tools that will submit cryo-EM analysis routines and atomic model building jobs directly to Amazon Web Services (AWS) from a local computer or laptop. These new software tools ("cryoem-cloud-tools") have incorporated optimal data movement, security, and cost-saving strategies, giving novice users access to complex cryo-EM data processing pipelines. Integrating these tools into the RELION processing pipeline and graphical user interface we determined a 2.2 Å structure of ß-galactosidase in ∼55 hours on AWS. We implemented a similar strategy to submit Rosetta atomic model building and refinement to AWS. These software tools dramatically reduce the barrier for entry of new users to cloud computing for cryo-EM and are freely available at cryoem-tools.cloud. Copyright © 2018. Published by Elsevier Inc.

Integration of end-user Cloud storage for CMS analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Riahi, Hassen; Aimar, Alberto; Ayllon, Alejandro Alvarez

End-user Cloud storage is increasing rapidly in popularity in research communities thanks to the collaboration capabilities it offers, namely synchronisation and sharing. CERN IT has implemented a model of such storage named, CERNBox, integrated with the CERN AuthN and AuthZ services. To exploit the use of the end-user Cloud storage for the distributed data analysis activity, the CMS experiment has started the integration of CERNBox as a Grid resource. This will allow CMS users to make use of their own storage in the Cloud for their analysis activities as well as to benefit from synchronisation and sharing capabilities to achievemore » results faster and more effectively. It will provide an integration model of Cloud storages in the Grid, which is implemented and commissioned over the world’s largest computing Grid infrastructure, Worldwide LHC Computing Grid (WLCG). In this paper, we present the integration strategy and infrastructure changes needed in order to transparently integrate end-user Cloud storage with the CMS distributed computing model. We describe the new challenges faced in data management between Grid and Cloud and how they were addressed, along with details of the support for Cloud storage recently introduced into the WLCG data movement middleware, FTS3. Finally, the commissioning experience of CERNBox for the distributed data analysis activity is also presented.« less

Integration of end-user Cloud storage for CMS analysis

DOE PAGES

Riahi, Hassen; Aimar, Alberto; Ayllon, Alejandro Alvarez; ...

2017-05-19

End-user Cloud storage is increasing rapidly in popularity in research communities thanks to the collaboration capabilities it offers, namely synchronisation and sharing. CERN IT has implemented a model of such storage named, CERNBox, integrated with the CERN AuthN and AuthZ services. To exploit the use of the end-user Cloud storage for the distributed data analysis activity, the CMS experiment has started the integration of CERNBox as a Grid resource. This will allow CMS users to make use of their own storage in the Cloud for their analysis activities as well as to benefit from synchronisation and sharing capabilities to achievemore » results faster and more effectively. It will provide an integration model of Cloud storages in the Grid, which is implemented and commissioned over the world’s largest computing Grid infrastructure, Worldwide LHC Computing Grid (WLCG). In this paper, we present the integration strategy and infrastructure changes needed in order to transparently integrate end-user Cloud storage with the CMS distributed computing model. We describe the new challenges faced in data management between Grid and Cloud and how they were addressed, along with details of the support for Cloud storage recently introduced into the WLCG data movement middleware, FTS3. Finally, the commissioning experience of CERNBox for the distributed data analysis activity is also presented.« less

Heads in the Cloud: A Primer on Neuroimaging Applications of High Performance Computing.

PubMed

Shatil, Anwar S; Younas, Sohail; Pourreza, Hossein; Figley, Chase R

2015-01-01

With larger data sets and more sophisticated analyses, it is becoming increasingly common for neuroimaging researchers to push (or exceed) the limitations of standalone computer workstations. Nonetheless, although high-performance computing platforms such as clusters, grids and clouds are already in routine use by a small handful of neuroimaging researchers to increase their storage and/or computational power, the adoption of such resources by the broader neuroimaging community remains relatively uncommon. Therefore, the goal of the current manuscript is to: 1) inform prospective users about the similarities and differences between computing clusters, grids and clouds; 2) highlight their main advantages; 3) discuss when it may (and may not) be advisable to use them; 4) review some of their potential problems and barriers to access; and finally 5) give a few practical suggestions for how interested new users can start analyzing their neuroimaging data using cloud resources. Although the aim of cloud computing is to hide most of the complexity of the infrastructure management from end-users, we recognize that this can still be an intimidating area for cognitive neuroscientists, psychologists, neurologists, radiologists, and other neuroimaging researchers lacking a strong computational background. Therefore, with this in mind, we have aimed to provide a basic introduction to cloud computing in general (including some of the basic terminology, computer architectures, infrastructure and service models, etc.), a practical overview of the benefits and drawbacks, and a specific focus on how cloud resources can be used for various neuroimaging applications.

Heads in the Cloud: A Primer on Neuroimaging Applications of High Performance Computing

PubMed Central

Shatil, Anwar S.; Younas, Sohail; Pourreza, Hossein; Figley, Chase R.

2015-01-01

With larger data sets and more sophisticated analyses, it is becoming increasingly common for neuroimaging researchers to push (or exceed) the limitations of standalone computer workstations. Nonetheless, although high-performance computing platforms such as clusters, grids and clouds are already in routine use by a small handful of neuroimaging researchers to increase their storage and/or computational power, the adoption of such resources by the broader neuroimaging community remains relatively uncommon. Therefore, the goal of the current manuscript is to: 1) inform prospective users about the similarities and differences between computing clusters, grids and clouds; 2) highlight their main advantages; 3) discuss when it may (and may not) be advisable to use them; 4) review some of their potential problems and barriers to access; and finally 5) give a few practical suggestions for how interested new users can start analyzing their neuroimaging data using cloud resources. Although the aim of cloud computing is to hide most of the complexity of the infrastructure management from end-users, we recognize that this can still be an intimidating area for cognitive neuroscientists, psychologists, neurologists, radiologists, and other neuroimaging researchers lacking a strong computational background. Therefore, with this in mind, we have aimed to provide a basic introduction to cloud computing in general (including some of the basic terminology, computer architectures, infrastructure and service models, etc.), a practical overview of the benefits and drawbacks, and a specific focus on how cloud resources can be used for various neuroimaging applications. PMID:27279746

Impact of office productivity cloud computing on energy consumption and greenhouse gas emissions.

PubMed

Williams, Daniel R; Tang, Yinshan

2013-05-07

Cloud computing is usually regarded as being energy efficient and thus emitting less greenhouse gases (GHG) than traditional forms of computing. When the energy consumption of Microsoft's cloud computing Office 365 (O365) and traditional Office 2010 (O2010) software suites were tested and modeled, some cloud services were found to consume more energy than the traditional form. The developed model in this research took into consideration the energy consumption at the three main stages of data transmission; data center, network, and end user device. Comparable products from each suite were selected and activities were defined for each product to represent a different computing type. Microsoft provided highly confidential data for the data center stage, while the networking and user device stages were measured directly. A new measurement and software apportionment approach was defined and utilized allowing the power consumption of cloud services to be directly measured for the user device stage. Results indicated that cloud computing is more energy efficient for Excel and Outlook which consumed less energy and emitted less GHG than the standalone counterpart. The power consumption of the cloud based Outlook (8%) and Excel (17%) was lower than their traditional counterparts. However, the power consumption of the cloud version of Word was 17% higher than its traditional equivalent. A third mixed access method was also measured for Word which emitted 5% more GHG than the traditional version. It is evident that cloud computing may not provide a unified way forward to reduce energy consumption and GHG. Direct conversion from the standalone package into the cloud provision platform can now consider energy and GHG emissions at the software development and cloud service design stage using the methods described in this research.

The cloud services innovation platform- enabling service-based environmental modelling using infrastructure-as-a-service cloud computing

USDA-ARS?s Scientific Manuscript database

Service oriented architectures allow modelling engines to be hosted over the Internet abstracting physical hardware configuration and software deployments from model users. Many existing environmental models are deployed as desktop applications running on user's personal computers (PCs). Migration ...

Comparative study of internet cloud and cloudlet over wireless mesh networks for real-time applications

NASA Astrophysics Data System (ADS)

Khan, Kashif A.; Wang, Qi; Luo, Chunbo; Wang, Xinheng; Grecos, Christos

2014-05-01

Mobile cloud computing is receiving world-wide momentum for ubiquitous on-demand cloud services for mobile users provided by Amazon, Google etc. with low capital cost. However, Internet-centric clouds introduce wide area network (WAN) delays that are often intolerable for real-time applications such as video streaming. One promising approach to addressing this challenge is to deploy decentralized mini-cloud facility known as cloudlets to enable localized cloud services. When supported by local wireless connectivity, a wireless cloudlet is expected to offer low cost and high performance cloud services for the users. In this work, we implement a realistic framework that comprises both a popular Internet cloud (Amazon Cloud) and a real-world cloudlet (based on Ubuntu Enterprise Cloud (UEC)) for mobile cloud users in a wireless mesh network. We focus on real-time video streaming over the HTTP standard and implement a typical application. We further perform a comprehensive comparative analysis and empirical evaluation of the application's performance when it is delivered over the Internet cloud and the cloudlet respectively. The study quantifies the influence of the two different cloud networking architectures on supporting real-time video streaming. We also enable movement of the users in the wireless mesh network and investigate the effect of user's mobility on mobile cloud computing over the cloudlet and Amazon cloud respectively. Our experimental results demonstrate the advantages of the cloudlet paradigm over its Internet cloud counterpart in supporting the quality of service of real-time applications.

A service brokering and recommendation mechanism for better selecting cloud services.

PubMed

Gui, Zhipeng; Yang, Chaowei; Xia, Jizhe; Huang, Qunying; Liu, Kai; Li, Zhenlong; Yu, Manzhu; Sun, Min; Zhou, Nanyin; Jin, Baoxuan

2014-01-01

Cloud computing is becoming the new generation computing infrastructure, and many cloud vendors provide different types of cloud services. How to choose the best cloud services for specific applications is very challenging. Addressing this challenge requires balancing multiple factors, such as business demands, technologies, policies and preferences in addition to the computing requirements. This paper recommends a mechanism for selecting the best public cloud service at the levels of Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). A systematic framework and associated workflow include cloud service filtration, solution generation, evaluation, and selection of public cloud services. Specifically, we propose the following: a hierarchical information model for integrating heterogeneous cloud information from different providers and a corresponding cloud information collecting mechanism; a cloud service classification model for categorizing and filtering cloud services and an application requirement schema for providing rules for creating application-specific configuration solutions; and a preference-aware solution evaluation mode for evaluating and recommending solutions according to the preferences of application providers. To test the proposed framework and methodologies, a cloud service advisory tool prototype was developed after which relevant experiments were conducted. The results show that the proposed system collects/updates/records the cloud information from multiple mainstream public cloud services in real-time, generates feasible cloud configuration solutions according to user specifications and acceptable cost predication, assesses solutions from multiple aspects (e.g., computing capability, potential cost and Service Level Agreement, SLA) and offers rational recommendations based on user preferences and practical cloud provisioning; and visually presents and compares solutions through an interactive web Graphical User Interface (GUI).

JINR cloud infrastructure evolution

NASA Astrophysics Data System (ADS)

Baranov, A. V.; Balashov, N. A.; Kutovskiy, N. A.; Semenov, R. N.

2016-09-01

To fulfil JINR commitments in different national and international projects related to the use of modern information technologies such as cloud and grid computing as well as to provide a modern tool for JINR users for their scientific research a cloud infrastructure was deployed at Laboratory of Information Technologies of Joint Institute for Nuclear Research. OpenNebula software was chosen as a cloud platform. Initially it was set up in simple configuration with single front-end host and a few cloud nodes. Some custom development was done to tune JINR cloud installation to fit local needs: web form in the cloud web-interface for resources request, a menu item with cloud utilization statistics, user authentication via Kerberos, custom driver for OpenVZ containers. Because of high demand in that cloud service and its resources over-utilization it was re-designed to cover increasing users' needs in capacity, availability and reliability. Recently a new cloud instance has been deployed in high-availability configuration with distributed network file system and additional computing power.

Personalized cloud-based bioinformatics services for research and education: use cases and the elasticHPC package

PubMed Central

2012-01-01

Background Bioinformatics services have been traditionally provided in the form of a web-server that is hosted at institutional infrastructure and serves multiple users. This model, however, is not flexible enough to cope with the increasing number of users, increasing data size, and new requirements in terms of speed and availability of service. The advent of cloud computing suggests a new service model that provides an efficient solution to these problems, based on the concepts of "resources-on-demand" and "pay-as-you-go". However, cloud computing has not yet been introduced within bioinformatics servers due to the lack of usage scenarios and software layers that address the requirements of the bioinformatics domain. Results In this paper, we provide different use case scenarios for providing cloud computing based services, considering both the technical and financial aspects of the cloud computing service model. These scenarios are for individual users seeking computational power as well as bioinformatics service providers aiming at provision of personalized bioinformatics services to their users. We also present elasticHPC, a software package and a library that facilitates the use of high performance cloud computing resources in general and the implementation of the suggested bioinformatics scenarios in particular. Concrete examples that demonstrate the suggested use case scenarios with whole bioinformatics servers and major sequence analysis tools like BLAST are presented. Experimental results with large datasets are also included to show the advantages of the cloud model. Conclusions Our use case scenarios and the elasticHPC package are steps towards the provision of cloud based bioinformatics services, which would help in overcoming the data challenge of recent biological research. All resources related to elasticHPC and its web-interface are available at http://www.elasticHPC.org. PMID:23281941

Personalized cloud-based bioinformatics services for research and education: use cases and the elasticHPC package.

PubMed

El-Kalioby, Mohamed; Abouelhoda, Mohamed; Krüger, Jan; Giegerich, Robert; Sczyrba, Alexander; Wall, Dennis P; Tonellato, Peter

2012-01-01

Bioinformatics services have been traditionally provided in the form of a web-server that is hosted at institutional infrastructure and serves multiple users. This model, however, is not flexible enough to cope with the increasing number of users, increasing data size, and new requirements in terms of speed and availability of service. The advent of cloud computing suggests a new service model that provides an efficient solution to these problems, based on the concepts of "resources-on-demand" and "pay-as-you-go". However, cloud computing has not yet been introduced within bioinformatics servers due to the lack of usage scenarios and software layers that address the requirements of the bioinformatics domain. In this paper, we provide different use case scenarios for providing cloud computing based services, considering both the technical and financial aspects of the cloud computing service model. These scenarios are for individual users seeking computational power as well as bioinformatics service providers aiming at provision of personalized bioinformatics services to their users. We also present elasticHPC, a software package and a library that facilitates the use of high performance cloud computing resources in general and the implementation of the suggested bioinformatics scenarios in particular. Concrete examples that demonstrate the suggested use case scenarios with whole bioinformatics servers and major sequence analysis tools like BLAST are presented. Experimental results with large datasets are also included to show the advantages of the cloud model. Our use case scenarios and the elasticHPC package are steps towards the provision of cloud based bioinformatics services, which would help in overcoming the data challenge of recent biological research. All resources related to elasticHPC and its web-interface are available at http://www.elasticHPC.org.

Future Naval Use of COTS Networking Infrastructure

DTIC Science & Technology

2009-07-01

user to benefit from Google’s vast databases and computational resources. Obviously, the ability to harness the full power of the Cloud could be... Computing Impact Findings Action Items Take-Aways Appendices: Pages 54-68 A. Terms of Reference Document B. Sample Definitions of Cloud ...and definition of Cloud Computing . While Cloud Computing is developing in many variations – including Infrastructure as a Service (IaaS), Platform as

Migrating Educational Data and Services to Cloud Computing: Exploring Benefits and Challenges

ERIC Educational Resources Information Center

Lahiri, Minakshi; Moseley, James L.

2013-01-01

"Cloud computing" is currently the "buzzword" in the Information Technology field. Cloud computing facilitates convenient access to information and software resources as well as easy storage and sharing of files and data, without the end users being aware of the details of the computing technology behind the process. This…

ProteoCloud: a full-featured open source proteomics cloud computing pipeline.

PubMed

Muth, Thilo; Peters, Julian; Blackburn, Jonathan; Rapp, Erdmann; Martens, Lennart

2013-08-02

We here present the ProteoCloud pipeline, a freely available, full-featured cloud-based platform to perform computationally intensive, exhaustive searches in a cloud environment using five different peptide identification algorithms. ProteoCloud is entirely open source, and is built around an easy to use and cross-platform software client with a rich graphical user interface. This client allows full control of the number of cloud instances to initiate and of the spectra to assign for identification. It also enables the user to track progress, and to visualize and interpret the results in detail. Source code, binaries and documentation are all available at http://proteocloud.googlecode.com. Copyright © 2012 Elsevier B.V. All rights reserved.

Volunteered Cloud Computing for Disaster Management

NASA Astrophysics Data System (ADS)

Evans, J. D.; Hao, W.; Chettri, S. R.

2014-12-01

Disaster management relies increasingly on interpreting earth observations and running numerical models; which require significant computing capacity - usually on short notice and at irregular intervals. Peak computing demand during event detection, hazard assessment, or incident response may exceed agency budgets; however some of it can be met through volunteered computing, which distributes subtasks to participating computers via the Internet. This approach has enabled large projects in mathematics, basic science, and climate research to harness the slack computing capacity of thousands of desktop computers. This capacity is likely to diminish as desktops give way to battery-powered mobile devices (laptops, smartphones, tablets) in the consumer market; but as cloud computing becomes commonplace, it may offer significant slack capacity -- if its users are given an easy, trustworthy mechanism for participating. Such a "volunteered cloud computing" mechanism would also offer several advantages over traditional volunteered computing: tasks distributed within a cloud have fewer bandwidth limitations; granular billing mechanisms allow small slices of "interstitial" computing at no marginal cost; and virtual storage volumes allow in-depth, reversible machine reconfiguration. Volunteered cloud computing is especially suitable for "embarrassingly parallel" tasks, including ones requiring large data volumes: examples in disaster management include near-real-time image interpretation, pattern / trend detection, or large model ensembles. In the context of a major disaster, we estimate that cloud users (if suitably informed) might volunteer hundreds to thousands of CPU cores across a large provider such as Amazon Web Services. To explore this potential, we are building a volunteered cloud computing platform and targeting it to a disaster management context. Using a lightweight, fault-tolerant network protocol, this platform helps cloud users join parallel computing projects; automates reconfiguration of their virtual machines; ensures accountability for donated computing; and optimizes the use of "interstitial" computing. Initial applications include fire detection from multispectral satellite imagery and flood risk mapping through hydrological simulations.

Secure Encapsulation and Publication of Biological Services in the Cloud Computing Environment

PubMed Central

Zhang, Weizhe; Wang, Xuehui; Lu, Bo; Kim, Tai-hoon

2013-01-01

Secure encapsulation and publication for bioinformatics software products based on web service are presented, and the basic function of biological information is realized in the cloud computing environment. In the encapsulation phase, the workflow and function of bioinformatics software are conducted, the encapsulation interfaces are designed, and the runtime interaction between users and computers is simulated. In the publication phase, the execution and management mechanisms and principles of the GRAM components are analyzed. The functions such as remote user job submission and job status query are implemented by using the GRAM components. The services of bioinformatics software are published to remote users. Finally the basic prototype system of the biological cloud is achieved. PMID:24078906

Secure encapsulation and publication of biological services in the cloud computing environment.

PubMed

Zhang, Weizhe; Wang, Xuehui; Lu, Bo; Kim, Tai-hoon

2013-01-01

Secure encapsulation and publication for bioinformatics software products based on web service are presented, and the basic function of biological information is realized in the cloud computing environment. In the encapsulation phase, the workflow and function of bioinformatics software are conducted, the encapsulation interfaces are designed, and the runtime interaction between users and computers is simulated. In the publication phase, the execution and management mechanisms and principles of the GRAM components are analyzed. The functions such as remote user job submission and job status query are implemented by using the GRAM components. The services of bioinformatics software are published to remote users. Finally the basic prototype system of the biological cloud is achieved.

Prototyping manufacturing in the cloud

NASA Astrophysics Data System (ADS)

Ciortea, E. M.

2017-08-01

This paper attempts a theoretical approach to cloud systems with impacts on production systems. I call systems as cloud computing because form a relatively new concept in the field of informatics, representing an overall distributed computing services, applications, access to information and data storage without the user to know the physical location and configuration of systems. The advantages of this approach are especially computing speed and storage capacity without investment in additional configurations, synchronizing user data, data processing using web applications. The disadvantage is that it wants to identify a solution for data security, leading to mistrust users. The case study is applied to a module of the system of production, because the system is complex.

Web N.0, the New Development Trend of Internet

NASA Astrophysics Data System (ADS)

Sun, Zhiguo; Wang, Wensheng

This article analyzes the Internet basic theory, the network foundation environment and the user behavior change and so on, Which analyzes the development tendency of existing partial Internet products in the future Internet environment. The article also hot on the concept of cloud computing, Demonstrates the relation between Cloud Computing and Web 2.0 from the angle of Cloud-based end-user applications, The possibly killing application in the future was discussed.

A Service Brokering and Recommendation Mechanism for Better Selecting Cloud Services

PubMed Central

Gui, Zhipeng; Yang, Chaowei; Xia, Jizhe; Huang, Qunying; Liu, Kai; Li, Zhenlong; Yu, Manzhu; Sun, Min; Zhou, Nanyin; Jin, Baoxuan

2014-01-01

Cloud computing is becoming the new generation computing infrastructure, and many cloud vendors provide different types of cloud services. How to choose the best cloud services for specific applications is very challenging. Addressing this challenge requires balancing multiple factors, such as business demands, technologies, policies and preferences in addition to the computing requirements. This paper recommends a mechanism for selecting the best public cloud service at the levels of Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). A systematic framework and associated workflow include cloud service filtration, solution generation, evaluation, and selection of public cloud services. Specifically, we propose the following: a hierarchical information model for integrating heterogeneous cloud information from different providers and a corresponding cloud information collecting mechanism; a cloud service classification model for categorizing and filtering cloud services and an application requirement schema for providing rules for creating application-specific configuration solutions; and a preference-aware solution evaluation mode for evaluating and recommending solutions according to the preferences of application providers. To test the proposed framework and methodologies, a cloud service advisory tool prototype was developed after which relevant experiments were conducted. The results show that the proposed system collects/updates/records the cloud information from multiple mainstream public cloud services in real-time, generates feasible cloud configuration solutions according to user specifications and acceptable cost predication, assesses solutions from multiple aspects (e.g., computing capability, potential cost and Service Level Agreement, SLA) and offers rational recommendations based on user preferences and practical cloud provisioning; and visually presents and compares solutions through an interactive web Graphical User Interface (GUI). PMID:25170937

Seismic waveform modeling over cloud

NASA Astrophysics Data System (ADS)

Luo, Cong; Friederich, Wolfgang

2016-04-01

With the fast growing computational technologies, numerical simulation of seismic wave propagation achieved huge successes. Obtaining the synthetic waveforms through numerical simulation receives an increasing amount of attention from seismologists. However, computational seismology is a data-intensive research field, and the numerical packages usually come with a steep learning curve. Users are expected to master considerable amount of computer knowledge and data processing skills. Training users to use the numerical packages, correctly access and utilize the computational resources is a troubled task. In addition to that, accessing to HPC is also a common difficulty for many users. To solve these problems, a cloud based solution dedicated on shallow seismic waveform modeling has been developed with the state-of-the-art web technologies. It is a web platform integrating both software and hardware with multilayer architecture: a well designed SQL database serves as the data layer, HPC and dedicated pipeline for it is the business layer. Through this platform, users will no longer need to compile and manipulate various packages on the local machine within local network to perform a simulation. By providing users professional access to the computational code through its interfaces and delivering our computational resources to the users over cloud, users can customize the simulation at expert-level, submit and run the job through it.

A service based adaptive U-learning system using UX.

PubMed

Jeong, Hwa-Young; Yi, Gangman

2014-01-01

In recent years, traditional development techniques for e-learning systems have been changing to become more convenient and efficient. One new technology in the development of application systems includes both cloud and ubiquitous computing. Cloud computing can support learning system processes by using services while ubiquitous computing can provide system operation and management via a high performance technical process and network. In the cloud computing environment, a learning service application can provide a business module or process to the user via the internet. This research focuses on providing the learning material and processes of courses by learning units using the services in a ubiquitous computing environment. And we also investigate functions that support users' tailored materials according to their learning style. That is, we analyzed the user's data and their characteristics in accordance with their user experience. We subsequently applied the learning process to fit on their learning performance and preferences. Finally, we demonstrate how the proposed system outperforms learning effects to learners better than existing techniques.

A Service Based Adaptive U-Learning System Using UX

PubMed Central

Jeong, Hwa-Young

2014-01-01

In recent years, traditional development techniques for e-learning systems have been changing to become more convenient and efficient. One new technology in the development of application systems includes both cloud and ubiquitous computing. Cloud computing can support learning system processes by using services while ubiquitous computing can provide system operation and management via a high performance technical process and network. In the cloud computing environment, a learning service application can provide a business module or process to the user via the internet. This research focuses on providing the learning material and processes of courses by learning units using the services in a ubiquitous computing environment. And we also investigate functions that support users' tailored materials according to their learning style. That is, we analyzed the user's data and their characteristics in accordance with their user experience. We subsequently applied the learning process to fit on their learning performance and preferences. Finally, we demonstrate how the proposed system outperforms learning effects to learners better than existing techniques. PMID:25147832

Cloud computing geospatial application for water resources based on free and open source software and open standards - a prototype

NASA Astrophysics Data System (ADS)

Delipetrev, Blagoj

2016-04-01

Presently, most of the existing software is desktop-based, designed to work on a single computer, which represents a major limitation in many ways, starting from limited computer processing, storage power, accessibility, availability, etc. The only feasible solution lies in the web and cloud. This abstract presents research and development of a cloud computing geospatial application for water resources based on free and open source software and open standards using hybrid deployment model of public - private cloud, running on two separate virtual machines (VMs). The first one (VM1) is running on Amazon web services (AWS) and the second one (VM2) is running on a Xen cloud platform. The presented cloud application is developed using free and open source software, open standards and prototype code. The cloud application presents a framework how to develop specialized cloud geospatial application that needs only a web browser to be used. This cloud application is the ultimate collaboration geospatial platform because multiple users across the globe with internet connection and browser can jointly model geospatial objects, enter attribute data and information, execute algorithms, and visualize results. The presented cloud application is: available all the time, accessible from everywhere, it is scalable, works in a distributed computer environment, it creates a real-time multiuser collaboration platform, the programing languages code and components are interoperable, and it is flexible in including additional components. The cloud geospatial application is implemented as a specialized water resources application with three web services for 1) data infrastructure (DI), 2) support for water resources modelling (WRM), 3) user management. The web services are running on two VMs that are communicating over the internet providing services to users. The application was tested on the Zletovica river basin case study with concurrent multiple users. The application is a state-of-the-art cloud geospatial collaboration platform. The presented solution is a prototype and can be used as a foundation for developing of any specialized cloud geospatial applications. Further research will be focused on distributing the cloud application on additional VMs, testing the scalability and availability of services.

Processing Shotgun Proteomics Data on the Amazon Cloud with the Trans-Proteomic Pipeline*

PubMed Central

Slagel, Joseph; Mendoza, Luis; Shteynberg, David; Deutsch, Eric W.; Moritz, Robert L.

2015-01-01

Cloud computing, where scalable, on-demand compute cycles and storage are available as a service, has the potential to accelerate mass spectrometry-based proteomics research by providing simple, expandable, and affordable large-scale computing to all laboratories regardless of location or information technology expertise. We present new cloud computing functionality for the Trans-Proteomic Pipeline, a free and open-source suite of tools for the processing and analysis of tandem mass spectrometry datasets. Enabled with Amazon Web Services cloud computing, the Trans-Proteomic Pipeline now accesses large scale computing resources, limited only by the available Amazon Web Services infrastructure, for all users. The Trans-Proteomic Pipeline runs in an environment fully hosted on Amazon Web Services, where all software and data reside on cloud resources to tackle large search studies. In addition, it can also be run on a local computer with computationally intensive tasks launched onto the Amazon Elastic Compute Cloud service to greatly decrease analysis times. We describe the new Trans-Proteomic Pipeline cloud service components, compare the relative performance and costs of various Elastic Compute Cloud service instance types, and present on-line tutorials that enable users to learn how to deploy cloud computing technology rapidly with the Trans-Proteomic Pipeline. We provide tools for estimating the necessary computing resources and costs given the scale of a job and demonstrate the use of cloud enabled Trans-Proteomic Pipeline by performing over 1100 tandem mass spectrometry files through four proteomic search engines in 9 h and at a very low cost. PMID:25418363

Processing shotgun proteomics data on the Amazon cloud with the trans-proteomic pipeline.

PubMed

Slagel, Joseph; Mendoza, Luis; Shteynberg, David; Deutsch, Eric W; Moritz, Robert L

2015-02-01

Cloud computing, where scalable, on-demand compute cycles and storage are available as a service, has the potential to accelerate mass spectrometry-based proteomics research by providing simple, expandable, and affordable large-scale computing to all laboratories regardless of location or information technology expertise. We present new cloud computing functionality for the Trans-Proteomic Pipeline, a free and open-source suite of tools for the processing and analysis of tandem mass spectrometry datasets. Enabled with Amazon Web Services cloud computing, the Trans-Proteomic Pipeline now accesses large scale computing resources, limited only by the available Amazon Web Services infrastructure, for all users. The Trans-Proteomic Pipeline runs in an environment fully hosted on Amazon Web Services, where all software and data reside on cloud resources to tackle large search studies. In addition, it can also be run on a local computer with computationally intensive tasks launched onto the Amazon Elastic Compute Cloud service to greatly decrease analysis times. We describe the new Trans-Proteomic Pipeline cloud service components, compare the relative performance and costs of various Elastic Compute Cloud service instance types, and present on-line tutorials that enable users to learn how to deploy cloud computing technology rapidly with the Trans-Proteomic Pipeline. We provide tools for estimating the necessary computing resources and costs given the scale of a job and demonstrate the use of cloud enabled Trans-Proteomic Pipeline by performing over 1100 tandem mass spectrometry files through four proteomic search engines in 9 h and at a very low cost. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

Towards Efficient Scientific Data Management Using Cloud Storage

NASA Technical Reports Server (NTRS)

He, Qiming

2013-01-01

A software prototype allows users to backup and restore data to/from both public and private cloud storage such as Amazon's S3 and NASA's Nebula. Unlike other off-the-shelf tools, this software ensures user data security in the cloud (through encryption), and minimizes users operating costs by using space- and bandwidth-efficient compression and incremental backup. Parallel data processing utilities have also been developed by using massively scalable cloud computing in conjunction with cloud storage. One of the innovations in this software is using modified open source components to work with a private cloud like NASA Nebula. Another innovation is porting the complex backup to- cloud software to embedded Linux, running on the home networking devices, in order to benefit more users.

Cloud Computing: Should It Be Integrated into the Curriculum?

ERIC Educational Resources Information Center

Changchit, Chuleeporn

2015-01-01

Cloud computing has become increasingly popular among users and businesses around the world, and education is no exception. Cloud computing can bring an increased number of benefits to an educational setting, not only for its cost effectiveness, but also for the thirst for technology that college students have today, which allows learning and…

Transitioning ISR architecture into the cloud

NASA Astrophysics Data System (ADS)

Lash, Thomas D.

2012-06-01

Emerging cloud computing platforms offer an ideal opportunity for Intelligence, Surveillance, and Reconnaissance (ISR) intelligence analysis. Cloud computing platforms help overcome challenges and limitations of traditional ISR architectures. Modern ISR architectures can benefit from examining commercial cloud applications, especially as they relate to user experience, usage profiling, and transformational business models. This paper outlines legacy ISR architectures and their limitations, presents an overview of cloud technologies and their applications to the ISR intelligence mission, and presents an idealized ISR architecture implemented with cloud computing.

A Cloud-Based Simulation Architecture for Pandemic Influenza Simulation

PubMed Central

Eriksson, Henrik; Raciti, Massimiliano; Basile, Maurizio; Cunsolo, Alessandro; Fröberg, Anders; Leifler, Ola; Ekberg, Joakim; Timpka, Toomas

2011-01-01

High-fidelity simulations of pandemic outbreaks are resource consuming. Cluster-based solutions have been suggested for executing such complex computations. We present a cloud-based simulation architecture that utilizes computing resources both locally available and dynamically rented online. The approach uses the Condor framework for job distribution and management of the Amazon Elastic Computing Cloud (EC2) as well as local resources. The architecture has a web-based user interface that allows users to monitor and control simulation execution. In a benchmark test, the best cost-adjusted performance was recorded for the EC2 H-CPU Medium instance, while a field trial showed that the job configuration had significant influence on the execution time and that the network capacity of the master node could become a bottleneck. We conclude that it is possible to develop a scalable simulation environment that uses cloud-based solutions, while providing an easy-to-use graphical user interface. PMID:22195089

Cloud Computing for radiologists.

PubMed

Kharat, Amit T; Safvi, Amjad; Thind, Ss; Singh, Amarjit

2012-07-01

Cloud computing is a concept wherein a computer grid is created using the Internet with the sole purpose of utilizing shared resources such as computer software, hardware, on a pay-per-use model. Using Cloud computing, radiology users can efficiently manage multimodality imaging units by using the latest software and hardware without paying huge upfront costs. Cloud computing systems usually work on public, private, hybrid, or community models. Using the various components of a Cloud, such as applications, client, infrastructure, storage, services, and processing power, Cloud computing can help imaging units rapidly scale and descale operations and avoid huge spending on maintenance of costly applications and storage. Cloud computing allows flexibility in imaging. It sets free radiology from the confines of a hospital and creates a virtual mobile office. The downsides to Cloud computing involve security and privacy issues which need to be addressed to ensure the success of Cloud computing in the future.

Cloud Computing for radiologists

PubMed Central

Kharat, Amit T; Safvi, Amjad; Thind, SS; Singh, Amarjit

2012-01-01

Cloud computing is a concept wherein a computer grid is created using the Internet with the sole purpose of utilizing shared resources such as computer software, hardware, on a pay-per-use model. Using Cloud computing, radiology users can efficiently manage multimodality imaging units by using the latest software and hardware without paying huge upfront costs. Cloud computing systems usually work on public, private, hybrid, or community models. Using the various components of a Cloud, such as applications, client, infrastructure, storage, services, and processing power, Cloud computing can help imaging units rapidly scale and descale operations and avoid huge spending on maintenance of costly applications and storage. Cloud computing allows flexibility in imaging. It sets free radiology from the confines of a hospital and creates a virtual mobile office. The downsides to Cloud computing involve security and privacy issues which need to be addressed to ensure the success of Cloud computing in the future. PMID:23599560

Behavior Life Style Analysis for Mobile Sensory Data in Cloud Computing through MapReduce

PubMed Central

Hussain, Shujaat; Bang, Jae Hun; Han, Manhyung; Ahmed, Muhammad Idris; Amin, Muhammad Bilal; Lee, Sungyoung; Nugent, Chris; McClean, Sally; Scotney, Bryan; Parr, Gerard

2014-01-01

Cloud computing has revolutionized healthcare in today's world as it can be seamlessly integrated into a mobile application and sensor devices. The sensory data is then transferred from these devices to the public and private clouds. In this paper, a hybrid and distributed environment is built which is capable of collecting data from the mobile phone application and store it in the cloud. We developed an activity recognition application and transfer the data to the cloud for further processing. Big data technology Hadoop MapReduce is employed to analyze the data and create user timeline of user's activities. These activities are visualized to find useful health analytics and trends. In this paper a big data solution is proposed to analyze the sensory data and give insights into user behavior and lifestyle trends. PMID:25420151

Behavior life style analysis for mobile sensory data in cloud computing through MapReduce.

PubMed

Hussain, Shujaat; Bang, Jae Hun; Han, Manhyung; Ahmed, Muhammad Idris; Amin, Muhammad Bilal; Lee, Sungyoung; Nugent, Chris; McClean, Sally; Scotney, Bryan; Parr, Gerard

2014-11-20

Cloud computing has revolutionized healthcare in today's world as it can be seamlessly integrated into a mobile application and sensor devices. The sensory data is then transferred from these devices to the public and private clouds. In this paper, a hybrid and distributed environment is built which is capable of collecting data from the mobile phone application and store it in the cloud. We developed an activity recognition application and transfer the data to the cloud for further processing. Big data technology Hadoop MapReduce is employed to analyze the data and create user timeline of user's activities. These activities are visualized to find useful health analytics and trends. In this paper a big data solution is proposed to analyze the sensory data and give insights into user behavior and lifestyle trends.

Two-Level Verification of Data Integrity for Data Storage in Cloud Computing

NASA Astrophysics Data System (ADS)

Xu, Guangwei; Chen, Chunlin; Wang, Hongya; Zang, Zhuping; Pang, Mugen; Jiang, Ping

Data storage in cloud computing can save capital expenditure and relive burden of storage management for users. As the lose or corruption of files stored may happen, many researchers focus on the verification of data integrity. However, massive users often bring large numbers of verifying tasks for the auditor. Moreover, users also need to pay extra fee for these verifying tasks beyond storage fee. Therefore, we propose a two-level verification of data integrity to alleviate these problems. The key idea is to routinely verify the data integrity by users and arbitrate the challenge between the user and cloud provider by the auditor according to the MACs and ϕ values. The extensive performance simulations show that the proposed scheme obviously decreases auditor's verifying tasks and the ratio of wrong arbitration.

Feasibility and demonstration of a cloud-based RIID analysis system

NASA Astrophysics Data System (ADS)

Wright, Michael C.; Hertz, Kristin L.; Johnson, William C.; Sword, Eric D.; Younkin, James R.; Sadler, Lorraine E.

2015-06-01

A significant limitation in the operational utility of handheld and backpack radioisotope identifiers (RIIDs) is the inability of their onboard algorithms to accurately and reliably identify the isotopic sources of the measured gamma-ray energy spectrum. A possible solution is to move the spectral analysis computations to an external device, the cloud, where significantly greater capabilities are available. The implementation and demonstration of a prototype cloud-based RIID analysis system have shown this type of system to be feasible with currently available communication and computational technology. A system study has shown that the potential user community could derive significant benefits from an appropriately implemented cloud-based analysis system and has identified the design and operational characteristics required by the users and stakeholders for such a system. A general description of the hardware and software necessary to implement reliable cloud-based analysis, the value of the cloud expressed by the user community, and the aspects of the cloud implemented in the demonstrations are discussed.

Privacy-Preserving Location-Based Service Scheme for Mobile Sensing Data.

PubMed

Xie, Qingqing; Wang, Liangmin

2016-11-25

With the wide use of mobile sensing application, more and more location-embedded data are collected and stored in mobile clouds, such as iCloud, Samsung cloud, etc. Using these data, the cloud service provider (CSP) can provide location-based service (LBS) for users. However, the mobile cloud is untrustworthy. The privacy concerns force the sensitive locations to be stored on the mobile cloud in an encrypted form. However, this brings a great challenge to utilize these data to provide efficient LBS. To solve this problem, we propose a privacy-preserving LBS scheme for mobile sensing data, based on the RSA (for Rivest, Shamir and Adleman) algorithm and ciphertext policy attribute-based encryption (CP-ABE) scheme. The mobile cloud can perform location distance computing and comparison efficiently for authorized users, without location privacy leakage. In the end, theoretical security analysis and experimental evaluation demonstrate that our scheme is secure against the chosen plaintext attack (CPA) and efficient enough for practical applications in terms of user side computation overhead.

Privacy-Preserving Location-Based Service Scheme for Mobile Sensing Data †

PubMed Central

Xie, Qingqing; Wang, Liangmin

2016-01-01

With the wide use of mobile sensing application, more and more location-embedded data are collected and stored in mobile clouds, such as iCloud, Samsung cloud, etc. Using these data, the cloud service provider (CSP) can provide location-based service (LBS) for users. However, the mobile cloud is untrustworthy. The privacy concerns force the sensitive locations to be stored on the mobile cloud in an encrypted form. However, this brings a great challenge to utilize these data to provide efficient LBS. To solve this problem, we propose a privacy-preserving LBS scheme for mobile sensing data, based on the RSA (for Rivest, Shamir and Adleman) algorithm and ciphertext policy attribute-based encryption (CP-ABE) scheme. The mobile cloud can perform location distance computing and comparison efficiently for authorized users, without location privacy leakage. In the end, theoretical security analysis and experimental evaluation demonstrate that our scheme is secure against the chosen plaintext attack (CPA) and efficient enough for practical applications in terms of user side computation overhead. PMID:27897984

Cloud Infrastructures for In Silico Drug Discovery: Economic and Practical Aspects

PubMed Central

Clematis, Andrea; Quarati, Alfonso; Cesini, Daniele; Milanesi, Luciano; Merelli, Ivan

2013-01-01

Cloud computing opens new perspectives for small-medium biotechnology laboratories that need to perform bioinformatics analysis in a flexible and effective way. This seems particularly true for hybrid clouds that couple the scalability offered by general-purpose public clouds with the greater control and ad hoc customizations supplied by the private ones. A hybrid cloud broker, acting as an intermediary between users and public providers, can support customers in the selection of the most suitable offers, optionally adding the provisioning of dedicated services with higher levels of quality. This paper analyses some economic and practical aspects of exploiting cloud computing in a real research scenario for the in silico drug discovery in terms of requirements, costs, and computational load based on the number of expected users. In particular, our work is aimed at supporting both the researchers and the cloud broker delivering an IaaS cloud infrastructure for biotechnology laboratories exposing different levels of nonfunctional requirements. PMID:24106693

An intelligent and secure system for predicting and preventing Zika virus outbreak using Fog computing

NASA Astrophysics Data System (ADS)

Sareen, Sanjay; Gupta, Sunil Kumar; Sood, Sandeep K.

2017-10-01

Zika virus is a mosquito-borne disease that spreads very quickly in different parts of the world. In this article, we proposed a system to prevent and control the spread of Zika virus disease using integration of Fog computing, cloud computing, mobile phones and the Internet of things (IoT)-based sensor devices. Fog computing is used as an intermediary layer between the cloud and end users to reduce the latency time and extra communication cost that is usually found high in cloud-based systems. A fuzzy k-nearest neighbour is used to diagnose the possibly infected users, and Google map web service is used to provide the geographic positioning system (GPS)-based risk assessment to prevent the outbreak. It is used to represent each Zika virus (ZikaV)-infected user, mosquito-dense sites and breeding sites on the Google map that help the government healthcare authorities to control such risk-prone areas effectively and efficiently. The proposed system is deployed on Amazon EC2 cloud to evaluate its performance and accuracy using data set for 2 million users. Our system provides high accuracy of 94.5% for initial diagnosis of different users according to their symptoms and appropriate GPS-based risk assessment.

Intelligent cloud computing security using genetic algorithm as a computational tools

NASA Astrophysics Data System (ADS)

Razuky AL-Shaikhly, Mazin H.

2018-05-01

An essential change had occurred in the field of Information Technology which represented with cloud computing, cloud giving virtual assets by means of web yet awesome difficulties in the field of information security and security assurance. Currently main problem with cloud computing is how to improve privacy and security for cloud “cloud is critical security”. This paper attempts to solve cloud security by using intelligent system with genetic algorithm as wall to provide cloud data secure, all services provided by cloud must detect who receive and register it to create list of users (trusted or un-trusted) depend on behavior. The execution of present proposal has shown great outcome.

A keyword searchable attribute-based encryption scheme with attribute update for cloud storage.

PubMed

Wang, Shangping; Ye, Jian; Zhang, Yaling

2018-01-01

Ciphertext-policy attribute-based encryption (CP-ABE) scheme is a new type of data encryption primitive, which is very suitable for data cloud storage for its fine-grained access control. Keyword-based searchable encryption scheme enables users to quickly find interesting data stored in the cloud server without revealing any information of the searched keywords. In this work, we provide a keyword searchable attribute-based encryption scheme with attribute update for cloud storage, which is a combination of attribute-based encryption scheme and keyword searchable encryption scheme. The new scheme supports the user's attribute update, especially in our new scheme when a user's attribute need to be updated, only the user's secret key related with the attribute need to be updated, while other user's secret key and the ciphertexts related with this attribute need not to be updated with the help of the cloud server. In addition, we outsource the operation with high computation cost to cloud server to reduce the user's computational burden. Moreover, our scheme is proven to be semantic security against chosen ciphertext-policy and chosen plaintext attack in the general bilinear group model. And our scheme is also proven to be semantic security against chosen keyword attack under bilinear Diffie-Hellman (BDH) assumption.

A keyword searchable attribute-based encryption scheme with attribute update for cloud storage

PubMed Central

Wang, Shangping; Zhang, Yaling

2018-01-01

Ciphertext-policy attribute-based encryption (CP-ABE) scheme is a new type of data encryption primitive, which is very suitable for data cloud storage for its fine-grained access control. Keyword-based searchable encryption scheme enables users to quickly find interesting data stored in the cloud server without revealing any information of the searched keywords. In this work, we provide a keyword searchable attribute-based encryption scheme with attribute update for cloud storage, which is a combination of attribute-based encryption scheme and keyword searchable encryption scheme. The new scheme supports the user's attribute update, especially in our new scheme when a user's attribute need to be updated, only the user's secret key related with the attribute need to be updated, while other user's secret key and the ciphertexts related with this attribute need not to be updated with the help of the cloud server. In addition, we outsource the operation with high computation cost to cloud server to reduce the user's computational burden. Moreover, our scheme is proven to be semantic security against chosen ciphertext-policy and chosen plaintext attack in the general bilinear group model. And our scheme is also proven to be semantic security against chosen keyword attack under bilinear Diffie-Hellman (BDH) assumption. PMID:29795577

Tavaxy: integrating Taverna and Galaxy workflows with cloud computing support.

PubMed

Abouelhoda, Mohamed; Issa, Shadi Alaa; Ghanem, Moustafa

2012-05-04

Over the past decade the workflow system paradigm has evolved as an efficient and user-friendly approach for developing complex bioinformatics applications. Two popular workflow systems that have gained acceptance by the bioinformatics community are Taverna and Galaxy. Each system has a large user-base and supports an ever-growing repository of application workflows. However, workflows developed for one system cannot be imported and executed easily on the other. The lack of interoperability is due to differences in the models of computation, workflow languages, and architectures of both systems. This lack of interoperability limits sharing of workflows between the user communities and leads to duplication of development efforts. In this paper, we present Tavaxy, a stand-alone system for creating and executing workflows based on using an extensible set of re-usable workflow patterns. Tavaxy offers a set of new features that simplify and enhance the development of sequence analysis applications: It allows the integration of existing Taverna and Galaxy workflows in a single environment, and supports the use of cloud computing capabilities. The integration of existing Taverna and Galaxy workflows is supported seamlessly at both run-time and design-time levels, based on the concepts of hierarchical workflows and workflow patterns. The use of cloud computing in Tavaxy is flexible, where the users can either instantiate the whole system on the cloud, or delegate the execution of certain sub-workflows to the cloud infrastructure. Tavaxy reduces the workflow development cycle by introducing the use of workflow patterns to simplify workflow creation. It enables the re-use and integration of existing (sub-) workflows from Taverna and Galaxy, and allows the creation of hybrid workflows. Its additional features exploit recent advances in high performance cloud computing to cope with the increasing data size and complexity of analysis.The system can be accessed either through a cloud-enabled web-interface or downloaded and installed to run within the user's local environment. All resources related to Tavaxy are available at http://www.tavaxy.org.

Homomorphic encryption experiments on IBM's cloud quantum computing platform

NASA Astrophysics Data System (ADS)

Huang, He-Liang; Zhao, You-Wei; Li, Tan; Li, Feng-Guang; Du, Yu-Tao; Fu, Xiang-Qun; Zhang, Shuo; Wang, Xiang; Bao, Wan-Su

2017-02-01

Quantum computing has undergone rapid development in recent years. Owing to limitations on scalability, personal quantum computers still seem slightly unrealistic in the near future. The first practical quantum computer for ordinary users is likely to be on the cloud. However, the adoption of cloud computing is possible only if security is ensured. Homomorphic encryption is a cryptographic protocol that allows computation to be performed on encrypted data without decrypting them, so it is well suited to cloud computing. Here, we first applied homomorphic encryption on IBM's cloud quantum computer platform. In our experiments, we successfully implemented a quantum algorithm for linear equations while protecting our privacy. This demonstration opens a feasible path to the next stage of development of cloud quantum information technology.

An Efficient Virtual Machine Consolidation Scheme for Multimedia Cloud Computing.

PubMed

Han, Guangjie; Que, Wenhui; Jia, Gangyong; Shu, Lei

2016-02-18

Cloud computing has innovated the IT industry in recent years, as it can delivery subscription-based services to users in the pay-as-you-go model. Meanwhile, multimedia cloud computing is emerging based on cloud computing to provide a variety of media services on the Internet. However, with the growing popularity of multimedia cloud computing, its large energy consumption cannot only contribute to greenhouse gas emissions, but also result in the rising of cloud users' costs. Therefore, the multimedia cloud providers should try to minimize its energy consumption as much as possible while satisfying the consumers' resource requirements and guaranteeing quality of service (QoS). In this paper, we have proposed a remaining utilization-aware (RUA) algorithm for virtual machine (VM) placement, and a power-aware algorithm (PA) is proposed to find proper hosts to shut down for energy saving. These two algorithms have been combined and applied to cloud data centers for completing the process of VM consolidation. Simulation results have shown that there exists a trade-off between the cloud data center's energy consumption and service-level agreement (SLA) violations. Besides, the RUA algorithm is able to deal with variable workload to prevent hosts from overloading after VM placement and to reduce the SLA violations dramatically.

Cloud computing task scheduling strategy based on improved differential evolution algorithm

NASA Astrophysics Data System (ADS)

Ge, Junwei; He, Qian; Fang, Yiqiu

2017-04-01

In order to optimize the cloud computing task scheduling scheme, an improved differential evolution algorithm for cloud computing task scheduling is proposed. Firstly, the cloud computing task scheduling model, according to the model of the fitness function, and then used improved optimization calculation of the fitness function of the evolutionary algorithm, according to the evolution of generation of dynamic selection strategy through dynamic mutation strategy to ensure the global and local search ability. The performance test experiment was carried out in the CloudSim simulation platform, the experimental results show that the improved differential evolution algorithm can reduce the cloud computing task execution time and user cost saving, good implementation of the optimal scheduling of cloud computing tasks.

Public-Private Partnership: Joint recommendations to improve downloads of large Earth observation data

NASA Astrophysics Data System (ADS)

Ramachandran, R.; Murphy, K. J.; Baynes, K.; Lynnes, C.

2016-12-01

With the volume of Earth observation data expanding rapidly, cloud computing is quickly changing the way Earth observation data is processed, analyzed, and visualized. The cloud infrastructure provides the flexibility to scale up to large volumes of data and handle high velocity data streams efficiently. Having freely available Earth observation data collocated on a cloud infrastructure creates opportunities for innovation and value-added data re-use in ways unforeseen by the original data provider. These innovations spur new industries and applications and spawn new scientific pathways that were previously limited due to data volume and computational infrastructure issues. NASA, in collaboration with Amazon, Google, and Microsoft, have jointly developed a set of recommendations to enable efficient transfer of Earth observation data from existing data systems to a cloud computing infrastructure. The purpose of these recommendations is to provide guidelines against which all data providers can evaluate existing data systems and be used to improve any issues uncovered to enable efficient search, access, and use of large volumes of data. Additionally, these guidelines ensure that all cloud providers utilize a common methodology for bulk-downloading data from data providers thus preventing the data providers from building custom capabilities to meet the needs of individual cloud providers. The intent is to share these recommendations with other Federal agencies and organizations that serve Earth observation to enable efficient search, access, and use of large volumes of data. Additionally, the adoption of these recommendations will benefit data users interested in moving large volumes of data from data systems to any other location. These data users include the cloud providers, cloud users such as scientists, and other users working in a high performance computing environment who need to move large volumes of data.

Enhancing Security by System-Level Virtualization in Cloud Computing Environments

NASA Astrophysics Data System (ADS)

Sun, Dawei; Chang, Guiran; Tan, Chunguang; Wang, Xingwei

Many trends are opening up the era of cloud computing, which will reshape the IT industry. Virtualization techniques have become an indispensable ingredient for almost all cloud computing system. By the virtual environments, cloud provider is able to run varieties of operating systems as needed by each cloud user. Virtualization can improve reliability, security, and availability of applications by using consolidation, isolation, and fault tolerance. In addition, it is possible to balance the workloads by using live migration techniques. In this paper, the definition of cloud computing is given; and then the service and deployment models are introduced. An analysis of security issues and challenges in implementation of cloud computing is identified. Moreover, a system-level virtualization case is established to enhance the security of cloud computing environments.

The Department of Defense and the Power of Cloud Computing: Weighing Acceptable Cost Versus Acceptable Risk

DTIC Science & Technology

2016-04-01

the DOD will put DOD systems and data at a risk level comparable to that of their neighbors in the cloud. Just as a user browses a Web page on the...proxy servers for controlling user access to Web pages, and large-scale storage for data management. Each of these devices allows access to the...user to develop applications. Acunetics.com describes Web applications as “computer programs allowing Website visitors to submit and retrieve data

Cloud based intelligent system for delivering health care as a service.

PubMed

Kaur, Pankaj Deep; Chana, Inderveer

2014-01-01

The promising potential of cloud computing and its convergence with technologies such as mobile computing, wireless networks, sensor technologies allows for creation and delivery of newer type of cloud services. In this paper, we advocate the use of cloud computing for the creation and management of cloud based health care services. As a representative case study, we design a Cloud Based Intelligent Health Care Service (CBIHCS) that performs real time monitoring of user health data for diagnosis of chronic illness such as diabetes. Advance body sensor components are utilized to gather user specific health data and store in cloud based storage repositories for subsequent analysis and classification. In addition, infrastructure level mechanisms are proposed to provide dynamic resource elasticity for CBIHCS. Experimental results demonstrate that classification accuracy of 92.59% is achieved with our prototype system and the predicted patterns of CPU usage offer better opportunities for adaptive resource elasticity. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Earthdata Cloud Analytics Project

NASA Technical Reports Server (NTRS)

Ramachandran, Rahul; Lynnes, Chris

2018-01-01

This presentation describes a nascent project in NASA to develop a framework to support end-user analytics of NASA's Earth science data in the cloud. The chief benefit of migrating EOSDIS (Earth Observation System Data and Information Systems) data to the cloud is to position the data next to enormous computing capacity to allow end users to process data at scale. The Earthdata Cloud Analytics project will user a service-based approach to facilitate the infusion of evolving analytics technology and the integration with non-NASA analytics or other complementary functionality at other agencies and in other nations.

Bigdata Driven Cloud Security: A Survey

NASA Astrophysics Data System (ADS)

Raja, K.; Hanifa, Sabibullah Mohamed

2017-08-01

Cloud Computing (CC) is a fast-growing technology to perform massive-scale and complex computing. It eliminates the need to maintain expensive computing hardware, dedicated space, and software. Recently, it has been observed that massive growth in the scale of data or big data generated through cloud computing. CC consists of a front-end, includes the users’ computers and software required to access the cloud network, and back-end consists of various computers, servers and database systems that create the cloud. In SaaS (Software as-a-Service - end users to utilize outsourced software), PaaS (Platform as-a-Service-platform is provided) and IaaS (Infrastructure as-a-Service-physical environment is outsourced), and DaaS (Database as-a-Service-data can be housed within a cloud), where leading / traditional cloud ecosystem delivers the cloud services become a powerful and popular architecture. Many challenges and issues are in security or threats, most vital barrier for cloud computing environment. The main barrier to the adoption of CC in health care relates to Data security. When placing and transmitting data using public networks, cyber attacks in any form are anticipated in CC. Hence, cloud service users need to understand the risk of data breaches and adoption of service delivery model during deployment. This survey deeply covers the CC security issues (covering Data Security in Health care) so as to researchers can develop the robust security application models using Big Data (BD) on CC (can be created / deployed easily). Since, BD evaluation is driven by fast-growing cloud-based applications developed using virtualized technologies. In this purview, MapReduce [12] is a good example of big data processing in a cloud environment, and a model for Cloud providers.

Cloud-based Web Services for Near-Real-Time Web access to NPP Satellite Imagery and other Data

NASA Astrophysics Data System (ADS)

Evans, J. D.; Valente, E. G.

2010-12-01

We are building a scalable, cloud computing-based infrastructure for Web access to near-real-time data products synthesized from the U.S. National Polar-Orbiting Environmental Satellite System (NPOESS) Preparatory Project (NPP) and other geospatial and meteorological data. Given recent and ongoing changes in the the NPP and NPOESS programs (now Joint Polar Satellite System), the need for timely delivery of NPP data is urgent. We propose an alternative to a traditional, centralized ground segment, using distributed Direct Broadcast facilities linked to industry-standard Web services by a streamlined processing chain running in a scalable cloud computing environment. Our processing chain, currently implemented on Amazon.com's Elastic Compute Cloud (EC2), retrieves raw data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) and synthesizes data products such as Sea-Surface Temperature, Vegetation Indices, etc. The cloud computing approach lets us grow and shrink computing resources to meet large and rapid fluctuations (twice daily) in both end-user demand and data availability from polar-orbiting sensors. Early prototypes have delivered various data products to end-users with latencies between 6 and 32 minutes. We have begun to replicate machine instances in the cloud, so as to reduce latency and maintain near-real time data access regardless of increased data input rates or user demand -- all at quite moderate monthly costs. Our service-based approach (in which users invoke software processes on a Web-accessible server) facilitates access into datasets of arbitrary size and resolution, and allows users to request and receive tailored and composite (e.g., false-color multiband) products on demand. To facilitate broad impact and adoption of our technology, we have emphasized open, industry-standard software interfaces and open source software. Through our work, we envision the widespread establishment of similar, derived, or interoperable systems for processing and serving near-real-time data from NPP and other sensors. A scalable architecture based on cloud computing ensures cost-effective, real-time processing and delivery of NPP and other data. Access via standard Web services maximizes its interoperability and usefulness.

Provider-Independent Use of the Cloud

NASA Astrophysics Data System (ADS)

Harmer, Terence; Wright, Peter; Cunningham, Christina; Perrott, Ron

Utility computing offers researchers and businesses the potential of significant cost-savings, making it possible for them to match the cost of their computing and storage to their demand for such resources. A utility compute provider enables the purchase of compute infrastructures on-demand; when a user requires computing resources a provider will provision a resource for them and charge them only for their period of use of that resource. There has been a significant growth in the number of cloud computing resource providers and each has a different resource usage model, application process and application programming interface (API)-developing generic multi-resource provider applications is thus difficult and time consuming. We have developed an abstraction layer that provides a single resource usage model, user authentication model and API for compute providers that enables cloud-provider neutral applications to be developed. In this paper we outline the issues in using external resource providers, give examples of using a number of the most popular cloud providers and provide examples of developing provider neutral applications. In addition, we discuss the development of the API to create a generic provisioning model based on a common architecture for cloud computing providers.

Cloud Computing: An Overview

NASA Astrophysics Data System (ADS)

Qian, Ling; Luo, Zhiguo; Du, Yujian; Guo, Leitao

In order to support the maximum number of user and elastic service with the minimum resource, the Internet service provider invented the cloud computing. within a few years, emerging cloud computing has became the hottest technology. From the publication of core papers by Google since 2003 to the commercialization of Amazon EC2 in 2006, and to the service offering of AT&T Synaptic Hosting, the cloud computing has been evolved from internal IT system to public service, from cost-saving tools to revenue generator, and from ISP to telecom. This paper introduces the concept, history, pros and cons of cloud computing as well as the value chain and standardization effort.

Where the Cloud Meets the Commons

ERIC Educational Resources Information Center

Ipri, Tom

2011-01-01

Changes presented by cloud computing--shared computing services, applications, and storage available to end users via the Internet--have the potential to seriously alter how libraries provide services, not only remotely, but also within the physical library, specifically concerning challenges facing the typical desktop computing experience.…

Key Technology Research on Open Architecture for The Sharing of Heterogeneous Geographic Analysis Models

NASA Astrophysics Data System (ADS)

Yue, S. S.; Wen, Y. N.; Lv, G. N.; Hu, D.

2013-10-01

In recent years, the increasing development of cloud computing technologies laid critical foundation for efficiently solving complicated geographic issues. However, it is still difficult to realize the cooperative operation of massive heterogeneous geographical models. Traditional cloud architecture is apt to provide centralized solution to end users, while all the required resources are often offered by large enterprises or special agencies. Thus, it's a closed framework from the perspective of resource utilization. Solving comprehensive geographic issues requires integrating multifarious heterogeneous geographical models and data. In this case, an open computing platform is in need, with which the model owners can package and deploy their models into cloud conveniently, while model users can search, access and utilize those models with cloud facility. Based on this concept, the open cloud service strategies for the sharing of heterogeneous geographic analysis models is studied in this article. The key technology: unified cloud interface strategy, sharing platform based on cloud service, and computing platform based on cloud service are discussed in detail, and related experiments are conducted for further verification.

Cloud computing applications for biomedical science: A perspective.

PubMed

Navale, Vivek; Bourne, Philip E

2018-06-01

Biomedical research has become a digital data-intensive endeavor, relying on secure and scalable computing, storage, and network infrastructure, which has traditionally been purchased, supported, and maintained locally. For certain types of biomedical applications, cloud computing has emerged as an alternative to locally maintained traditional computing approaches. Cloud computing offers users pay-as-you-go access to services such as hardware infrastructure, platforms, and software for solving common biomedical computational problems. Cloud computing services offer secure on-demand storage and analysis and are differentiated from traditional high-performance computing by their rapid availability and scalability of services. As such, cloud services are engineered to address big data problems and enhance the likelihood of data and analytics sharing, reproducibility, and reuse. Here, we provide an introductory perspective on cloud computing to help the reader determine its value to their own research.

Cloud computing applications for biomedical science: A perspective

PubMed Central

2018-01-01

Biomedical research has become a digital data–intensive endeavor, relying on secure and scalable computing, storage, and network infrastructure, which has traditionally been purchased, supported, and maintained locally. For certain types of biomedical applications, cloud computing has emerged as an alternative to locally maintained traditional computing approaches. Cloud computing offers users pay-as-you-go access to services such as hardware infrastructure, platforms, and software for solving common biomedical computational problems. Cloud computing services offer secure on-demand storage and analysis and are differentiated from traditional high-performance computing by their rapid availability and scalability of services. As such, cloud services are engineered to address big data problems and enhance the likelihood of data and analytics sharing, reproducibility, and reuse. Here, we provide an introductory perspective on cloud computing to help the reader determine its value to their own research. PMID:29902176

Research on OpenStack of open source cloud computing in colleges and universities’ computer room

NASA Astrophysics Data System (ADS)

Wang, Lei; Zhang, Dandan

2017-06-01

In recent years, the cloud computing technology has a rapid development, especially open source cloud computing. Open source cloud computing has attracted a large number of user groups by the advantages of open source and low cost, have now become a large-scale promotion and application. In this paper, firstly we briefly introduced the main functions and architecture of the open source cloud computing OpenStack tools, and then discussed deeply the core problems of computer labs in colleges and universities. Combining with this research, it is not that the specific application and deployment of university computer rooms with OpenStack tool. The experimental results show that the application of OpenStack tool can efficiently and conveniently deploy cloud of university computer room, and its performance is stable and the functional value is good.

A Cloud-Based Infrastructure for Near-Real-Time Processing and Dissemination of NPP Data

NASA Astrophysics Data System (ADS)

Evans, J. D.; Valente, E. G.; Chettri, S. S.

2011-12-01

We are building a scalable cloud-based infrastructure for generating and disseminating near-real-time data products from a variety of geospatial and meteorological data sources, including the new National Polar-Orbiting Environmental Satellite System (NPOESS) Preparatory Project (NPP). Our approach relies on linking Direct Broadcast and other data streams to a suite of scientific algorithms coordinated by NASA's International Polar-Orbiter Processing Package (IPOPP). The resulting data products are directly accessible to a wide variety of end-user applications, via industry-standard protocols such as OGC Web Services, Unidata Local Data Manager, or OPeNDAP, using open source software components. The processing chain employs on-demand computing resources from Amazon.com's Elastic Compute Cloud and NASA's Nebula cloud services. Our current prototype targets short-term weather forecasting, in collaboration with NASA's Short-term Prediction Research and Transition (SPoRT) program and the National Weather Service. Direct Broadcast is especially crucial for NPP, whose current ground segment is unlikely to deliver data quickly enough for short-term weather forecasters and other near-real-time users. Direct Broadcast also allows full local control over data handling, from the receiving antenna to end-user applications: this provides opportunities to streamline processes for data ingest, processing, and dissemination, and thus to make interpreted data products (Environmental Data Records) available to practitioners within minutes of data capture at the sensor. Cloud computing lets us grow and shrink computing resources to meet large and rapid fluctuations in data availability (twice daily for polar orbiters) - and similarly large fluctuations in demand from our target (near-real-time) users. This offers a compelling business case for cloud computing: the processing or dissemination systems can grow arbitrarily large to sustain near-real time data access despite surges in data volumes or user demand, but that computing capacity (and hourly costs) can be dropped almost instantly once the surge passes. Cloud computing also allows low-risk experimentation with a variety of machine architectures (processor types; bandwidth, memory, and storage capacities, etc.) and of system configurations (including massively parallel computing patterns). Finally, our service-based approach (in which user applications invoke software processes on a Web-accessible server) facilitates access into datasets of arbitrary size and resolution, and allows users to request and receive tailored products on demand. To maximize the usefulness and impact of our technology, we have emphasized open, industry-standard software interfaces. We are also using and developing open source software to facilitate the widespread adoption of similar, derived, or interoperable systems for processing and serving near-real-time data from NPP and other sources.

The Awareness and Challenges of Cloud Computing Adoption on Tertiary Education in Malaysia

NASA Astrophysics Data System (ADS)

Hazreeni Hamzah, Nor; Mahmud, Maziah; Zukri, Shamsunarnie Mohamed; Yaacob, Wan Fairos Wan; Yacob, Jusoh

2017-09-01

This preliminary study aims to investigate the awareness of the adoption of cloud computing among the academicians in tertiary education in Malaysia. Besides, this study also want to explore the possible challenges faced by the academician while adopting this new technology. The pilot study was done on 40 lecturers in Universiti Teknologi MARA Kampus Kota Bharu (UiTMKB) by using self administered questionnaire. The results found that almost half (40 percent) were not aware on the existing of cloud computing in teaching and learning (T&L) process. The challenges confronting the adoption of cloud computing are data insecurity, data insecurity, unsolicited advertisement, lock-in, reluctance to eliminate staff positions, privacy concerns, reliability challenge, regulatory compliance concerns/user control and institutional culture/resistance to change in technology. This possible challenges can be factorized in two major factors which were security and dependency factor and user control and mentality factor.

Now and next-generation sequencing techniques: future of sequence analysis using cloud computing.

PubMed

Thakur, Radhe Shyam; Bandopadhyay, Rajib; Chaudhary, Bratati; Chatterjee, Sourav

2012-01-01

Advances in the field of sequencing techniques have resulted in the greatly accelerated production of huge sequence datasets. This presents immediate challenges in database maintenance at datacenters. It provides additional computational challenges in data mining and sequence analysis. Together these represent a significant overburden on traditional stand-alone computer resources, and to reach effective conclusions quickly and efficiently, the virtualization of the resources and computation on a pay-as-you-go concept (together termed "cloud computing") has recently appeared. The collective resources of the datacenter, including both hardware and software, can be available publicly, being then termed a public cloud, the resources being provided in a virtual mode to the clients who pay according to the resources they employ. Examples of public companies providing these resources include Amazon, Google, and Joyent. The computational workload is shifted to the provider, which also implements required hardware and software upgrades over time. A virtual environment is created in the cloud corresponding to the computational and data storage needs of the user via the internet. The task is then performed, the results transmitted to the user, and the environment finally deleted after all tasks are completed. In this discussion, we focus on the basics of cloud computing, and go on to analyze the prerequisites and overall working of clouds. Finally, the applications of cloud computing in biological systems, particularly in comparative genomics, genome informatics, and SNP detection are discussed with reference to traditional workflows.

Eleven quick tips for architecting biomedical informatics workflows with cloud computing.

PubMed

Cole, Brian S; Moore, Jason H

2018-03-01

Cloud computing has revolutionized the development and operations of hardware and software across diverse technological arenas, yet academic biomedical research has lagged behind despite the numerous and weighty advantages that cloud computing offers. Biomedical researchers who embrace cloud computing can reap rewards in cost reduction, decreased development and maintenance workload, increased reproducibility, ease of sharing data and software, enhanced security, horizontal and vertical scalability, high availability, a thriving technology partner ecosystem, and much more. Despite these advantages that cloud-based workflows offer, the majority of scientific software developed in academia does not utilize cloud computing and must be migrated to the cloud by the user. In this article, we present 11 quick tips for architecting biomedical informatics workflows on compute clouds, distilling knowledge gained from experience developing, operating, maintaining, and distributing software and virtualized appliances on the world's largest cloud. Researchers who follow these tips stand to benefit immediately by migrating their workflows to cloud computing and embracing the paradigm of abstraction.

Eleven quick tips for architecting biomedical informatics workflows with cloud computing

PubMed Central

Moore, Jason H.

2018-01-01

Cloud computing has revolutionized the development and operations of hardware and software across diverse technological arenas, yet academic biomedical research has lagged behind despite the numerous and weighty advantages that cloud computing offers. Biomedical researchers who embrace cloud computing can reap rewards in cost reduction, decreased development and maintenance workload, increased reproducibility, ease of sharing data and software, enhanced security, horizontal and vertical scalability, high availability, a thriving technology partner ecosystem, and much more. Despite these advantages that cloud-based workflows offer, the majority of scientific software developed in academia does not utilize cloud computing and must be migrated to the cloud by the user. In this article, we present 11 quick tips for architecting biomedical informatics workflows on compute clouds, distilling knowledge gained from experience developing, operating, maintaining, and distributing software and virtualized appliances on the world’s largest cloud. Researchers who follow these tips stand to benefit immediately by migrating their workflows to cloud computing and embracing the paradigm of abstraction. PMID:29596416

Cloud Computing with iPlant Atmosphere.

PubMed

McKay, Sheldon J; Skidmore, Edwin J; LaRose, Christopher J; Mercer, Andre W; Noutsos, Christos

2013-10-15

Cloud Computing refers to distributed computing platforms that use virtualization software to provide easy access to physical computing infrastructure and data storage, typically administered through a Web interface. Cloud-based computing provides access to powerful servers, with specific software and virtual hardware configurations, while eliminating the initial capital cost of expensive computers and reducing the ongoing operating costs of system administration, maintenance contracts, power consumption, and cooling. This eliminates a significant barrier to entry into bioinformatics and high-performance computing for many researchers. This is especially true of free or modestly priced cloud computing services. The iPlant Collaborative offers a free cloud computing service, Atmosphere, which allows users to easily create and use instances on virtual servers preconfigured for their analytical needs. Atmosphere is a self-service, on-demand platform for scientific computing. This unit demonstrates how to set up, access and use cloud computing in Atmosphere. Copyright © 2013 John Wiley & Sons, Inc.

Identification of Program Signatures from Cloud Computing System Telemetry Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nichols, Nicole M.; Greaves, Mark T.; Smith, William P.

Malicious cloud computing activity can take many forms, including running unauthorized programs in a virtual environment. Detection of these malicious activities while preserving the privacy of the user is an important research challenge. Prior work has shown the potential viability of using cloud service billing metrics as a mechanism for proxy identification of malicious programs. Previously this novel detection method has been evaluated in a synthetic and isolated computational environment. In this paper we demonstrate the ability of billing metrics to identify programs, in an active cloud computing environment, including multiple virtual machines running on the same hypervisor. The openmore » source cloud computing platform OpenStack, is used for private cloud management at Pacific Northwest National Laboratory. OpenStack provides a billing tool (Ceilometer) to collect system telemetry measurements. We identify four different programs running on four virtual machines under the same cloud user account. Programs were identified with up to 95% accuracy. This accuracy is dependent on the distinctiveness of telemetry measurements for the specific programs we tested. Future work will examine the scalability of this approach for a larger selection of programs to better understand the uniqueness needed to identify a program. Additionally, future work should address the separation of signatures when multiple programs are running on the same virtual machine.« less

Providing Assistive Technology Applications as a Service Through Cloud Computing.

PubMed

Mulfari, Davide; Celesti, Antonio; Villari, Massimo; Puliafito, Antonio

2015-01-01

Users with disabilities interact with Personal Computers (PCs) using Assistive Technology (AT) software solutions. Such applications run on a PC that a person with a disability commonly uses. However the configuration of AT applications is not trivial at all, especially whenever the user needs to work on a PC that does not allow him/her to rely on his / her AT tools (e.g., at work, at university, in an Internet point). In this paper, we discuss how cloud computing provides a valid technological solution to enhance such a scenario.With the emergence of cloud computing, many applications are executed on top of virtual machines (VMs). Virtualization allows us to achieve a software implementation of a real computer able to execute a standard operating system and any kind of application. In this paper we propose to build personalized VMs running AT programs and settings. By using the remote desktop technology, our solution enables users to control their customized virtual desktop environment by means of an HTML5-based web interface running on any computer equipped with a browser, whenever they are.

Quantitative Investigation of the Technologies That Support Cloud Computing

ERIC Educational Resources Information Center

Hu, Wenjin

2014-01-01

Cloud computing is dramatically shaping modern IT infrastructure. It virtualizes computing resources, provides elastic scalability, serves as a pay-as-you-use utility, simplifies the IT administrators' daily tasks, enhances the mobility and collaboration of data, and increases user productivity. We focus on providing generalized black-box…

Biomedical image analysis and processing in clouds

NASA Astrophysics Data System (ADS)

Bednarz, Tomasz; Szul, Piotr; Arzhaeva, Yulia; Wang, Dadong; Burdett, Neil; Khassapov, Alex; Chen, Shiping; Vallotton, Pascal; Lagerstrom, Ryan; Gureyev, Tim; Taylor, John

2013-10-01

Cloud-based Image Analysis and Processing Toolbox project runs on the Australian National eResearch Collaboration Tools and Resources (NeCTAR) cloud infrastructure and allows access to biomedical image processing and analysis services to researchers via remotely accessible user interfaces. By providing user-friendly access to cloud computing resources and new workflow-based interfaces, our solution enables researchers to carry out various challenging image analysis and reconstruction tasks. Several case studies will be presented during the conference.

Now and Next-Generation Sequencing Techniques: Future of Sequence Analysis Using Cloud Computing

PubMed Central

Thakur, Radhe Shyam; Bandopadhyay, Rajib; Chaudhary, Bratati; Chatterjee, Sourav

2012-01-01

Advances in the field of sequencing techniques have resulted in the greatly accelerated production of huge sequence datasets. This presents immediate challenges in database maintenance at datacenters. It provides additional computational challenges in data mining and sequence analysis. Together these represent a significant overburden on traditional stand-alone computer resources, and to reach effective conclusions quickly and efficiently, the virtualization of the resources and computation on a pay-as-you-go concept (together termed “cloud computing”) has recently appeared. The collective resources of the datacenter, including both hardware and software, can be available publicly, being then termed a public cloud, the resources being provided in a virtual mode to the clients who pay according to the resources they employ. Examples of public companies providing these resources include Amazon, Google, and Joyent. The computational workload is shifted to the provider, which also implements required hardware and software upgrades over time. A virtual environment is created in the cloud corresponding to the computational and data storage needs of the user via the internet. The task is then performed, the results transmitted to the user, and the environment finally deleted after all tasks are completed. In this discussion, we focus on the basics of cloud computing, and go on to analyze the prerequisites and overall working of clouds. Finally, the applications of cloud computing in biological systems, particularly in comparative genomics, genome informatics, and SNP detection are discussed with reference to traditional workflows. PMID:23248640

On the Modeling and Management of Cloud Data Analytics

NASA Astrophysics Data System (ADS)

Castillo, Claris; Tantawi, Asser; Steinder, Malgorzata; Pacifici, Giovanni

A new era is dawning where vast amount of data is subjected to intensive analysis in a cloud computing environment. Over the years, data about a myriad of things, ranging from user clicks to galaxies, have been accumulated, and continue to be collected, on storage media. The increasing availability of such data, along with the abundant supply of compute power and the urge to create useful knowledge, gave rise to a new data analytics paradigm in which data is subjected to intensive analysis, and additional data is created in the process. Meanwhile, a new cloud computing environment has emerged where seemingly limitless compute and storage resources are being provided to host computation and data for multiple users through virtualization technologies. Such a cloud environment is becoming the home for data analytics. Consequently, providing good performance at run-time to data analytics workload is an important issue for cloud management. In this paper, we provide an overview of the data analytics and cloud environment landscapes, and investigate the performance management issues related to running data analytics in the cloud. In particular, we focus on topics such as workload characterization, profiling analytics applications and their pattern of data usage, cloud resource allocation, placement of computation and data and their dynamic migration in the cloud, and performance prediction. In solving such management problems one relies on various run-time analytic models. We discuss approaches for modeling and optimizing the dynamic data analytics workload in the cloud environment. All along, we use the Map-Reduce paradigm as an illustration of data analytics.

Tavaxy: Integrating Taverna and Galaxy workflows with cloud computing support

PubMed Central

2012-01-01

Background Over the past decade the workflow system paradigm has evolved as an efficient and user-friendly approach for developing complex bioinformatics applications. Two popular workflow systems that have gained acceptance by the bioinformatics community are Taverna and Galaxy. Each system has a large user-base and supports an ever-growing repository of application workflows. However, workflows developed for one system cannot be imported and executed easily on the other. The lack of interoperability is due to differences in the models of computation, workflow languages, and architectures of both systems. This lack of interoperability limits sharing of workflows between the user communities and leads to duplication of development efforts. Results In this paper, we present Tavaxy, a stand-alone system for creating and executing workflows based on using an extensible set of re-usable workflow patterns. Tavaxy offers a set of new features that simplify and enhance the development of sequence analysis applications: It allows the integration of existing Taverna and Galaxy workflows in a single environment, and supports the use of cloud computing capabilities. The integration of existing Taverna and Galaxy workflows is supported seamlessly at both run-time and design-time levels, based on the concepts of hierarchical workflows and workflow patterns. The use of cloud computing in Tavaxy is flexible, where the users can either instantiate the whole system on the cloud, or delegate the execution of certain sub-workflows to the cloud infrastructure. Conclusions Tavaxy reduces the workflow development cycle by introducing the use of workflow patterns to simplify workflow creation. It enables the re-use and integration of existing (sub-) workflows from Taverna and Galaxy, and allows the creation of hybrid workflows. Its additional features exploit recent advances in high performance cloud computing to cope with the increasing data size and complexity of analysis. The system can be accessed either through a cloud-enabled web-interface or downloaded and installed to run within the user's local environment. All resources related to Tavaxy are available at http://www.tavaxy.org. PMID:22559942

AceCloud: Molecular Dynamics Simulations in the Cloud.

PubMed

Harvey, M J; De Fabritiis, G

2015-05-26

We present AceCloud, an on-demand service for molecular dynamics simulations. AceCloud is designed to facilitate the secure execution of large ensembles of simulations on an external cloud computing service (currently Amazon Web Services). The AceCloud client, integrated into the ACEMD molecular dynamics package, provides an easy-to-use interface that abstracts all aspects of interaction with the cloud services. This gives the user the experience that all simulations are running on their local machine, minimizing the learning curve typically associated with the transition to using high performance computing services.

Automating NEURON Simulation Deployment in Cloud Resources.

PubMed

Stockton, David B; Santamaria, Fidel

2017-01-01

Simulations in neuroscience are performed on local servers or High Performance Computing (HPC) facilities. Recently, cloud computing has emerged as a potential computational platform for neuroscience simulation. In this paper we compare and contrast HPC and cloud resources for scientific computation, then report how we deployed NEURON, a widely used simulator of neuronal activity, in three clouds: Chameleon Cloud, a hybrid private academic cloud for cloud technology research based on the OpenStack software; Rackspace, a public commercial cloud, also based on OpenStack; and Amazon Elastic Cloud Computing, based on Amazon's proprietary software. We describe the manual procedures and how to automate cloud operations. We describe extending our simulation automation software called NeuroManager (Stockton and Santamaria, Frontiers in Neuroinformatics, 2015), so that the user is capable of recruiting private cloud, public cloud, HPC, and local servers simultaneously with a simple common interface. We conclude by performing several studies in which we examine speedup, efficiency, total session time, and cost for sets of simulations of a published NEURON model.

Automating NEURON Simulation Deployment in Cloud Resources

PubMed Central

Santamaria, Fidel

2016-01-01

Simulations in neuroscience are performed on local servers or High Performance Computing (HPC) facilities. Recently, cloud computing has emerged as a potential computational platform for neuroscience simulation. In this paper we compare and contrast HPC and cloud resources for scientific computation, then report how we deployed NEURON, a widely used simulator of neuronal activity, in three clouds: Chameleon Cloud, a hybrid private academic cloud for cloud technology research based on the Open-Stack software; Rackspace, a public commercial cloud, also based on OpenStack; and Amazon Elastic Cloud Computing, based on Amazon’s proprietary software. We describe the manual procedures and how to automate cloud operations. We describe extending our simulation automation software called NeuroManager (Stockton and Santamaria, Frontiers in Neuroinformatics, 2015), so that the user is capable of recruiting private cloud, public cloud, HPC, and local servers simultaneously with a simple common interface. We conclude by performing several studies in which we examine speedup, efficiency, total session time, and cost for sets of simulations of a published NEURON model. PMID:27655341

Cloud Computing: A Free Technology Option to Promote Collaborative Learning

ERIC Educational Resources Information Center

Siegle, Del

2010-01-01

In a time of budget cuts and limited funding, purchasing and installing the latest software on classroom computers can be prohibitive for schools. Many educators are unaware that a variety of free software options exist, and some of them do not actually require installing software on the user's computer. One such option is cloud computing. This…

Interfacing HTCondor-CE with OpenStack

NASA Astrophysics Data System (ADS)

Bockelman, B.; Caballero Bejar, J.; Hover, J.

2017-10-01

Over the past few years, Grid Computing technologies have reached a high level of maturity. One key aspect of this success has been the development and adoption of newer Compute Elements to interface the external Grid users with local batch systems. These new Compute Elements allow for better handling of jobs requirements and a more precise management of diverse local resources. However, despite this level of maturity, the Grid Computing world is lacking diversity in local execution platforms. As Grid Computing technologies have historically been driven by the needs of the High Energy Physics community, most resource providers run the platform (operating system version and architecture) that best suits the needs of their particular users. In parallel, the development of virtualization and cloud technologies has accelerated recently, making available a variety of solutions, both commercial and academic, proprietary and open source. Virtualization facilitates performing computational tasks on platforms not available at most computing sites. This work attempts to join the technologies, allowing users to interact with computing sites through one of the standard Computing Elements, HTCondor-CE, but running their jobs within VMs on a local cloud platform, OpenStack, when needed. The system will re-route, in a transparent way, end user jobs into dynamically-launched VM worker nodes when they have requirements that cannot be satisfied by the static local batch system nodes. Also, once the automated mechanisms are in place, it becomes straightforward to allow an end user to invoke a custom Virtual Machine at the site. This will allow cloud resources to be used without requiring the user to establish a separate account. Both scenarios are described in this work.

Cloud Computing - A Unified Approach for Surveillance Issues

NASA Astrophysics Data System (ADS)

Rachana, C. R.; Banu, Reshma, Dr.; Ahammed, G. F. Ali, Dr.; Parameshachari, B. D., Dr.

2017-08-01

Cloud computing describes highly scalable resources provided as an external service via the Internet on a basis of pay-per-use. From the economic point of view, the main attractiveness of cloud computing is that users only use what they need, and only pay for what they actually use. Resources are available for access from the cloud at any time, and from any location through networks. Cloud computing is gradually replacing the traditional Information Technology Infrastructure. Securing data is one of the leading concerns and biggest issue for cloud computing. Privacy of information is always a crucial pointespecially when an individual’s personalinformation or sensitive information is beingstored in the organization. It is indeed true that today; cloud authorization systems are notrobust enough. This paper presents a unified approach for analyzing the various security issues and techniques to overcome the challenges in the cloud environment.

Elastic Cloud Computing Infrastructures in the Open Cirrus Testbed Implemented via Eucalyptus

NASA Astrophysics Data System (ADS)

Baun, Christian; Kunze, Marcel

Cloud computing realizes the advantages and overcomes some restrictionsof the grid computing paradigm. Elastic infrastructures can easily be createdand managed by cloud users. In order to accelerate the research ondata center management and cloud services the OpenCirrusTM researchtestbed has been started by HP, Intel and Yahoo!. Although commercialcloud offerings are proprietary, Open Source solutions exist in the field ofIaaS with Eucalyptus, PaaS with AppScale and at the applications layerwith Hadoop MapReduce. This paper examines the I/O performance ofcloud computing infrastructures implemented with Eucalyptus in contrastto Amazon S3.

Integration of hybrid wireless networks in cloud services oriented enterprise information systems

NASA Astrophysics Data System (ADS)

Li, Shancang; Xu, Lida; Wang, Xinheng; Wang, Jue

2012-05-01

This article presents a hybrid wireless network integration scheme in cloud services-based enterprise information systems (EISs). With the emerging hybrid wireless networks and cloud computing technologies, it is necessary to develop a scheme that can seamlessly integrate these new technologies into existing EISs. By combining the hybrid wireless networks and computing in EIS, a new framework is proposed, which includes frontend layer, middle layer and backend layers connected to IP EISs. Based on a collaborative architecture, cloud services management framework and process diagram are presented. As a key feature, the proposed approach integrates access control functionalities within the hybrid framework that provide users with filtered views on available cloud services based on cloud service access requirements and user security credentials. In future work, we will implement the proposed framework over SwanMesh platform by integrating the UPnP standard into an enterprise information system.

User's manual for the REEDM (Rocket Exhaust Effluent Diffusion Model) computer program

NASA Technical Reports Server (NTRS)

Bjorklund, J. R.; Dumbauld, R. K.; Cheney, C. S.; Geary, H. V.

1982-01-01

The REEDM computer program predicts concentrations, dosages, and depositions downwind from normal and abnormal launches of rocket vehicles at NASA's Kennedy Space Center. The atmospheric dispersion models, cloud-rise models, and other formulas used in the REEDM model are described mathematically Vehicle and source parameters, other pertinent physical properties of the rocket exhaust cloud, and meteorological layering techniques are presented as well as user's instructions for REEDM. Worked example problems are included.

Security on Cloud Revocation Authority using Identity Based Encryption

NASA Astrophysics Data System (ADS)

Rajaprabha, M. N.

2017-11-01

As due to the era of cloud computing most of the people are saving there documents, files and other things on cloud spaces. Due to this security over the cloud is also important because all the confidential things are there on the cloud. So to overcome private key infrastructure (PKI) issues some revocable Identity Based Encryption (IBE) techniques are introduced which eliminates the demand of PKI. The technique introduced is key update cloud service provider which is having two issues in it and they are computation and communication cost is high and second one is scalability issue. So to overcome this problem we come along with the system in which the Cloud Revocation Authority (CRA) is there for the security which will only hold the secret key for each user. And the secret key was send with the help of advanced encryption standard security. The key is encrypted and send to the CRA for giving the authentication to the person who wants to share the data or files or for the communication purpose. Through that key only the other user will able to access that file and if the user apply some invalid key on the particular file than the information of that user and file is send to the administrator and administrator is having rights to block that person of black list that person to use the system services.

Virtual pools for interactive analysis and software development through an integrated Cloud environment

NASA Astrophysics Data System (ADS)

Grandi, C.; Italiano, A.; Salomoni, D.; Calabrese Melcarne, A. K.

2011-12-01

WNoDeS, an acronym for Worker Nodes on Demand Service, is software developed at CNAF-Tier1, the National Computing Centre of the Italian Institute for Nuclear Physics (INFN) located in Bologna. WNoDeS provides on demand, integrated access to both Grid and Cloud resources through virtualization technologies. Besides the traditional use of computing resources in batch mode, users need to have interactive and local access to a number of systems. WNoDeS can dynamically select these computers instantiating Virtual Machines, according to the requirements (computing, storage and network resources) of users through either the Open Cloud Computing Interface API, or through a web console. An interactive use is usually limited to activities in user space, i.e. where the machine configuration is not modified. In some other instances the activity concerns development and testing of services and thus implies the modification of the system configuration (and, therefore, root-access to the resource). The former use case is a simple extension of the WNoDeS approach, where the resource is provided in interactive mode. The latter implies saving the virtual image at the end of each user session so that it can be presented to the user at subsequent requests. This work describes how the LHC experiments at INFN-Bologna are testing and making use of these dynamically created ad-hoc machines via WNoDeS to support flexible, interactive analysis and software development at the INFN Tier-1 Computing Centre.

The Role of Networks in Cloud Computing

NASA Astrophysics Data System (ADS)

Lin, Geng; Devine, Mac

The confluence of technology advancements and business developments in Broadband Internet, Web services, computing systems, and application software over the past decade has created a perfect storm for cloud computing. The "cloud model" of delivering and consuming IT functions as services is poised to fundamentally transform the IT industry and rebalance the inter-relationships among end users, enterprise IT, software companies, and the service providers in the IT ecosystem (Armbrust et al., 2009; Lin, Fu, Zhu, & Dasmalchi, 2009).

Low cost, high performance processing of single particle cryo-electron microscopy data in the cloud.

PubMed

Cianfrocco, Michael A; Leschziner, Andres E

2015-05-08

The advent of a new generation of electron microscopes and direct electron detectors has realized the potential of single particle cryo-electron microscopy (cryo-EM) as a technique to generate high-resolution structures. Calculating these structures requires high performance computing clusters, a resource that may be limiting to many likely cryo-EM users. To address this limitation and facilitate the spread of cryo-EM, we developed a publicly available 'off-the-shelf' computing environment on Amazon's elastic cloud computing infrastructure. This environment provides users with single particle cryo-EM software packages and the ability to create computing clusters with 16-480+ CPUs. We tested our computing environment using a publicly available 80S yeast ribosome dataset and estimate that laboratories could determine high-resolution cryo-EM structures for $50 to $1500 per structure within a timeframe comparable to local clusters. Our analysis shows that Amazon's cloud computing environment may offer a viable computing environment for cryo-EM.

A Location-Based Interactive Model of Internet of Things and Cloud (IoT-Cloud) for Mobile Cloud Computing Applications.

PubMed

Dinh, Thanh; Kim, Younghan; Lee, Hyukjoon

2017-03-01

This paper presents a location-based interactive model of Internet of Things (IoT) and cloud integration (IoT-cloud) for mobile cloud computing applications, in comparison with the periodic sensing model. In the latter, sensing collections are performed without awareness of sensing demands. Sensors are required to report their sensing data periodically regardless of whether or not there are demands for their sensing services. This leads to unnecessary energy loss due to redundant transmission. In the proposed model, IoT-cloud provides sensing services on demand based on interest and location of mobile users. By taking advantages of the cloud as a coordinator, sensing scheduling of sensors is controlled by the cloud, which knows when and where mobile users request for sensing services. Therefore, when there is no demand, sensors are put into an inactive mode to save energy. Through extensive analysis and experimental results, we show that the location-based model achieves a significant improvement in terms of network lifetime compared to the periodic model.

A Location-Based Interactive Model of Internet of Things and Cloud (IoT-Cloud) for Mobile Cloud Computing Applications †

PubMed Central

Dinh, Thanh; Kim, Younghan; Lee, Hyukjoon

2017-01-01

This paper presents a location-based interactive model of Internet of Things (IoT) and cloud integration (IoT-cloud) for mobile cloud computing applications, in comparison with the periodic sensing model. In the latter, sensing collections are performed without awareness of sensing demands. Sensors are required to report their sensing data periodically regardless of whether or not there are demands for their sensing services. This leads to unnecessary energy loss due to redundant transmission. In the proposed model, IoT-cloud provides sensing services on demand based on interest and location of mobile users. By taking advantages of the cloud as a coordinator, sensing scheduling of sensors is controlled by the cloud, which knows when and where mobile users request for sensing services. Therefore, when there is no demand, sensors are put into an inactive mode to save energy. Through extensive analysis and experimental results, we show that the location-based model achieves a significant improvement in terms of network lifetime compared to the periodic model. PMID:28257067

The Cloud Area Padovana: from pilot to production

NASA Astrophysics Data System (ADS)

Andreetto, P.; Costa, F.; Crescente, A.; Dorigo, A.; Fantinel, S.; Fanzago, F.; Sgaravatto, M.; Traldi, S.; Verlato, M.; Zangrando, L.

2017-10-01

The Cloud Area Padovana has been running for almost two years. This is an OpenStack-based scientific cloud, spread across two different sites: the INFN Padova Unit and the INFN Legnaro National Labs. The hardware resources have been scaled horizontally and vertically, by upgrading some hypervisors and by adding new ones: currently it provides about 1100 cores. Some in-house developments were also integrated in the OpenStack dashboard, such as a tool for user and project registrations with direct support for the INFN-AAI Identity Provider as a new option for the user authentication. In collaboration with the EU-funded Indigo DataCloud project, the integration with Docker-based containers has been experimented with and will be available in production soon. This computing facility now satisfies the computational and storage demands of more than 70 users affiliated with about 20 research projects. We present here the architecture of this Cloud infrastructure, the tools and procedures used to operate it. We also focus on the lessons learnt in these two years, describing the problems that were found and the corrective actions that had to be applied. We also discuss about the chosen strategy for upgrades, which combines the need to promptly integrate the OpenStack new developments, the demand to reduce the downtimes of the infrastructure, and the need to limit the effort requested for such updates. We also discuss how this Cloud infrastructure is being used. In particular we focus on two big physics experiments which are intensively exploiting this computing facility: CMS and SPES. CMS deployed on the cloud a complex computational infrastructure, composed of several user interfaces for job submission in the Grid environment/local batch queues or for interactive processes; this is fully integrated with the local Tier-2 facility. To avoid a static allocation of the resources, an elastic cluster, based on cernVM, has been configured: it allows to automatically create and delete virtual machines according to the user needs. SPES, using a client-server system called TraceWin, exploits INFN’s virtual resources performing a very large number of simulations on about a thousand nodes elastically managed.

Secure Dynamic access control scheme of PHR in cloud computing.

PubMed

Chen, Tzer-Shyong; Liu, Chia-Hui; Chen, Tzer-Long; Chen, Chin-Sheng; Bau, Jian-Guo; Lin, Tzu-Ching

2012-12-01

With the development of information technology and medical technology, medical information has been developed from traditional paper records into electronic medical records, which have now been widely applied. The new-style medical information exchange system "personal health records (PHR)" is gradually developed. PHR is a kind of health records maintained and recorded by individuals. An ideal personal health record could integrate personal medical information from different sources and provide complete and correct personal health and medical summary through the Internet or portable media under the requirements of security and privacy. A lot of personal health records are being utilized. The patient-centered PHR information exchange system allows the public autonomously maintain and manage personal health records. Such management is convenient for storing, accessing, and sharing personal medical records. With the emergence of Cloud computing, PHR service has been transferred to storing data into Cloud servers that the resources could be flexibly utilized and the operation cost can be reduced. Nevertheless, patients would face privacy problem when storing PHR data into Cloud. Besides, it requires a secure protection scheme to encrypt the medical records of each patient for storing PHR into Cloud server. In the encryption process, it would be a challenge to achieve accurately accessing to medical records and corresponding to flexibility and efficiency. A new PHR access control scheme under Cloud computing environments is proposed in this study. With Lagrange interpolation polynomial to establish a secure and effective PHR information access scheme, it allows to accurately access to PHR with security and is suitable for enormous multi-users. Moreover, this scheme also dynamically supports multi-users in Cloud computing environments with personal privacy and offers legal authorities to access to PHR. From security and effectiveness analyses, the proposed PHR access scheme in Cloud computing environments is proven flexible and secure and could effectively correspond to real-time appending and deleting user access authorization and appending and revising PHR records.

The JASMIN Cloud: specialised and hybrid to meet the needs of the Environmental Sciences Community

NASA Astrophysics Data System (ADS)

Kershaw, Philip; Lawrence, Bryan; Churchill, Jonathan; Pritchard, Matt

2014-05-01

Cloud computing provides enormous opportunities for the research community. The large public cloud providers provide near-limitless scaling capability. However, adapting Cloud to scientific workloads is not without its problems. The commodity nature of the public cloud infrastructure can be at odds with the specialist requirements of the research community. Issues such as trust, ownership of data, WAN bandwidth and costing models make additional barriers to more widespread adoption. Alongside the application of public cloud for scientific applications, a number of private cloud initiatives are underway in the research community of which the JASMIN Cloud is one example. Here, cloud service models are being effectively super-imposed over more established services such as data centres, compute cluster facilities and Grids. These have the potential to deliver the specialist infrastructure needed for the science community coupled with the benefits of a Cloud service model. The JASMIN facility based at the Rutherford Appleton Laboratory was established in 2012 to support the data analysis requirements of the climate and Earth Observation community. In its first year of operation, the 5PB of available storage capacity was filled and the hosted compute capability used extensively. JASMIN has modelled the concept of a centralised large-volume data analysis facility. Key characteristics have enabled success: peta-scale fast disk connected via low latency networks to compute resources and the use of virtualisation for effective management of the resources for a range of users. A second phase is now underway funded through NERC's (Natural Environment Research Council) Big Data initiative. This will see significant expansion to the resources available with a doubling of disk-based storage to 12PB and an increase of compute capacity by a factor of ten to over 3000 processing cores. This expansion is accompanied by a broadening in the scope for JASMIN, as a service available to the entire UK environmental science community. Experience with the first phase demonstrated the range of user needs. A trade-off is needed between access privileges to resources, flexibility of use and security. This has influenced the form and types of service under development for the new phase. JASMIN will deploy a specialised private cloud organised into "Managed" and "Unmanaged" components. In the Managed Cloud, users have direct access to the storage and compute resources for optimal performance but for reasons of security, via a more restrictive PaaS (Platform-as-a-Service) interface. The Unmanaged Cloud is deployed in an isolated part of the network but co-located with the rest of the infrastructure. This enables greater liberty to tenants - full IaaS (Infrastructure-as-a-Service) capability to provision customised infrastructure - whilst at the same time protecting more sensitive parts of the system from direct access using these elevated privileges. The private cloud will be augmented with cloud-bursting capability so that it can exploit the resources available from public clouds, making it effectively a hybrid solution. A single interface will overlay the functionality of both the private cloud and external interfaces to public cloud providers giving users the flexibility to migrate resources between infrastructures as requirements dictate.

Real-time video streaming in mobile cloud over heterogeneous wireless networks

NASA Astrophysics Data System (ADS)

Abdallah-Saleh, Saleh; Wang, Qi; Grecos, Christos

2012-06-01

Recently, the concept of Mobile Cloud Computing (MCC) has been proposed to offload the resource requirements in computational capabilities, storage and security from mobile devices into the cloud. Internet video applications such as real-time streaming are expected to be ubiquitously deployed and supported over the cloud for mobile users, who typically encounter a range of wireless networks of diverse radio access technologies during their roaming. However, real-time video streaming for mobile cloud users across heterogeneous wireless networks presents multiple challenges. The network-layer quality of service (QoS) provision to support high-quality mobile video delivery in this demanding scenario remains an open research question, and this in turn affects the application-level visual quality and impedes mobile users' perceived quality of experience (QoE). In this paper, we devise a framework to support real-time video streaming in this new mobile video networking paradigm and evaluate the performance of the proposed framework empirically through a lab-based yet realistic testing platform. One particular issue we focus on is the effect of users' mobility on the QoS of video streaming over the cloud. We design and implement a hybrid platform comprising of a test-bed and an emulator, on which our concept of mobile cloud computing, video streaming and heterogeneous wireless networks are implemented and integrated to allow the testing of our framework. As representative heterogeneous wireless networks, the popular WLAN (Wi-Fi) and MAN (WiMAX) networks are incorporated in order to evaluate effects of handovers between these different radio access technologies. The H.264/AVC (Advanced Video Coding) standard is employed for real-time video streaming from a server to mobile users (client nodes) in the networks. Mobility support is introduced to enable continuous streaming experience for a mobile user across the heterogeneous wireless network. Real-time video stream packets are captured for analytical purposes on the mobile user node. Experimental results are obtained and analysed. Future work is identified towards further improvement of the current design and implementation. With this new mobile video networking concept and paradigm implemented and evaluated, results and observations obtained from this study would form the basis of a more in-depth, comprehensive understanding of various challenges and opportunities in supporting high-quality real-time video streaming in mobile cloud over heterogeneous wireless networks.

Modelling operations and security of cloud systems using Z-notation and Chinese Wall security policy

NASA Astrophysics Data System (ADS)

Basu, Srijita; Sengupta, Anirban; Mazumdar, Chandan

2016-11-01

Enterprises are increasingly using cloud computing for hosting their applications. Availability of fast Internet and cheap bandwidth are causing greater number of people to use cloud-based services. This has the advantage of lower cost and minimum maintenance. However, ensuring security of user data and proper management of cloud infrastructure remain major areas of concern. Existing techniques are either too complex, or fail to properly represent the actual cloud scenario. This article presents a formal cloud model using the constructs of Z-notation. Principles of the Chinese Wall security policy have been applied to design secure cloud-specific operations. The proposed methodology will enable users to safely host their services, as well as process sensitive data, on cloud.

A high performance scientific cloud computing environment for materials simulations

NASA Astrophysics Data System (ADS)

Jorissen, K.; Vila, F. D.; Rehr, J. J.

2012-09-01

We describe the development of a scientific cloud computing (SCC) platform that offers high performance computation capability. The platform consists of a scientific virtual machine prototype containing a UNIX operating system and several materials science codes, together with essential interface tools (an SCC toolset) that offers functionality comparable to local compute clusters. In particular, our SCC toolset provides automatic creation of virtual clusters for parallel computing, including tools for execution and monitoring performance, as well as efficient I/O utilities that enable seamless connections to and from the cloud. Our SCC platform is optimized for the Amazon Elastic Compute Cloud (EC2). We present benchmarks for prototypical scientific applications and demonstrate performance comparable to local compute clusters. To facilitate code execution and provide user-friendly access, we have also integrated cloud computing capability in a JAVA-based GUI. Our SCC platform may be an alternative to traditional HPC resources for materials science or quantum chemistry applications.

Advanced cloud fault tolerance system

NASA Astrophysics Data System (ADS)

Sumangali, K.; Benny, Niketa

2017-11-01

Cloud computing has become a prevalent on-demand service on the internet to store, manage and process data. A pitfall that accompanies cloud computing is the failures that can be encountered in the cloud. To overcome these failures, we require a fault tolerance mechanism to abstract faults from users. We have proposed a fault tolerant architecture, which is a combination of proactive and reactive fault tolerance. This architecture essentially increases the reliability and the availability of the cloud. In the future, we would like to compare evaluations of our proposed architecture with existing architectures and further improve it.

Performance Management of High Performance Computing for Medical Image Processing in Amazon Web Services.

PubMed

Bao, Shunxing; Damon, Stephen M; Landman, Bennett A; Gokhale, Aniruddha

2016-02-27

Adopting high performance cloud computing for medical image processing is a popular trend given the pressing needs of large studies. Amazon Web Services (AWS) provide reliable, on-demand, and inexpensive cloud computing services. Our research objective is to implement an affordable, scalable and easy-to-use AWS framework for the Java Image Science Toolkit (JIST). JIST is a plugin for Medical-Image Processing, Analysis, and Visualization (MIPAV) that provides a graphical pipeline implementation allowing users to quickly test and develop pipelines. JIST is DRMAA-compliant allowing it to run on portable batch system grids. However, as new processing methods are implemented and developed, memory may often be a bottleneck for not only lab computers, but also possibly some local grids. Integrating JIST with the AWS cloud alleviates these possible restrictions and does not require users to have deep knowledge of programming in Java. Workflow definition/management and cloud configurations are two key challenges in this research. Using a simple unified control panel, users have the ability to set the numbers of nodes and select from a variety of pre-configured AWS EC2 nodes with different numbers of processors and memory storage. Intuitively, we configured Amazon S3 storage to be mounted by pay-for-use Amazon EC2 instances. Hence, S3 storage is recognized as a shared cloud resource. The Amazon EC2 instances provide pre-installs of all necessary packages to run JIST. This work presents an implementation that facilitates the integration of JIST with AWS. We describe the theoretical cost/benefit formulae to decide between local serial execution versus cloud computing and apply this analysis to an empirical diffusion tensor imaging pipeline.

Performance management of high performance computing for medical image processing in Amazon Web Services

NASA Astrophysics Data System (ADS)

Bao, Shunxing; Damon, Stephen M.; Landman, Bennett A.; Gokhale, Aniruddha

2016-03-01

Adopting high performance cloud computing for medical image processing is a popular trend given the pressing needs of large studies. Amazon Web Services (AWS) provide reliable, on-demand, and inexpensive cloud computing services. Our research objective is to implement an affordable, scalable and easy-to-use AWS framework for the Java Image Science Toolkit (JIST). JIST is a plugin for Medical- Image Processing, Analysis, and Visualization (MIPAV) that provides a graphical pipeline implementation allowing users to quickly test and develop pipelines. JIST is DRMAA-compliant allowing it to run on portable batch system grids. However, as new processing methods are implemented and developed, memory may often be a bottleneck for not only lab computers, but also possibly some local grids. Integrating JIST with the AWS cloud alleviates these possible restrictions and does not require users to have deep knowledge of programming in Java. Workflow definition/management and cloud configurations are two key challenges in this research. Using a simple unified control panel, users have the ability to set the numbers of nodes and select from a variety of pre-configured AWS EC2 nodes with different numbers of processors and memory storage. Intuitively, we configured Amazon S3 storage to be mounted by pay-for- use Amazon EC2 instances. Hence, S3 storage is recognized as a shared cloud resource. The Amazon EC2 instances provide pre-installs of all necessary packages to run JIST. This work presents an implementation that facilitates the integration of JIST with AWS. We describe the theoretical cost/benefit formulae to decide between local serial execution versus cloud computing and apply this analysis to an empirical diffusion tensor imaging pipeline.

Performance Management of High Performance Computing for Medical Image Processing in Amazon Web Services

PubMed Central

Bao, Shunxing; Damon, Stephen M.; Landman, Bennett A.; Gokhale, Aniruddha

2016-01-01

Adopting high performance cloud computing for medical image processing is a popular trend given the pressing needs of large studies. Amazon Web Services (AWS) provide reliable, on-demand, and inexpensive cloud computing services. Our research objective is to implement an affordable, scalable and easy-to-use AWS framework for the Java Image Science Toolkit (JIST). JIST is a plugin for Medical-Image Processing, Analysis, and Visualization (MIPAV) that provides a graphical pipeline implementation allowing users to quickly test and develop pipelines. JIST is DRMAA-compliant allowing it to run on portable batch system grids. However, as new processing methods are implemented and developed, memory may often be a bottleneck for not only lab computers, but also possibly some local grids. Integrating JIST with the AWS cloud alleviates these possible restrictions and does not require users to have deep knowledge of programming in Java. Workflow definition/management and cloud configurations are two key challenges in this research. Using a simple unified control panel, users have the ability to set the numbers of nodes and select from a variety of pre-configured AWS EC2 nodes with different numbers of processors and memory storage. Intuitively, we configured Amazon S3 storage to be mounted by pay-for-use Amazon EC2 instances. Hence, S3 storage is recognized as a shared cloud resource. The Amazon EC2 instances provide pre-installs of all necessary packages to run JIST. This work presents an implementation that facilitates the integration of JIST with AWS. We describe the theoretical cost/benefit formulae to decide between local serial execution versus cloud computing and apply this analysis to an empirical diffusion tensor imaging pipeline. PMID:27127335

Research on multi-user encrypted search scheme in cloud environment

NASA Astrophysics Data System (ADS)

Yu, Zonghua; Lin, Sui

2017-05-01

Aiming at the existing problems of multi-user encrypted search scheme in cloud computing environment, a basic multi-user encrypted scheme is proposed firstly, and then the basic scheme is extended to an anonymous hierarchical management authority. Compared with most of the existing schemes, the scheme not only to achieve the protection of keyword information, but also to achieve the protection of user identity privacy; the same time, data owners can directly control the user query permissions, rather than the cloud server. In addition, through the use of a special query key generation rules, to achieve the hierarchical management of the user's query permissions. The safety analysis shows that the scheme is safe and that the performance analysis and experimental data show that the scheme is practicable.

The Integration of CloudStack and OCCI/OpenNebula with DIRAC

NASA Astrophysics Data System (ADS)

Méndez Muñoz, Víctor; Fernández Albor, Víctor; Graciani Diaz, Ricardo; Casajús Ramo, Adriàn; Fernández Pena, Tomás; Merino Arévalo, Gonzalo; José Saborido Silva, Juan

2012-12-01

The increasing availability of Cloud resources is arising as a realistic alternative to the Grid as a paradigm for enabling scientific communities to access large distributed computing resources. The DIRAC framework for distributed computing is an easy way to efficiently access to resources from both systems. This paper explains the integration of DIRAC with two open-source Cloud Managers: OpenNebula (taking advantage of the OCCI standard) and CloudStack. These are computing tools to manage the complexity and heterogeneity of distributed data center infrastructures, allowing to create virtual clusters on demand, including public, private and hybrid clouds. This approach has required to develop an extension to the previous DIRAC Virtual Machine engine, which was developed for Amazon EC2, allowing the connection with these new cloud managers. In the OpenNebula case, the development has been based on the CernVM Virtual Software Appliance with appropriate contextualization, while in the case of CloudStack, the infrastructure has been kept more general, which permits other Virtual Machine sources and operating systems being used. In both cases, CernVM File System has been used to facilitate software distribution to the computing nodes. With the resulting infrastructure, the cloud resources are transparent to the users through a friendly interface, like the DIRAC Web Portal. The main purpose of this integration is to get a system that can manage cloud and grid resources at the same time. This particular feature pushes DIRAC to a new conceptual denomination as interware, integrating different middleware. Users from different communities do not need to care about the installation of the standard software that is available at the nodes, nor the operating system of the host machine which is transparent to the user. This paper presents an analysis of the overhead of the virtual layer, doing some tests to compare the proposed approach with the existing Grid solution. License Notice: Published under licence in Journal of Physics: Conference Series by IOP Publishing Ltd.

Smart Learning Services Based on Smart Cloud Computing

PubMed Central

Kim, Svetlana; Song, Su-Mi; Yoon, Yong-Ik

2011-01-01

Context-aware technologies can make e-learning services smarter and more efficient since context-aware services are based on the user’s behavior. To add those technologies into existing e-learning services, a service architecture model is needed to transform the existing e-learning environment, which is situation-aware, into the environment that understands context as well. The context-awareness in e-learning may include the awareness of user profile and terminal context. In this paper, we propose a new notion of service that provides context-awareness to smart learning content in a cloud computing environment. We suggest the elastic four smarts (E4S)—smart pull, smart prospect, smart content, and smart push—concept to the cloud services so smart learning services are possible. The E4S focuses on meeting the users’ needs by collecting and analyzing users’ behavior, prospecting future services, building corresponding contents, and delivering the contents through cloud computing environment. Users’ behavior can be collected through mobile devices such as smart phones that have built-in sensors. As results, the proposed smart e-learning model in cloud computing environment provides personalized and customized learning services to its users. PMID:22164048

Menu-driven cloud computing and resource sharing for R and Bioconductor.

PubMed

Bolouri, Hamid; Dulepet, Rajiv; Angerman, Michael

2011-08-15

We report CRdata.org, a cloud-based, free, open-source web server for running analyses and sharing data and R scripts with others. In addition to using the free, public service, CRdata users can launch their own private Amazon Elastic Computing Cloud (EC2) nodes and store private data and scripts on Amazon's Simple Storage Service (S3) with user-controlled access rights. All CRdata services are provided via point-and-click menus. CRdata is open-source and free under the permissive MIT License (opensource.org/licenses/mit-license.php). The source code is in Ruby (ruby-lang.org/en/) and available at: github.com/seerdata/crdata. hbolouri@fhcrc.org.

High-performance scientific computing in the cloud

NASA Astrophysics Data System (ADS)

Jorissen, Kevin; Vila, Fernando; Rehr, John

2011-03-01

Cloud computing has the potential to open up high-performance computational science to a much broader class of researchers, owing to its ability to provide on-demand, virtualized computational resources. However, before such approaches can become commonplace, user-friendly tools must be developed that hide the unfamiliar cloud environment and streamline the management of cloud resources for many scientific applications. We have recently shown that high-performance cloud computing is feasible for parallelized x-ray spectroscopy calculations. We now present benchmark results for a wider selection of scientific applications focusing on electronic structure and spectroscopic simulation software in condensed matter physics. These applications are driven by an improved portable interface that can manage virtual clusters and run various applications in the cloud. We also describe a next generation of cluster tools, aimed at improved performance and a more robust cluster deployment. Supported by NSF grant OCI-1048052.

Legal issues in clouds: towards a risk inventory.

PubMed

Djemame, Karim; Barnitzke, Benno; Corrales, Marcelo; Kiran, Mariam; Jiang, Ming; Armstrong, Django; Forgó, Nikolaus; Nwankwo, Iheanyi

2013-01-28

Cloud computing technologies have reached a high level of development, yet a number of obstacles still exist that must be overcome before widespread commercial adoption can become a reality. In a cloud environment, end users requesting services and cloud providers negotiate service-level agreements (SLAs) that provide explicit statements of all expectations and obligations of the participants. If cloud computing is to experience widespread commercial adoption, then incorporating risk assessment techniques is essential during SLA negotiation and service operation. This article focuses on the legal issues surrounding risk assessment in cloud computing. Specifically, it analyses risk regarding data protection and security, and presents the requirements of an inherent risk inventory. The usefulness of such a risk inventory is described in the context of the OPTIMIS project.

Use of cloud computing technology in natural hazard assessment and emergency management

NASA Astrophysics Data System (ADS)

Webley, P. W.; Dehn, J.

2015-12-01

During a natural hazard event, the most up-to-date data needs to be in the hands of those on the front line. Decision support system tools can be developed to provide access to pre-made outputs to quickly assess the hazard and potential risk. However, with the ever growing availability of new satellite data as well as ground and airborne data generated in real-time there is a need to analyze the large volumes of data in an easy-to-access and effective environment. With the growth in the use of cloud computing, where the analysis and visualization system can grow with the needs of the user, then these facilities can used to provide this real-time analysis. Think of a central command center uploading the data to the cloud compute system and then those researchers in-the-field connecting to a web-based tool to view the newly acquired data. New data can be added by any user and then viewed instantly by anyone else in the organization through the cloud computing interface. This provides the ideal tool for collaborative data analysis, hazard assessment and decision making. We present the rationale for developing a cloud computing systems and illustrate how this tool can be developed for use in real-time environments. Users would have access to an interactive online image analysis tool without the need for specific remote sensing software on their local system therefore increasing their understanding of the ongoing hazard and mitigate its impact on the surrounding region.

Usage of Thin-Client/Server Architecture in Computer Aided Education

ERIC Educational Resources Information Center

Cimen, Caghan; Kavurucu, Yusuf; Aydin, Halit

2014-01-01

With the advances of technology, thin-client/server architecture has become popular in multi-user/single network environments. Thin-client is a user terminal in which the user can login to a domain and run programs by connecting to a remote server. Recent developments in network and hardware technologies (cloud computing, virtualization, etc.)…

A European Federated Cloud: Innovative distributed computing solutions by EGI

NASA Astrophysics Data System (ADS)

Sipos, Gergely; Turilli, Matteo; Newhouse, Steven; Kacsuk, Peter

2013-04-01

The European Grid Infrastructure (EGI) is the result of pioneering work that has, over the last decade, built a collaborative production infrastructure of uniform services through the federation of national resource providers that supports multi-disciplinary science across Europe and around the world. This presentation will provide an overview of the recently established 'federated cloud computing services' that the National Grid Initiatives (NGIs), operators of EGI, offer to scientific communities. The presentation will explain the technical capabilities of the 'EGI Federated Cloud' and the processes whereby earth and space science researchers can engage with it. EGI's resource centres have been providing services for collaborative, compute- and data-intensive applications for over a decade. Besides the well-established 'grid services', several NGIs already offer privately run cloud services to their national researchers. Many of these researchers recently expressed the need to share these cloud capabilities within their international research collaborations - a model similar to the way the grid emerged through the federation of institutional batch computing and file storage servers. To facilitate the setup of a pan-European cloud service from the NGIs' resources, the EGI-InSPIRE project established a Federated Cloud Task Force in September 2011. The Task Force has a mandate to identify and test technologies for a multinational federated cloud that could be provisioned within EGI by the NGIs. A guiding principle for the EGI Federated Cloud is to remain technology neutral and flexible for both resource providers and users: • Resource providers are allowed to use any cloud hypervisor and management technology to join virtualised resources into the EGI Federated Cloud as long as the site is subscribed to the user-facing interfaces selected by the EGI community. • Users can integrate high level services - such as brokers, portals and customised Virtual Research Environments - with the EGI Federated Cloud as long as these services access cloud resources through the user-facing interfaces selected by the EGI community. The Task Force will be closed in May 2013. It already • Identified key enabling technologies by which a multinational, federated 'Infrastructure as a Service' (IaaS) type cloud can be built from the NGIs' resources; • Deployed a test bed to evaluate the integration of virtualised resources within EGI and to engage with early adopter use cases from different scientific domains; • Integrated cloud resources into the EGI production infrastructure through cloud specific bindings of the EGI information system, monitoring system, authentication system, etc.; • Collected and catalogued requirements concerning the federated cloud services from the feedback of early adopter use cases; • Provided feedback and requirements to relevant technology providers on their implementations and worked with these providers to address those requirements; • Identified issues that need to be addressed by other areas of EGI (such as portal solutions, resource allocation policies, marketing and user support) to reach a production system. The Task Force will publish a blueprint in April 2013. The blueprint will drive the establishment of a production level EGI Federated Cloud service after May 2013.

A Contextual Information Acquisition Approach Based on Semantics and Mashup Technology

NASA Astrophysics Data System (ADS)

He, Yangfan; Li, Lu; He, Keqing; Chen, Xiuhong

Pay per use is an essential feature of cloud computing. Users can make use of some parts of a large scale service to satisfy their requirements, merely at the cost of a little payment. A good understanding of the users' requirement is a prerequisite for choosing the service in need precisely. Context implies users' potential requirements, which can be a complement to the requirements delivered explicitly. However, traditional context-aware computing research always demands some specific kinds of sensors to acquire contextual information, which renders a threshold too high for an application to become context-aware. This paper comes up with an approach which combines contextual information obtained directly and indirectly from the cloud services. Semantic relationship between different kinds of contexts lays foundation for the searching of the cloud services. And mashup technology is adopted to compose the heterogonous services. Abundant contextual information may lend strong support to a comprehensive understanding of users' context and a bettered abstraction of contextual requirements.

Finding Tropical Cyclones on a Cloud Computing Cluster: Using Parallel Virtualization for Large-Scale Climate Simulation Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hasenkamp, Daren; Sim, Alexander; Wehner, Michael

Extensive computing power has been used to tackle issues such as climate changes, fusion energy, and other pressing scientific challenges. These computations produce a tremendous amount of data; however, many of the data analysis programs currently only run a single processor. In this work, we explore the possibility of using the emerging cloud computing platform to parallelize such sequential data analysis tasks. As a proof of concept, we wrap a program for analyzing trends of tropical cyclones in a set of virtual machines (VMs). This approach allows the user to keep their familiar data analysis environment in the VMs, whilemore » we provide the coordination and data transfer services to ensure the necessary input and output are directed to the desired locations. This work extensively exercises the networking capability of the cloud computing systems and has revealed a number of weaknesses in the current cloud system software. In our tests, we are able to scale the parallel data analysis job to a modest number of VMs and achieve a speedup that is comparable to running the same analysis task using MPI. However, compared to MPI based parallelization, the cloud-based approach has a number of advantages. The cloud-based approach is more flexible because the VMs can capture arbitrary software dependencies without requiring the user to rewrite their programs. The cloud-based approach is also more resilient to failure; as long as a single VM is running, it can make progress while as soon as one MPI node fails the whole analysis job fails. In short, this initial work demonstrates that a cloud computing system is a viable platform for distributed scientific data analyses traditionally conducted on dedicated supercomputing systems.« less

Off the Shelf Cloud Robotics for the Smart Home: Empowering a Wireless Robot through Cloud Computing.

PubMed

Ramírez De La Pinta, Javier; Maestre Torreblanca, José María; Jurado, Isabel; Reyes De Cozar, Sergio

2017-03-06

In this paper, we explore the possibilities offered by the integration of home automation systems and service robots. In particular, we examine how advanced computationally expensive services can be provided by using a cloud computing approach to overcome the limitations of the hardware available at the user's home. To this end, we integrate two wireless low-cost, off-the-shelf systems in this work, namely, the service robot Rovio and the home automation system Z-wave. Cloud computing is used to enhance the capabilities of these systems so that advanced sensing and interaction services based on image processing and voice recognition can be offered.

BigDebug: Debugging Primitives for Interactive Big Data Processing in Spark.

PubMed

Gulzar, Muhammad Ali; Interlandi, Matteo; Yoo, Seunghyun; Tetali, Sai Deep; Condie, Tyson; Millstein, Todd; Kim, Miryung

2016-05-01

Developers use cloud computing platforms to process a large quantity of data in parallel when developing big data analytics. Debugging the massive parallel computations that run in today's data-centers is time consuming and error-prone. To address this challenge, we design a set of interactive, real-time debugging primitives for big data processing in Apache Spark, the next generation data-intensive scalable cloud computing platform. This requires re-thinking the notion of step-through debugging in a traditional debugger such as gdb, because pausing the entire computation across distributed worker nodes causes significant delay and naively inspecting millions of records using a watchpoint is too time consuming for an end user. First, BIGDEBUG's simulated breakpoints and on-demand watchpoints allow users to selectively examine distributed, intermediate data on the cloud with little overhead. Second, a user can also pinpoint a crash-inducing record and selectively resume relevant sub-computations after a quick fix. Third, a user can determine the root causes of errors (or delays) at the level of individual records through a fine-grained data provenance capability. Our evaluation shows that BIGDEBUG scales to terabytes and its record-level tracing incurs less than 25% overhead on average. It determines crash culprits orders of magnitude more accurately and provides up to 100% time saving compared to the baseline replay debugger. The results show that BIGDEBUG supports debugging at interactive speeds with minimal performance impact.

Scaling the CERN OpenStack cloud

NASA Astrophysics Data System (ADS)

Bell, T.; Bompastor, B.; Bukowiec, S.; Castro Leon, J.; Denis, M. K.; van Eldik, J.; Fermin Lobo, M.; Fernandez Alvarez, L.; Fernandez Rodriguez, D.; Marino, A.; Moreira, B.; Noel, B.; Oulevey, T.; Takase, W.; Wiebalck, A.; Zilli, S.

2015-12-01

CERN has been running a production OpenStack cloud since July 2013 to support physics computing and infrastructure services for the site. In the past year, CERN Cloud Infrastructure has seen a constant increase in nodes, virtual machines, users and projects. This paper will present what has been done in order to make the CERN cloud infrastructure scale out.

An Analysis of the Use of Cloud Computing among University Lecturers: A Case Study in Zimbabwe

ERIC Educational Resources Information Center

Musungwini, Samuel; Mugoniwa, Beauty; Furusa, Samuel Simbarashe; Rebanowako, Taurai George

2016-01-01

Cloud computing is a novel model of computing that may bring extensive benefits to users, institutions, businesses and academics, while at the same time also giving rise to new risks and challenges. This study looked at the benefits of using Google docs by researchers and academics and analysing the factors affecting the adoption and use of the…

Program listing for the REEDM (Rocket Exhaust Effluent Diffusion Model) computer program

NASA Technical Reports Server (NTRS)

Bjorklund, J. R.; Dumbauld, R. K.; Cheney, C. S.; Geary, H. V.

1982-01-01

The program listing for the REEDM Computer Program is provided. A mathematical description of the atmospheric dispersion models, cloud-rise models, and other formulas used in the REEDM model; vehicle and source parameters, other pertinent physical properties of the rocket exhaust cloud and meteorological layering techniques; user's instructions for the REEDM computer program; and worked example problems are contained in NASA CR-3646.

Low cost, high performance processing of single particle cryo-electron microscopy data in the cloud

PubMed Central

Cianfrocco, Michael A; Leschziner, Andres E

2015-01-01

The advent of a new generation of electron microscopes and direct electron detectors has realized the potential of single particle cryo-electron microscopy (cryo-EM) as a technique to generate high-resolution structures. Calculating these structures requires high performance computing clusters, a resource that may be limiting to many likely cryo-EM users. To address this limitation and facilitate the spread of cryo-EM, we developed a publicly available ‘off-the-shelf’ computing environment on Amazon's elastic cloud computing infrastructure. This environment provides users with single particle cryo-EM software packages and the ability to create computing clusters with 16–480+ CPUs. We tested our computing environment using a publicly available 80S yeast ribosome dataset and estimate that laboratories could determine high-resolution cryo-EM structures for $50 to $1500 per structure within a timeframe comparable to local clusters. Our analysis shows that Amazon's cloud computing environment may offer a viable computing environment for cryo-EM. DOI: http://dx.doi.org/10.7554/eLife.06664.001 PMID:25955969

The StratusLab cloud distribution: Use-cases and support for scientific applications

NASA Astrophysics Data System (ADS)

Floros, E.

2012-04-01

The StratusLab project is integrating an open cloud software distribution that enables organizations to setup and provide their own private or public IaaS (Infrastructure as a Service) computing clouds. StratusLab distribution capitalizes on popular infrastructure virtualization solutions like KVM, the OpenNebula virtual machine manager, Claudia service manager and SlipStream deployment platform, which are further enhanced and expanded with additional components developed within the project. The StratusLab distribution covers the core aspects of a cloud IaaS architecture, namely Computing (life-cycle management of virtual machines), Storage, Appliance management and Networking. The resulting software stack provides a packaged turn-key solution for deploying cloud computing services. The cloud computing infrastructures deployed using StratusLab can support a wide range of scientific and business use cases. Grid computing has been the primary use case pursued by the project and for this reason the initial priority has been the support for the deployment and operation of fully virtualized production-level grid sites; a goal that has already been achieved by operating such a site as part of EGI's (European Grid Initiative) pan-european grid infrastructure. In this area the project is currently working to provide non-trivial capabilities like elastic and autonomic management of grid site resources. Although grid computing has been the motivating paradigm, StratusLab's cloud distribution can support a wider range of use cases. Towards this direction, we have developed and currently provide support for setting up general purpose computing solutions like Hadoop, MPI and Torque clusters. For what concerns scientific applications the project is collaborating closely with the Bioinformatics community in order to prepare VM appliances and deploy optimized services for bioinformatics applications. In a similar manner additional scientific disciplines like Earth Science can take advantage of StratusLab cloud solutions. Interested users are welcomed to join StratusLab's user community by getting access to the reference cloud services deployed by the project and offered to the public.

A Distributed Parallel Genetic Algorithm of Placement Strategy for Virtual Machines Deployment on Cloud Platform

PubMed Central

Dong, Yu-Shuang; Xu, Gao-Chao; Fu, Xiao-Dong

2014-01-01

The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform. PMID:25097872

A distributed parallel genetic algorithm of placement strategy for virtual machines deployment on cloud platform.

PubMed

Dong, Yu-Shuang; Xu, Gao-Chao; Fu, Xiao-Dong

2014-01-01

The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform.

Genomics Virtual Laboratory: A Practical Bioinformatics Workbench for the Cloud

PubMed Central

Afgan, Enis; Sloggett, Clare; Goonasekera, Nuwan; Makunin, Igor; Benson, Derek; Crowe, Mark; Gladman, Simon; Kowsar, Yousef; Pheasant, Michael; Horst, Ron; Lonie, Andrew

2015-01-01

Background Analyzing high throughput genomics data is a complex and compute intensive task, generally requiring numerous software tools and large reference data sets, tied together in successive stages of data transformation and visualisation. A computational platform enabling best practice genomics analysis ideally meets a number of requirements, including: a wide range of analysis and visualisation tools, closely linked to large user and reference data sets; workflow platform(s) enabling accessible, reproducible, portable analyses, through a flexible set of interfaces; highly available, scalable computational resources; and flexibility and versatility in the use of these resources to meet demands and expertise of a variety of users. Access to an appropriate computational platform can be a significant barrier to researchers, as establishing such a platform requires a large upfront investment in hardware, experience, and expertise. Results We designed and implemented the Genomics Virtual Laboratory (GVL) as a middleware layer of machine images, cloud management tools, and online services that enable researchers to build arbitrarily sized compute clusters on demand, pre-populated with fully configured bioinformatics tools, reference datasets and workflow and visualisation options. The platform is flexible in that users can conduct analyses through web-based (Galaxy, RStudio, IPython Notebook) or command-line interfaces, and add/remove compute nodes and data resources as required. Best-practice tutorials and protocols provide a path from introductory training to practice. The GVL is available on the OpenStack-based Australian Research Cloud (http://nectar.org.au) and the Amazon Web Services cloud. The principles, implementation and build process are designed to be cloud-agnostic. Conclusions This paper provides a blueprint for the design and implementation of a cloud-based Genomics Virtual Laboratory. We discuss scope, design considerations and technical and logistical constraints, and explore the value added to the research community through the suite of services and resources provided by our implementation. PMID:26501966

Genomics Virtual Laboratory: A Practical Bioinformatics Workbench for the Cloud.

PubMed

Afgan, Enis; Sloggett, Clare; Goonasekera, Nuwan; Makunin, Igor; Benson, Derek; Crowe, Mark; Gladman, Simon; Kowsar, Yousef; Pheasant, Michael; Horst, Ron; Lonie, Andrew

2015-01-01

Analyzing high throughput genomics data is a complex and compute intensive task, generally requiring numerous software tools and large reference data sets, tied together in successive stages of data transformation and visualisation. A computational platform enabling best practice genomics analysis ideally meets a number of requirements, including: a wide range of analysis and visualisation tools, closely linked to large user and reference data sets; workflow platform(s) enabling accessible, reproducible, portable analyses, through a flexible set of interfaces; highly available, scalable computational resources; and flexibility and versatility in the use of these resources to meet demands and expertise of a variety of users. Access to an appropriate computational platform can be a significant barrier to researchers, as establishing such a platform requires a large upfront investment in hardware, experience, and expertise. We designed and implemented the Genomics Virtual Laboratory (GVL) as a middleware layer of machine images, cloud management tools, and online services that enable researchers to build arbitrarily sized compute clusters on demand, pre-populated with fully configured bioinformatics tools, reference datasets and workflow and visualisation options. The platform is flexible in that users can conduct analyses through web-based (Galaxy, RStudio, IPython Notebook) or command-line interfaces, and add/remove compute nodes and data resources as required. Best-practice tutorials and protocols provide a path from introductory training to practice. The GVL is available on the OpenStack-based Australian Research Cloud (http://nectar.org.au) and the Amazon Web Services cloud. The principles, implementation and build process are designed to be cloud-agnostic. This paper provides a blueprint for the design and implementation of a cloud-based Genomics Virtual Laboratory. We discuss scope, design considerations and technical and logistical constraints, and explore the value added to the research community through the suite of services and resources provided by our implementation.

Enhancing data utilization through adoption of cloud-based data architectures (Invited Paper 211869)

NASA Astrophysics Data System (ADS)

Kearns, E. J.

2017-12-01

A traditional approach to data distribution and utilization of open government data involves continuously moving those data from a central government location to each potential user, who would then utilize them on their local computer systems. An alternate approach would be to bring those users to the open government data, where users would also have access to computing and analytics capabilities that would support data utilization. NOAA's Big Data Project is exploring such an alternate approach through an experimental collaboration with Amazon Web Services, Google Cloud Platform, IBM, Microsoft Azure, and the Open Commons Consortium. As part of this ongoing experiment, NOAA is providing open data of interest which are freely hosted by the Big Data Project Collaborators, who provide a variety of cloud-based services and capabilities to enable utilization by data users. By the terms of the agreement, the Collaborators may charge for those value-added services and processing capacities to recover their costs to freely host the data and to generate profits if so desired. Initial results have shown sustained increases in data utilization from 2 to over 100 times previously-observed access patterns from traditional approaches. Significantly increased utilization speed as compared to the traditional approach has also been observed by NOAA data users who have volunteered their experiences on these cloud-based systems. The potential for implementing and sustaining the alternate cloud-based approach as part of a change in operational data utilization strategies will be discussed.

Geometric Data Perturbation-Based Personal Health Record Transactions in Cloud Computing

PubMed Central

Balasubramaniam, S.; Kavitha, V.

2015-01-01

Cloud computing is a new delivery model for information technology services and it typically involves the provision of dynamically scalable and often virtualized resources over the Internet. However, cloud computing raises concerns on how cloud service providers, user organizations, and governments should handle such information and interactions. Personal health records represent an emerging patient-centric model for health information exchange, and they are outsourced for storage by third parties, such as cloud providers. With these records, it is necessary for each patient to encrypt their own personal health data before uploading them to cloud servers. Current techniques for encryption primarily rely on conventional cryptographic approaches. However, key management issues remain largely unsolved with these cryptographic-based encryption techniques. We propose that personal health record transactions be managed using geometric data perturbation in cloud computing. In our proposed scheme, the personal health record database is perturbed using geometric data perturbation and outsourced to the Amazon EC2 cloud. PMID:25767826

Geometric data perturbation-based personal health record transactions in cloud computing.

PubMed

Balasubramaniam, S; Kavitha, V

2015-01-01

Cloud computing is a new delivery model for information technology services and it typically involves the provision of dynamically scalable and often virtualized resources over the Internet. However, cloud computing raises concerns on how cloud service providers, user organizations, and governments should handle such information and interactions. Personal health records represent an emerging patient-centric model for health information exchange, and they are outsourced for storage by third parties, such as cloud providers. With these records, it is necessary for each patient to encrypt their own personal health data before uploading them to cloud servers. Current techniques for encryption primarily rely on conventional cryptographic approaches. However, key management issues remain largely unsolved with these cryptographic-based encryption techniques. We propose that personal health record transactions be managed using geometric data perturbation in cloud computing. In our proposed scheme, the personal health record database is perturbed using geometric data perturbation and outsourced to the Amazon EC2 cloud.

On-demand Simulation of Atmospheric Transport Processes on the AlpEnDAC Cloud

NASA Astrophysics Data System (ADS)

Hachinger, S.; Harsch, C.; Meyer-Arnek, J.; Frank, A.; Heller, H.; Giemsa, E.

2016-12-01

The "Alpine Environmental Data Analysis Centre" (AlpEnDAC) develops a data-analysis platform for high-altitude research facilities within the "Virtual Alpine Observatory" project (VAO). This platform, with its web portal, will support use cases going much beyond data management: On user request, the data are augmented with "on-demand" simulation results, such as air-parcel trajectories for tracing down the source of pollutants when they appear in high concentration. The respective back-end mechanism uses the Compute Cloud of the Leibniz Supercomputing Centre (LRZ) to transparently calculate results requested by the user, as far as they have not yet been stored in AlpEnDAC. The queuing-system operation model common in supercomputing is replaced by a model in which Virtual Machines (VMs) on the cloud are automatically created/destroyed, providing the necessary computing power immediately on demand. From a security point of view, this allows to perform simulations in a sandbox defined by the VM configuration, without direct access to a computing cluster. Within few minutes, the user receives conveniently visualized results. The AlpEnDAC infrastructure is distributed among two participating institutes [front-end at German Aerospace Centre (DLR), simulation back-end at LRZ], requiring an efficient mechanism for synchronization of measured and augmented data. We discuss our iRODS-based solution for these data-management tasks as well as the general AlpEnDAC framework. Our cloud-based offerings aim at making scientific computing for our users much more convenient and flexible than it has been, and to allow scientists without a broad background in scientific computing to benefit from complex numerical simulations.

Menu-driven cloud computing and resource sharing for R and Bioconductor

PubMed Central

Bolouri, Hamid; Angerman, Michael

2011-01-01

Summary: We report CRdata.org, a cloud-based, free, open-source web server for running analyses and sharing data and R scripts with others. In addition to using the free, public service, CRdata users can launch their own private Amazon Elastic Computing Cloud (EC2) nodes and store private data and scripts on Amazon's Simple Storage Service (S3) with user-controlled access rights. All CRdata services are provided via point-and-click menus. Availability and Implementation: CRdata is open-source and free under the permissive MIT License (opensource.org/licenses/mit-license.php). The source code is in Ruby (ruby-lang.org/en/) and available at: github.com/seerdata/crdata. Contact: hbolouri@fhcrc.org PMID:21685055

The Virtual Geophysics Laboratory (VGL): Scientific Workflows Operating Across Organizations and Across Infrastructures

NASA Astrophysics Data System (ADS)

Cox, S. J.; Wyborn, L. A.; Fraser, R.; Rankine, T.; Woodcock, R.; Vote, J.; Evans, B.

2012-12-01

The Virtual Geophysics Laboratory (VGL) is web portal that provides geoscientists with an integrated online environment that: seamlessly accesses geophysical and geoscience data services from the AuScope national geoscience information infrastructure; loosely couples these data to a variety of gesocience software tools; and provides large scale processing facilities via cloud computing. VGL is a collaboration between CSIRO, Geoscience Australia, National Computational Infrastructure, Monash University, Australian National University and the University of Queensland. The VGL provides a distributed system whereby a user can enter an online virtual laboratory to seamlessly connect to OGC web services for geoscience data. The data is supplied in open standards formats using international standards like GeoSciML. A VGL user uses a web mapping interface to discover and filter the data sources using spatial and attribute filters to define a subset. Once the data is selected the user is not required to download the data. VGL collates the service query information for later in the processing workflow where it will be staged directly to the computing facilities. The combination of deferring data download and access to Cloud computing enables VGL users to access their data at higher resolutions and to undertake larger scale inversions, more complex models and simulations than their own local computing facilities might allow. Inside the Virtual Geophysics Laboratory, the user has access to a library of existing models, complete with exemplar workflows for specific scientific problems based on those models. For example, the user can load a geological model published by Geoscience Australia, apply a basic deformation workflow provided by a CSIRO scientist, and have it run in a scientific code from Monash. Finally the user can publish these results to share with a colleague or cite in a paper. This opens new opportunities for access and collaboration as all the resources (models, code, data, processing) are shared in the one virtual laboratory. VGL provides end users with access to an intuitive, user-centered interface that leverages cloud storage and cloud and cluster processing from both the research communities and commercial suppliers (e.g. Amazon). As the underlying data and information services are agnostic of the scientific domain, they can support many other data types. This fundamental characteristic results in a highly reusable virtual laboratory infrastructure that could also be used for example natural hazards, satellite processing, soil geochemistry, climate modeling, agriculture crop modeling.

Sector and Sphere: the design and implementation of a high-performance data cloud

PubMed Central

Gu, Yunhong; Grossman, Robert L.

2009-01-01

Cloud computing has demonstrated that processing very large datasets over commodity clusters can be done simply, given the right programming model and infrastructure. In this paper, we describe the design and implementation of the Sector storage cloud and the Sphere compute cloud. By contrast with the existing storage and compute clouds, Sector can manage data not only within a data centre, but also across geographically distributed data centres. Similarly, the Sphere compute cloud supports user-defined functions (UDFs) over data both within and across data centres. As a special case, MapReduce-style programming can be implemented in Sphere by using a Map UDF followed by a Reduce UDF. We describe some experimental studies comparing Sector/Sphere and Hadoop using the Terasort benchmark. In these studies, Sector is approximately twice as fast as Hadoop. Sector/Sphere is open source. PMID:19451100

Leveraging the Cloud for Robust and Efficient Lunar Image Processing

NASA Technical Reports Server (NTRS)

Chang, George; Malhotra, Shan; Wolgast, Paul

2011-01-01

The Lunar Mapping and Modeling Project (LMMP) is tasked to aggregate lunar data, from the Apollo era to the latest instruments on the LRO spacecraft, into a central repository accessible by scientists and the general public. A critical function of this task is to provide users with the best solution for browsing the vast amounts of imagery available. The image files LMMP manages range from a few gigabytes to hundreds of gigabytes in size with new data arriving every day. Despite this ever-increasing amount of data, LMMP must make the data readily available in a timely manner for users to view and analyze. This is accomplished by tiling large images into smaller images using Hadoop, a distributed computing software platform implementation of the MapReduce framework, running on a small cluster of machines locally. Additionally, the software is implemented to use Amazon's Elastic Compute Cloud (EC2) facility. We also developed a hybrid solution to serve images to users by leveraging cloud storage using Amazon's Simple Storage Service (S3) for public data while keeping private information on our own data servers. By using Cloud Computing, we improve upon our local solution by reducing the need to manage our own hardware and computing infrastructure, thereby reducing costs. Further, by using a hybrid of local and cloud storage, we are able to provide data to our users more efficiently and securely. 12 This paper examines the use of a distributed approach with Hadoop to tile images, an approach that provides significant improvements in image processing time, from hours to minutes. This paper describes the constraints imposed on the solution and the resulting techniques developed for the hybrid solution of a customized Hadoop infrastructure over local and cloud resources in managing this ever-growing data set. It examines the performance trade-offs of using the more plentiful resources of the cloud, such as those provided by S3, against the bandwidth limitations such use encounters with remote resources. As part of this discussion this paper will outline some of the technologies employed, the reasons for their selection, the resulting performance metrics and the direction the project is headed based upon the demonstrated capabilities thus far.

Military clouds: utilization of cloud computing systems at the battlefield

NASA Astrophysics Data System (ADS)

Süleyman, Sarıkürk; Volkan, Karaca; İbrahim, Kocaman; Ahmet, Şirzai

2012-05-01

Cloud computing is known as a novel information technology (IT) concept, which involves facilitated and rapid access to networks, servers, data saving media, applications and services via Internet with minimum hardware requirements. Use of information systems and technologies at the battlefield is not new. Information superiority is a force multiplier and is crucial to mission success. Recent advances in information systems and technologies provide new means to decision makers and users in order to gain information superiority. These developments in information technologies lead to a new term, which is known as network centric capability. Similar to network centric capable systems, cloud computing systems are operational today. In the near future extensive use of military clouds at the battlefield is predicted. Integrating cloud computing logic to network centric applications will increase the flexibility, cost-effectiveness, efficiency and accessibility of network-centric capabilities. In this paper, cloud computing and network centric capability concepts are defined. Some commercial cloud computing products and applications are mentioned. Network centric capable applications are covered. Cloud computing supported battlefield applications are analyzed. The effects of cloud computing systems on network centric capability and on the information domain in future warfare are discussed. Battlefield opportunities and novelties which might be introduced to network centric capability by cloud computing systems are researched. The role of military clouds in future warfare is proposed in this paper. It was concluded that military clouds will be indispensible components of the future battlefield. Military clouds have the potential of improving network centric capabilities, increasing situational awareness at the battlefield and facilitating the settlement of information superiority.

Game Theory Based Trust Model for Cloud Environment

PubMed Central

Gokulnath, K.; Uthariaraj, Rhymend

2015-01-01

The aim of this work is to propose a method to establish trust at bootload level in cloud computing environment. This work proposes a game theoretic based approach for achieving trust at bootload level of both resources and users perception. Nash equilibrium (NE) enhances the trust evaluation of the first-time users and providers. It also restricts the service providers and the users to violate service level agreement (SLA). Significantly, the problem of cold start and whitewashing issues are addressed by the proposed method. In addition appropriate mapping of cloud user's application to cloud service provider for segregating trust level is achieved as a part of mapping. Thus, time complexity and space complexity are handled efficiently. Experiments were carried out to compare and contrast the performance of the conventional methods and the proposed method. Several metrics like execution time, accuracy, error identification, and undecidability of the resources were considered. PMID:26380365

A City Parking Integration System Combined with Cloud Computing Technologies and Smart Mobile Devices

ERIC Educational Resources Information Center

Yeh, Her-Tyan; Chen, Bing-Chang; Wang, Bo-Xun

2016-01-01

The current study applied cloud computing technology and smart mobile devices combined with a streaming server for parking lots to plan a city parking integration system. It is also equipped with a parking search system, parking navigation system, parking reservation service, and car retrieval service. With this system, users can quickly find…

Service Mediation and Negotiation Bootstrapping as First Achievements Towards Self-adaptable Cloud Services

NASA Astrophysics Data System (ADS)

Brandic, Ivona; Music, Dejan; Dustdar, Schahram

Nowadays, novel computing paradigms as for example Cloud Computing are gaining more and more on importance. In case of Cloud Computing users pay for the usage of the computing power provided as a service. Beforehand they can negotiate specific functional and non-functional requirements relevant for the application execution. However, providing computing power as a service bears different research challenges. On one hand dynamic, versatile, and adaptable services are required, which can cope with system failures and environmental changes. On the other hand, human interaction with the system should be minimized. In this chapter we present the first results in establishing adaptable, versatile, and dynamic services considering negotiation bootstrapping and service mediation achieved in context of the Foundations of Self-Governing ICT Infrastructures (FoSII) project. We discuss novel meta-negotiation and SLA mapping solutions for Cloud services bridging the gap between current QoS models and Cloud middleware and representing important prerequisites for the establishment of autonomic Cloud services.

Validity of association rules extracted by healthcare-data-mining.

PubMed

Takeuchi, Hiroshi; Kodama, Naoki

2014-01-01

A personal healthcare system used with cloud computing has been developed. It enables a daily time-series of personal health and lifestyle data to be stored in the cloud through mobile devices. The cloud automatically extracts personally useful information, such as rules and patterns concerning the user's lifestyle and health condition embedded in their personal big data, by using healthcare-data-mining. This study has verified that the extracted rules on the basis of a daily time-series data stored during a half- year by volunteer users of this system are valid.

Analysis of an ABE Scheme with Verifiable Outsourced Decryption.

PubMed

Liao, Yongjian; He, Yichuan; Li, Fagen; Jiang, Shaoquan; Zhou, Shijie

2018-01-10

Attribute-based encryption (ABE) is a popular cryptographic technology to protect the security of users' data in cloud computing. In order to reduce its decryption cost, outsourcing the decryption of ciphertexts is an available method, which enables users to outsource a large number of decryption operations to the cloud service provider. To guarantee the correctness of transformed ciphertexts computed by the cloud server via the outsourced decryption, it is necessary to check the correctness of the outsourced decryption to ensure security for the data of users. Recently, Li et al. proposed a full verifiability of the outsourced decryption of ABE scheme (ABE-VOD) for the authorized users and unauthorized users, which can simultaneously check the correctness of the transformed ciphertext for both them. However, in this paper we show that their ABE-VOD scheme cannot obtain the results which they had shown, such as finding out all invalid ciphertexts, and checking the correctness of the transformed ciphertext for the authorized user via checking it for the unauthorized user. We first construct some invalid ciphertexts which can pass the validity checking in the decryption algorithm. That means their "verify-then-decrypt" skill is unavailable. Next, we show that the method to check the validity of the outsourced decryption for the authorized users via checking it for the unauthorized users is not always correct. That is to say, there exist some invalid ciphertexts which can pass the validity checking for the unauthorized user, but cannot pass the validity checking for the authorized user.

A Secure and Verifiable Outsourced Access Control Scheme in Fog-Cloud Computing.

PubMed

Fan, Kai; Wang, Junxiong; Wang, Xin; Li, Hui; Yang, Yintang

2017-07-24

With the rapid development of big data and Internet of things (IOT), the number of networking devices and data volume are increasing dramatically. Fog computing, which extends cloud computing to the edge of the network can effectively solve the bottleneck problems of data transmission and data storage. However, security and privacy challenges are also arising in the fog-cloud computing environment. Ciphertext-policy attribute-based encryption (CP-ABE) can be adopted to realize data access control in fog-cloud computing systems. In this paper, we propose a verifiable outsourced multi-authority access control scheme, named VO-MAACS. In our construction, most encryption and decryption computations are outsourced to fog devices and the computation results can be verified by using our verification method. Meanwhile, to address the revocation issue, we design an efficient user and attribute revocation method for it. Finally, analysis and simulation results show that our scheme is both secure and highly efficient.

BigDebug: Debugging Primitives for Interactive Big Data Processing in Spark

PubMed Central

Gulzar, Muhammad Ali; Interlandi, Matteo; Yoo, Seunghyun; Tetali, Sai Deep; Condie, Tyson; Millstein, Todd; Kim, Miryung

2016-01-01

Developers use cloud computing platforms to process a large quantity of data in parallel when developing big data analytics. Debugging the massive parallel computations that run in today’s data-centers is time consuming and error-prone. To address this challenge, we design a set of interactive, real-time debugging primitives for big data processing in Apache Spark, the next generation data-intensive scalable cloud computing platform. This requires re-thinking the notion of step-through debugging in a traditional debugger such as gdb, because pausing the entire computation across distributed worker nodes causes significant delay and naively inspecting millions of records using a watchpoint is too time consuming for an end user. First, BIGDEBUG’s simulated breakpoints and on-demand watchpoints allow users to selectively examine distributed, intermediate data on the cloud with little overhead. Second, a user can also pinpoint a crash-inducing record and selectively resume relevant sub-computations after a quick fix. Third, a user can determine the root causes of errors (or delays) at the level of individual records through a fine-grained data provenance capability. Our evaluation shows that BIGDEBUG scales to terabytes and its record-level tracing incurs less than 25% overhead on average. It determines crash culprits orders of magnitude more accurately and provides up to 100% time saving compared to the baseline replay debugger. The results show that BIGDEBUG supports debugging at interactive speeds with minimal performance impact. PMID:27390389

Are Cloud Environments Ready for Scientific Applications?

NASA Astrophysics Data System (ADS)

Mehrotra, P.; Shackleford, K.

2011-12-01

Cloud computing environments are becoming widely available both in the commercial and government sectors. They provide flexibility to rapidly provision resources in order to meet dynamic and changing computational needs without the customers incurring capital expenses and/or requiring technical expertise. Clouds also provide reliable access to resources even though the end-user may not have in-house expertise for acquiring or operating such resources. Consolidation and pooling in a cloud environment allow organizations to achieve economies of scale in provisioning or procuring computing resources and services. Because of these and other benefits, many businesses and organizations are migrating their business applications (e.g., websites, social media, and business processes) to cloud environments-evidenced by the commercial success of offerings such as the Amazon EC2. In this paper, we focus on the feasibility of utilizing cloud environments for scientific workloads and workflows particularly of interest to NASA scientists and engineers. There is a wide spectrum of such technical computations. These applications range from small workstation-level computations to mid-range computing requiring small clusters to high-performance simulations requiring supercomputing systems with high bandwidth/low latency interconnects. Data-centric applications manage and manipulate large data sets such as satellite observational data and/or data previously produced by high-fidelity modeling and simulation computations. Most of the applications are run in batch mode with static resource requirements. However, there do exist situations that have dynamic demands, particularly ones with public-facing interfaces providing information to the general public, collaborators and partners, as well as to internal NASA users. In the last few months we have been studying the suitability of cloud environments for NASA's technical and scientific workloads. We have ported several applications to multiple cloud environments including NASA's Nebula environment, Amazon's EC2, Magellan at NERSC, and SGI's Cyclone system. We critically examined the performance of the applications on these systems. We also collected information on the usability of these cloud environments. In this talk we will present the results of our study focusing on the efficacy of using clouds for NASA's scientific applications.

Evolving the Land Information System into a Cloud Computing Service

DOE Office of Scientific and Technical Information (OSTI.GOV)

Houser, Paul R.

The Land Information System (LIS) was developed to use advanced flexible land surface modeling and data assimilation frameworks to integrate extremely large satellite- and ground-based observations with advanced land surface models to produce continuous high-resolution fields of land surface states and fluxes. The resulting fields are extremely useful for drought and flood assessment, agricultural planning, disaster management, weather and climate forecasting, water resources assessment, and the like. We envisioned transforming the LIS modeling system into a scientific cloud computing-aware web and data service that would allow clients to easily setup and configure for use in addressing large water management issues.more » The focus of this Phase 1 project was to determine the scientific, technical, commercial merit and feasibility of the proposed LIS-cloud innovations that are currently barriers to broad LIS applicability. We (a) quantified the barriers to broad LIS utility and commercialization (high performance computing, big data, user interface, and licensing issues); (b) designed the proposed LIS-cloud web service, model-data interface, database services, and user interfaces; (c) constructed a prototype LIS user interface including abstractions for simulation control, visualization, and data interaction, (d) used the prototype to conduct a market analysis and survey to determine potential market size and competition, (e) identified LIS software licensing and copyright limitations and developed solutions, and (f) developed a business plan for development and marketing of the LIS-cloud innovation. While some significant feasibility issues were found in the LIS licensing, overall a high degree of LIS-cloud technical feasibility was found.« less

Cloud computing for genomic data analysis and collaboration.

PubMed

Langmead, Ben; Nellore, Abhinav

2018-04-01

Next-generation sequencing has made major strides in the past decade. Studies based on large sequencing data sets are growing in number, and public archives for raw sequencing data have been doubling in size every 18 months. Leveraging these data requires researchers to use large-scale computational resources. Cloud computing, a model whereby users rent computers and storage from large data centres, is a solution that is gaining traction in genomics research. Here, we describe how cloud computing is used in genomics for research and large-scale collaborations, and argue that its elasticity, reproducibility and privacy features make it ideally suited for the large-scale reanalysis of publicly available archived data, including privacy-protected data.

An infrastructure with a unified control plane to integrate IP into optical metro networks to provide flexible and intelligent bandwidth on demand for cloud computing

NASA Astrophysics Data System (ADS)

Yang, Wei; Hall, Trevor

2012-12-01

The Internet is entering an era of cloud computing to provide more cost effective, eco-friendly and reliable services to consumer and business users and the nature of the Internet traffic will undertake a fundamental transformation. Consequently, the current Internet will no longer suffice for serving cloud traffic in metro areas. This work proposes an infrastructure with a unified control plane that integrates simple packet aggregation technology with optical express through the interoperation between IP routers and electrical traffic controllers in optical metro networks. The proposed infrastructure provides flexible, intelligent, and eco-friendly bandwidth on demand for cloud computing in metro areas.

A security-awareness virtual machine management scheme based on Chinese wall policy in cloud computing.

PubMed

Yu, Si; Gui, Xiaolin; Lin, Jiancai; Tian, Feng; Zhao, Jianqiang; Dai, Min

2014-01-01

Cloud computing gets increasing attention for its capacity to leverage developers from infrastructure management tasks. However, recent works reveal that side channel attacks can lead to privacy leakage in the cloud. Enhancing isolation between users is an effective solution to eliminate the attack. In this paper, to eliminate side channel attacks, we investigate the isolation enhancement scheme from the aspect of virtual machine (VM) management. The security-awareness VMs management scheme (SVMS), a VMs isolation enhancement scheme to defend against side channel attacks, is proposed. First, we use the aggressive conflict of interest relation (ACIR) and aggressive in ally with relation (AIAR) to describe user constraint relations. Second, based on the Chinese wall policy, we put forward four isolation rules. Third, the VMs placement and migration algorithms are designed to enforce VMs isolation between the conflict users. Finally, based on the normal distribution, we conduct a series of experiments to evaluate SVMS. The experimental results show that SVMS is efficient in guaranteeing isolation between VMs owned by conflict users, while the resource utilization rate decreases but not by much.

A Security-Awareness Virtual Machine Management Scheme Based on Chinese Wall Policy in Cloud Computing

PubMed Central

Gui, Xiaolin; Lin, Jiancai; Tian, Feng; Zhao, Jianqiang; Dai, Min

2014-01-01

Cloud computing gets increasing attention for its capacity to leverage developers from infrastructure management tasks. However, recent works reveal that side channel attacks can lead to privacy leakage in the cloud. Enhancing isolation between users is an effective solution to eliminate the attack. In this paper, to eliminate side channel attacks, we investigate the isolation enhancement scheme from the aspect of virtual machine (VM) management. The security-awareness VMs management scheme (SVMS), a VMs isolation enhancement scheme to defend against side channel attacks, is proposed. First, we use the aggressive conflict of interest relation (ACIR) and aggressive in ally with relation (AIAR) to describe user constraint relations. Second, based on the Chinese wall policy, we put forward four isolation rules. Third, the VMs placement and migration algorithms are designed to enforce VMs isolation between the conflict users. Finally, based on the normal distribution, we conduct a series of experiments to evaluate SVMS. The experimental results show that SVMS is efficient in guaranteeing isolation between VMs owned by conflict users, while the resource utilization rate decreases but not by much. PMID:24688434

A browser-based 3D Visualization Tool designed for comparing CERES/CALIOP/CloudSAT level-2 data sets.

NASA Astrophysics Data System (ADS)

Chu, C.; Sun-Mack, S.; Chen, Y.; Heckert, E.; Doelling, D. R.

2017-12-01

In Langley NASA, Clouds and the Earth's Radiant Energy System (CERES) and Moderate Resolution Imaging Spectroradiometer (MODIS) are merged with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and CloudSat Cloud Profiling Radar (CPR). The CERES merged product (C3M) matches up to three CALIPSO footprints with each MODIS pixel along its ground track. It then assigns the nearest CloudSat footprint to each of those MODIS pixels. The cloud properties from MODIS, retrieved using the CERES algorithms, are included in C3M with the matched CALIPSO and CloudSat products along with radiances from 18 MODIS channels. The dataset is used to validate the CERES retrieved MODIS cloud properties and the computed TOA and surface flux difference using MODIS or CALIOP/CloudSAT retrieved clouds. This information is then used to tune the computed fluxes to match the CERES observed TOA flux. A visualization tool will be invaluable to determine the cause of these large cloud and flux differences in order to improve the methodology. This effort is part of larger effort to allow users to order the CERES C3M product sub-setted by time and parameter as well as the previously mentioned visualization capabilities. This presentation will show a new graphical 3D-interface, 3D-CERESVis, that allows users to view both passive remote sensing satellites (MODIS and CERES) and active satellites (CALIPSO and CloudSat), such that the detailed vertical structures of cloud properties from CALIPSO and CloudSat are displayed side by side with horizontally retrieved cloud properties from MODIS and CERES. Similarly, the CERES computed profile fluxes whether using MODIS or CALIPSO and CloudSat clouds can also be compared. 3D-CERESVis is a browser-based visualization tool that makes uses of techniques such as multiple synchronized cursors, COLLADA format data and Cesium.

Cloudweaver: Adaptive and Data-Driven Workload Manager for Generic Clouds

NASA Astrophysics Data System (ADS)

Li, Rui; Chen, Lei; Li, Wen-Syan

Cloud computing denotes the latest trend in application development for parallel computing on massive data volumes. It relies on clouds of servers to handle tasks that used to be managed by an individual server. With cloud computing, software vendors can provide business intelligence and data analytic services for internet scale data sets. Many open source projects, such as Hadoop, offer various software components that are essential for building a cloud infrastructure. Current Hadoop (and many others) requires users to configure cloud infrastructures via programs and APIs and such configuration is fixed during the runtime. In this chapter, we propose a workload manager (WLM), called CloudWeaver, which provides automated configuration of a cloud infrastructure for runtime execution. The workload management is data-driven and can adapt to dynamic nature of operator throughput during different execution phases. CloudWeaver works for a single job and a workload consisting of multiple jobs running concurrently, which aims at maximum throughput using a minimum set of processors.

Cloud Computing and the Power to Choose

ERIC Educational Resources Information Center

Bristow, Rob; Dodds, Ted; Northam, Richard; Plugge, Leo

2010-01-01

Some of the most significant changes in information technology are those that have given the individual user greater power to choose. The first of these changes was the development of the personal computer. The PC liberated the individual user from the limitations of the mainframe and minicomputers and from the rules and regulations of centralized…

Spontaneous Ad Hoc Mobile Cloud Computing Network

PubMed Central

Lacuesta, Raquel; Sendra, Sandra; Peñalver, Lourdes

2014-01-01

Cloud computing helps users and companies to share computing resources instead of having local servers or personal devices to handle the applications. Smart devices are becoming one of the main information processing devices. Their computing features are reaching levels that let them create a mobile cloud computing network. But sometimes they are not able to create it and collaborate actively in the cloud because it is difficult for them to build easily a spontaneous network and configure its parameters. For this reason, in this paper, we are going to present the design and deployment of a spontaneous ad hoc mobile cloud computing network. In order to perform it, we have developed a trusted algorithm that is able to manage the activity of the nodes when they join and leave the network. The paper shows the network procedures and classes that have been designed. Our simulation results using Castalia show that our proposal presents a good efficiency and network performance even by using high number of nodes. PMID:25202715

Spontaneous ad hoc mobile cloud computing network.

PubMed

Lacuesta, Raquel; Lloret, Jaime; Sendra, Sandra; Peñalver, Lourdes

2014-01-01

Cloud computing helps users and companies to share computing resources instead of having local servers or personal devices to handle the applications. Smart devices are becoming one of the main information processing devices. Their computing features are reaching levels that let them create a mobile cloud computing network. But sometimes they are not able to create it and collaborate actively in the cloud because it is difficult for them to build easily a spontaneous network and configure its parameters. For this reason, in this paper, we are going to present the design and deployment of a spontaneous ad hoc mobile cloud computing network. In order to perform it, we have developed a trusted algorithm that is able to manage the activity of the nodes when they join and leave the network. The paper shows the network procedures and classes that have been designed. Our simulation results using Castalia show that our proposal presents a good efficiency and network performance even by using high number of nodes.

Analysis of an ABE Scheme with Verifiable Outsourced Decryption

PubMed Central

He, Yichuan; Li, Fagen; Jiang, Shaoquan; Zhou, Shijie

2018-01-01

Attribute-based encryption (ABE) is a popular cryptographic technology to protect the security of users’ data in cloud computing. In order to reduce its decryption cost, outsourcing the decryption of ciphertexts is an available method, which enables users to outsource a large number of decryption operations to the cloud service provider. To guarantee the correctness of transformed ciphertexts computed by the cloud server via the outsourced decryption, it is necessary to check the correctness of the outsourced decryption to ensure security for the data of users. Recently, Li et al. proposed a full verifiability of the outsourced decryption of ABE scheme (ABE-VOD) for the authorized users and unauthorized users, which can simultaneously check the correctness of the transformed ciphertext for both them. However, in this paper we show that their ABE-VOD scheme cannot obtain the results which they had shown, such as finding out all invalid ciphertexts, and checking the correctness of the transformed ciphertext for the authorized user via checking it for the unauthorized user. We first construct some invalid ciphertexts which can pass the validity checking in the decryption algorithm. That means their “verify-then-decrypt” skill is unavailable. Next, we show that the method to check the validity of the outsourced decryption for the authorized users via checking it for the unauthorized users is not always correct. That is to say, there exist some invalid ciphertexts which can pass the validity checking for the unauthorized user, but cannot pass the validity checking for the authorized user. PMID:29320418

Dynamic Extension of a Virtualized Cluster by using Cloud Resources

NASA Astrophysics Data System (ADS)

Oberst, Oliver; Hauth, Thomas; Kernert, David; Riedel, Stephan; Quast, Günter

2012-12-01

The specific requirements concerning the software environment within the HEP community constrain the choice of resource providers for the outsourcing of computing infrastructure. The use of virtualization in HPC clusters and in the context of cloud resources is therefore a subject of recent developments in scientific computing. The dynamic virtualization of worker nodes in common batch systems provided by ViBatch serves each user with a dynamically virtualized subset of worker nodes on a local cluster. Now it can be transparently extended by the use of common open source cloud interfaces like OpenNebula or Eucalyptus, launching a subset of the virtual worker nodes within the cloud. This paper demonstrates how a dynamically virtualized computing cluster is combined with cloud resources by attaching remotely started virtual worker nodes to the local batch system.

Efficient operating system level virtualization techniques for cloud resources

NASA Astrophysics Data System (ADS)

Ansu, R.; Samiksha; Anju, S.; Singh, K. John

2017-11-01

Cloud computing is an advancing technology which provides the servcies of Infrastructure, Platform and Software. Virtualization and Computer utility are the keys of Cloud computing. The numbers of cloud users are increasing day by day. So it is the need of the hour to make resources available on demand to satisfy user requirements. The technique in which resources namely storage, processing power, memory and network or I/O are abstracted is known as Virtualization. For executing the operating systems various virtualization techniques are available. They are: Full System Virtualization and Para Virtualization. In Full Virtualization, the whole architecture of hardware is duplicated virtually. No modifications are required in Guest OS as the OS deals with the VM hypervisor directly. In Para Virtualization, modifications of OS is required to run in parallel with other OS. For the Guest OS to access the hardware, the host OS must provide a Virtual Machine Interface. OS virtualization has many advantages such as migrating applications transparently, consolidation of server, online maintenance of OS and providing security. This paper briefs both the virtualization techniques and discusses the issues in OS level virtualization.

Cloud-based crowd sensing: a framework for location-based crowd analyzer and advisor

NASA Astrophysics Data System (ADS)

Aishwarya, K. C.; Nambi, A.; Hudson, S.; Nadesh, R. K.

2017-11-01

Cloud computing is an emerging field of computer science to integrate and explore large and powerful computing systems and storages for personal and also for enterprise requirements. Mobile Cloud Computing is the inheritance of this concept towards mobile hand-held devices. Crowdsensing, or to be precise, Mobile Crowdsensing is the process of sharing resources from an available group of mobile handheld devices that support sharing of different resources such as data, memory and bandwidth to perform a single task for collective reasons. In this paper, we propose a framework to use Crowdsensing and perform a crowd analyzer and advisor whether the user can go to the place or not. This is an ongoing research and is a new concept to which the direction of cloud computing has shifted and is viable for more expansion in the near future.

An expert fitness diagnosis system based on elastic cloud computing.

PubMed

Tseng, Kevin C; Wu, Chia-Chuan

2014-01-01

This paper presents an expert diagnosis system based on cloud computing. It classifies a user's fitness level based on supervised machine learning techniques. This system is able to learn and make customized diagnoses according to the user's physiological data, such as age, gender, and body mass index (BMI). In addition, an elastic algorithm based on Poisson distribution is presented to allocate computation resources dynamically. It predicts the required resources in the future according to the exponential moving average of past observations. The experimental results show that Naïve Bayes is the best classifier with the highest accuracy (90.8%) and that the elastic algorithm is able to capture tightly the trend of requests generated from the Internet and thus assign corresponding computation resources to ensure the quality of service.

An Analysis of Cloud Computing with Amazon Web Services for the Atmospheric Science Data Center

NASA Astrophysics Data System (ADS)

Gleason, J. L.; Little, M. M.

2013-12-01

NASA science and engineering efforts rely heavily on compute and data handling systems. The nature of NASA science data is such that it is not restricted to NASA users, instead it is widely shared across a globally distributed user community including scientists, educators, policy decision makers, and the public. Therefore NASA science computing is a candidate use case for cloud computing where compute resources are outsourced to an external vendor. Amazon Web Services (AWS) is a commercial cloud computing service developed to use excess computing capacity at Amazon, and potentially provides an alternative to costly and potentially underutilized dedicated acquisitions whenever NASA scientists or engineers require additional data processing. AWS desires to provide a simplified avenue for NASA scientists and researchers to share large, complex data sets with external partners and the public. AWS has been extensively used by JPL for a wide range of computing needs and was previously tested on a NASA Agency basis during the Nebula testing program. Its ability to support the Langley Science Directorate needs to be evaluated by integrating it with real world operational needs across NASA and the associated maturity that would come with that. The strengths and weaknesses of this architecture and its ability to support general science and engineering applications has been demonstrated during the previous testing. The Langley Office of the Chief Information Officer in partnership with the Atmospheric Sciences Data Center (ASDC) has established a pilot business interface to utilize AWS cloud computing resources on a organization and project level pay per use model. This poster discusses an effort to evaluate the feasibility of the pilot business interface from a project level perspective by specifically using a processing scenario involving the Clouds and Earth's Radiant Energy System (CERES) project.

Integration of High-Performance Computing into Cloud Computing Services

NASA Astrophysics Data System (ADS)

Vouk, Mladen A.; Sills, Eric; Dreher, Patrick

High-Performance Computing (HPC) projects span a spectrum of computer hardware implementations ranging from peta-flop supercomputers, high-end tera-flop facilities running a variety of operating systems and applications, to mid-range and smaller computational clusters used for HPC application development, pilot runs and prototype staging clusters. What they all have in common is that they operate as a stand-alone system rather than a scalable and shared user re-configurable resource. The advent of cloud computing has changed the traditional HPC implementation. In this article, we will discuss a very successful production-level architecture and policy framework for supporting HPC services within a more general cloud computing infrastructure. This integrated environment, called Virtual Computing Lab (VCL), has been operating at NC State since fall 2004. Nearly 8,500,000 HPC CPU-Hrs were delivered by this environment to NC State faculty and students during 2009. In addition, we present and discuss operational data that show that integration of HPC and non-HPC (or general VCL) services in a cloud can substantially reduce the cost of delivering cloud services (down to cents per CPU hour).

Survey on Security Issues in File Management in Cloud Computing Environment

NASA Astrophysics Data System (ADS)

Gupta, Udit

2015-06-01

Cloud computing has pervaded through every aspect of Information technology in past decade. It has become easier to process plethora of data, generated by various devices in real time, with the advent of cloud networks. The privacy of users data is maintained by data centers around the world and hence it has become feasible to operate on that data from lightweight portable devices. But with ease of processing comes the security aspect of the data. One such security aspect is secure file transfer either internally within cloud or externally from one cloud network to another. File management is central to cloud computing and it is paramount to address the security concerns which arise out of it. This survey paper aims to elucidate the various protocols which can be used for secure file transfer and analyze the ramifications of using each protocol.

Design and Development of ChemInfoCloud: An Integrated Cloud Enabled Platform for Virtual Screening.

PubMed

Karthikeyan, Muthukumarasamy; Pandit, Deepak; Bhavasar, Arvind; Vyas, Renu

2015-01-01

The power of cloud computing and distributed computing has been harnessed to handle vast and heterogeneous data required to be processed in any virtual screening protocol. A cloud computing platorm ChemInfoCloud was built and integrated with several chemoinformatics and bioinformatics tools. The robust engine performs the core chemoinformatics tasks of lead generation, lead optimisation and property prediction in a fast and efficient manner. It has also been provided with some of the bioinformatics functionalities including sequence alignment, active site pose prediction and protein ligand docking. Text mining, NMR chemical shift (1H, 13C) prediction and reaction fingerprint generation modules for efficient lead discovery are also implemented in this platform. We have developed an integrated problem solving cloud environment for virtual screening studies that also provides workflow management, better usability and interaction with end users using container based virtualization, OpenVz.

Benefits of cloud computing for PACS and archiving.

PubMed

Koch, Patrick

2012-01-01

The goal of cloud-based services is to provide easy, scalable access to computing resources and IT services. The healthcare industry requires a private cloud that adheres to government mandates designed to ensure privacy and security of patient data while enabling access by authorized users. Cloud-based computing in the imaging market has evolved from a service that provided cost effective disaster recovery for archived data to fully featured PACS and vendor neutral archiving services that can address the needs of healthcare providers of all sizes. Healthcare providers worldwide are now using the cloud to distribute images to remote radiologists while supporting advanced reading tools, deliver radiology reports and imaging studies to referring physicians, and provide redundant data storage. Vendor managed cloud services eliminate large capital investments in equipment and maintenance, as well as staffing for the data center--creating a reduction in total cost of ownership for the healthcare provider.

Enabling Analytics in the Cloud for Earth Science Data

NASA Technical Reports Server (NTRS)

Ramachandran, Rahul; Lynnes, Christopher; Bingham, Andrew W.; Quam, Brandi M.

2018-01-01

The purpose of this workshop was to hold interactive discussions where providers, users, and other stakeholders could explore the convergence of three main elements in the rapidly developing world of technology: Big Data, Cloud Computing, and Analytics, [for earth science data].

Tools for Analyzing Computing Resource Management Strategies and Algorithms for SDR Clouds

NASA Astrophysics Data System (ADS)

Marojevic, Vuk; Gomez-Miguelez, Ismael; Gelonch, Antoni

2012-09-01

Software defined radio (SDR) clouds centralize the computing resources of base stations. The computing resource pool is shared between radio operators and dynamically loads and unloads digital signal processing chains for providing wireless communications services on demand. Each new user session request particularly requires the allocation of computing resources for executing the corresponding SDR transceivers. The huge amount of computing resources of SDR cloud data centers and the numerous session requests at certain hours of a day require an efficient computing resource management. We propose a hierarchical approach, where the data center is divided in clusters that are managed in a distributed way. This paper presents a set of computing resource management tools for analyzing computing resource management strategies and algorithms for SDR clouds. We use the tools for evaluating a different strategies and algorithms. The results show that more sophisticated algorithms can achieve higher resource occupations and that a tradeoff exists between cluster size and algorithm complexity.

Efficient Redundancy Techniques in Cloud and Desktop Grid Systems using MAP/G/c-type Queues

NASA Astrophysics Data System (ADS)

Chakravarthy, Srinivas R.; Rumyantsev, Alexander

2018-03-01

Cloud computing is continuing to prove its flexibility and versatility in helping industries and businesses as well as academia as a way of providing needed computing capacity. As an important alternative to cloud computing, desktop grids allow to utilize the idle computer resources of an enterprise/community by means of distributed computing system, providing a more secure and controllable environment with lower operational expenses. Further, both cloud computing and desktop grids are meant to optimize limited resources and at the same time to decrease the expected latency for users. The crucial parameter for optimization both in cloud computing and in desktop grids is the level of redundancy (replication) for service requests/workunits. In this paper we study the optimal replication policies by considering three variations of Fork-Join systems in the context of a multi-server queueing system with a versatile point process for the arrivals. For services we consider phase type distributions as well as shifted exponential and Weibull. We use both analytical and simulation approach in our analysis and report some interesting qualitative results.

The HEPiX Virtualisation Working Group: Towards a Grid of Clouds

NASA Astrophysics Data System (ADS)

Cass, Tony

2012-12-01

The use of virtual machine images, as for example with Cloud services such as Amazon's Elastic Compute Cloud, is attractive for users as they have a guaranteed execution environment, something that cannot today be provided across sites participating in computing grids such as the Worldwide LHC Computing Grid. However, Grid sites often operate within computer security frameworks which preclude the use of remotely generated images. The HEPiX Virtualisation Working Group was setup with the objective to enable use of remotely generated virtual machine images at Grid sites and, to this end, has introduced the idea of trusted virtual machine images which are guaranteed to be secure and configurable by sites such that security policy commitments can be met. This paper describes the requirements and details of these trusted virtual machine images and presents a model for their use to facilitate the integration of Grid- and Cloud-based computing environments for High Energy Physics.

An Efficient Virtual Machine Consolidation Scheme for Multimedia Cloud Computing

PubMed Central

Han, Guangjie; Que, Wenhui; Jia, Gangyong; Shu, Lei

2016-01-01

Cloud computing has innovated the IT industry in recent years, as it can delivery subscription-based services to users in the pay-as-you-go model. Meanwhile, multimedia cloud computing is emerging based on cloud computing to provide a variety of media services on the Internet. However, with the growing popularity of multimedia cloud computing, its large energy consumption cannot only contribute to greenhouse gas emissions, but also result in the rising of cloud users’ costs. Therefore, the multimedia cloud providers should try to minimize its energy consumption as much as possible while satisfying the consumers’ resource requirements and guaranteeing quality of service (QoS). In this paper, we have proposed a remaining utilization-aware (RUA) algorithm for virtual machine (VM) placement, and a power-aware algorithm (PA) is proposed to find proper hosts to shut down for energy saving. These two algorithms have been combined and applied to cloud data centers for completing the process of VM consolidation. Simulation results have shown that there exists a trade-off between the cloud data center’s energy consumption and service-level agreement (SLA) violations. Besides, the RUA algorithm is able to deal with variable workload to prevent hosts from overloading after VM placement and to reduce the SLA violations dramatically. PMID:26901201

Hydrodynamics and Water Quality forecasting over a Cloud Computing environment: INDIGO-DataCloud

NASA Astrophysics Data System (ADS)

Aguilar Gómez, Fernando; de Lucas, Jesús Marco; García, Daniel; Monteoliva, Agustín

2017-04-01

Algae Bloom due to eutrophication is an extended problem for water reservoirs and lakes that impacts directly in water quality. It can create a dead zone that lacks enough oxygen to support life and it can also be human harmful, so it must be controlled in water masses for supplying, bathing or other uses. Hydrodynamic and Water Quality modelling can contribute to forecast the status of the water system in order to alert authorities before an algae bloom event occurs. It can be used to predict scenarios and find solutions to reduce the harmful impact of the blooms. High resolution models need to process a big amount of data using a robust enough computing infrastructure. INDIGO-DataCloud (https://www.indigo-datacloud.eu/) is an European Commission funded project that aims at developing a data and computing platform targeting scientific communities, deployable on multiple hardware and provisioned over hybrid (private or public) e-infrastructures. The project addresses the development of solutions for different Case Studies using different Cloud-based alternatives. In the first INDIGO software release, a set of components are ready to manage the deployment of services to perform N number of Delft3D simulations (for calibrating or scenario definition) over a Cloud Computing environment, using the Docker technology: TOSCA requirement description, Docker repository, Orchestrator, AAI (Authorization, Authentication) and OneData (Distributed Storage System). Moreover, the Future Gateway portal based on Liferay, provides an user-friendly interface where the user can configure the simulations. Due to the data approach of INDIGO, the developed solutions can contribute to manage the full data life cycle of a project, thanks to different tools to manage datasets or even metadata. Furthermore, the cloud environment contributes to provide a dynamic, scalable and easy-to-use framework for non-IT experts users. This framework is potentially capable to automatize the processing of forecasting applying periodic tasks. For instance, a user can forecast every month the hydrodynamics and water quality status of a reservoir starting from a base model and supplying new data gathered from the instrumentation or observations. This interactive presentation aims to show the use of INDIGO solutions in a particular forecasting use case and to inspire others in the use of a Cloud framework for their applications.

Genes2WordCloud: a quick way to identify biological themes from gene lists and free text.

PubMed

Baroukh, Caroline; Jenkins, Sherry L; Dannenfelser, Ruth; Ma'ayan, Avi

2011-10-13

Word-clouds recently emerged on the web as a solution for quickly summarizing text by maximizing the display of most relevant terms about a specific topic in the minimum amount of space. As biologists are faced with the daunting amount of new research data commonly presented in textual formats, word-clouds can be used to summarize and represent biological and/or biomedical content for various applications. Genes2WordCloud is a web application that enables users to quickly identify biological themes from gene lists and research relevant text by constructing and displaying word-clouds. It provides users with several different options and ideas for the sources that can be used to generate a word-cloud. Different options for rendering and coloring the word-clouds give users the flexibility to quickly generate customized word-clouds of their choice. Genes2WordCloud is a word-cloud generator and a word-cloud viewer that is based on WordCram implemented using Java, Processing, AJAX, mySQL, and PHP. Text is fetched from several sources and then processed to extract the most relevant terms with their computed weights based on word frequencies. Genes2WordCloud is freely available for use online; it is open source software and is available for installation on any web-site along with supporting documentation at http://www.maayanlab.net/G2W. Genes2WordCloud provides a useful way to summarize and visualize large amounts of textual biological data or to find biological themes from several different sources. The open source availability of the software enables users to implement customized word-clouds on their own web-sites and desktop applications.

Genes2WordCloud: a quick way to identify biological themes from gene lists and free text

PubMed Central

2011-01-01

Background Word-clouds recently emerged on the web as a solution for quickly summarizing text by maximizing the display of most relevant terms about a specific topic in the minimum amount of space. As biologists are faced with the daunting amount of new research data commonly presented in textual formats, word-clouds can be used to summarize and represent biological and/or biomedical content for various applications. Results Genes2WordCloud is a web application that enables users to quickly identify biological themes from gene lists and research relevant text by constructing and displaying word-clouds. It provides users with several different options and ideas for the sources that can be used to generate a word-cloud. Different options for rendering and coloring the word-clouds give users the flexibility to quickly generate customized word-clouds of their choice. Methods Genes2WordCloud is a word-cloud generator and a word-cloud viewer that is based on WordCram implemented using Java, Processing, AJAX, mySQL, and PHP. Text is fetched from several sources and then processed to extract the most relevant terms with their computed weights based on word frequencies. Genes2WordCloud is freely available for use online; it is open source software and is available for installation on any web-site along with supporting documentation at http://www.maayanlab.net/G2W. Conclusions Genes2WordCloud provides a useful way to summarize and visualize large amounts of textual biological data or to find biological themes from several different sources. The open source availability of the software enables users to implement customized word-clouds on their own web-sites and desktop applications. PMID:21995939

Dynamic VMs placement for energy efficiency by PSO in cloud computing

NASA Astrophysics Data System (ADS)

Dashti, Seyed Ebrahim; Rahmani, Amir Masoud

2016-03-01

Recently, cloud computing is growing fast and helps to realise other high technologies. In this paper, we propose a hieratical architecture to satisfy both providers' and consumers' requirements in these technologies. We design a new service in the PaaS layer for scheduling consumer tasks. In the providers' perspective, incompatibility between specification of physical machine and user requests in cloud leads to problems such as energy-performance trade-off and large power consumption so that profits are decreased. To guarantee Quality of service of users' tasks, and reduce energy efficiency, we proposed to modify Particle Swarm Optimisation to reallocate migrated virtual machines in the overloaded host. We also dynamically consolidate the under-loaded host which provides power saving. Simulation results in CloudSim demonstrated that whatever simulation condition is near to the real environment, our method is able to save as much as 14% more energy and the number of migrations and simulation time significantly reduces compared with the previous works.

SnowCloud - a Framework to Predict Streamflow in Snowmelt-dominated Watersheds Using Cloud-based Computing

NASA Astrophysics Data System (ADS)

Sproles, E. A.; Crumley, R. L.; Nolin, A. W.; Mar, E.; Lopez-Moreno, J. J.

2017-12-01

Streamflow in snowy mountain regions is extraordinarily challenging to forecast, and prediction efforts are hampered by the lack of timely snow data—particularly in data sparse regions. SnowCloud is a prototype web-based framework that integrates remote sensing, cloud computing, interactive mapping tools, and a hydrologic model to offer a new paradigm for delivering key data to water resource managers. We tested the skill of SnowCloud to forecast monthly streamflow with one month lead time in three snow-dominated headwaters. These watersheds represent a range of precipitation/runoff schemes: the Río Elqui in northern Chile (200 mm/yr, entirely snowmelt); the John Day River, Oregon, USA (635 mm/yr, primarily snowmelt); and the Río Aragon in the northern Spain (850 mm/yr, snowmelt dominated). Model skill corresponded to snowpack contribution with Nash-Sutcliffe Efficiencies of 0.86, 0.52, and 0.21 respectively. SnowCloud does not require the user to possess advanced programming skills or proprietary software. We access NASA's MOD10A1 snow cover product to calculate the snow metrics globally using Google Earth Engine's geospatial analysis and cloud computing service. The analytics and forecast tools are provided through a web-based portal that requires only internet access and minimal training. To test the efficacy of SnowCloud we provided the tools and a series of tutorials in English and Spanish to water resource managers in Chile, Spain, and the United States. Participants assessed their user experience and provided feedback, and the results of our multi-cultural assessment are also presented. While our results focus on SnowCloud, they outline methods to develop cloud-based tools that function effectively across cultures and languages. Our approach also addresses the primary challenges of science-based computing; human resource limitations, infrastructure costs, and expensive proprietary software. These challenges are particularly problematic in developing countries.

Trust-Enhanced Cloud Service Selection Model Based on QoS Analysis.

PubMed

Pan, Yuchen; Ding, Shuai; Fan, Wenjuan; Li, Jing; Yang, Shanlin

2015-01-01

Cloud computing technology plays a very important role in many areas, such as in the construction and development of the smart city. Meanwhile, numerous cloud services appear on the cloud-based platform. Therefore how to how to select trustworthy cloud services remains a significant problem in such platforms, and extensively investigated owing to the ever-growing needs of users. However, trust relationship in social network has not been taken into account in existing methods of cloud service selection and recommendation. In this paper, we propose a cloud service selection model based on the trust-enhanced similarity. Firstly, the direct, indirect, and hybrid trust degrees are measured based on the interaction frequencies among users. Secondly, we estimate the overall similarity by combining the experience usability measured based on Jaccard's Coefficient and the numerical distance computed by Pearson Correlation Coefficient. Then through using the trust degree to modify the basic similarity, we obtain a trust-enhanced similarity. Finally, we utilize the trust-enhanced similarity to find similar trusted neighbors and predict the missing QoS values as the basis of cloud service selection and recommendation. The experimental results show that our approach is able to obtain optimal results via adjusting parameters and exhibits high effectiveness. The cloud services ranking by our model also have better QoS properties than other methods in the comparison experiments.

Trust-Enhanced Cloud Service Selection Model Based on QoS Analysis

PubMed Central

Pan, Yuchen; Ding, Shuai; Fan, Wenjuan; Li, Jing; Yang, Shanlin

2015-01-01

Cloud computing technology plays a very important role in many areas, such as in the construction and development of the smart city. Meanwhile, numerous cloud services appear on the cloud-based platform. Therefore how to how to select trustworthy cloud services remains a significant problem in such platforms, and extensively investigated owing to the ever-growing needs of users. However, trust relationship in social network has not been taken into account in existing methods of cloud service selection and recommendation. In this paper, we propose a cloud service selection model based on the trust-enhanced similarity. Firstly, the direct, indirect, and hybrid trust degrees are measured based on the interaction frequencies among users. Secondly, we estimate the overall similarity by combining the experience usability measured based on Jaccard’s Coefficient and the numerical distance computed by Pearson Correlation Coefficient. Then through using the trust degree to modify the basic similarity, we obtain a trust-enhanced similarity. Finally, we utilize the trust-enhanced similarity to find similar trusted neighbors and predict the missing QoS values as the basis of cloud service selection and recommendation. The experimental results show that our approach is able to obtain optimal results via adjusting parameters and exhibits high effectiveness. The cloud services ranking by our model also have better QoS properties than other methods in the comparison experiments. PMID:26606388

Performing quantum computing experiments in the cloud

NASA Astrophysics Data System (ADS)

Devitt, Simon J.

2016-09-01

Quantum computing technology has reached a second renaissance in the past five years. Increased interest from both the private and public sector combined with extraordinary theoretical and experimental progress has solidified this technology as a major advancement in the 21st century. As anticipated my many, some of the first realizations of quantum computing technology has occured over the cloud, with users logging onto dedicated hardware over the classical internet. Recently, IBM has released the Quantum Experience, which allows users to access a five-qubit quantum processor. In this paper we take advantage of this online availability of actual quantum hardware and present four quantum information experiments. We utilize the IBM chip to realize protocols in quantum error correction, quantum arithmetic, quantum graph theory, and fault-tolerant quantum computation by accessing the device remotely through the cloud. While the results are subject to significant noise, the correct results are returned from the chip. This demonstrates the power of experimental groups opening up their technology to a wider audience and will hopefully allow for the next stage of development in quantum information technology.

Uncover the Cloud for Geospatial Sciences and Applications to Adopt Cloud Computing

NASA Astrophysics Data System (ADS)

Yang, C.; Huang, Q.; Xia, J.; Liu, K.; Li, J.; Xu, C.; Sun, M.; Bambacus, M.; Xu, Y.; Fay, D.

2012-12-01

Cloud computing is emerging as the future infrastructure for providing computing resources to support and enable scientific research, engineering development, and application construction, as well as work force education. On the other hand, there is a lot of doubt about the readiness of cloud computing to support a variety of scientific research, development and educations. This research is a project funded by NASA SMD to investigate through holistic studies how ready is the cloud computing to support geosciences. Four applications with different computing characteristics including data, computing, concurrent, and spatiotemporal intensities are taken to test the readiness of cloud computing to support geosciences. Three popular and representative cloud platforms including Amazon EC2, Microsoft Azure, and NASA Nebula as well as a traditional cluster are utilized in the study. Results illustrates that cloud is ready to some degree but more research needs to be done to fully implemented the cloud benefit as advertised by many vendors and defined by NIST. Specifically, 1) most cloud platform could help stand up new computing instances, a new computer, in a few minutes as envisioned, therefore, is ready to support most computing needs in an on demand fashion; 2) the load balance and elasticity, a defining characteristic, is ready in some cloud platforms, such as Amazon EC2, to support bigger jobs, e.g., needs response in minutes, while some are not ready to support the elasticity and load balance well. All cloud platform needs further research and development to support real time application at subminute level; 3) the user interface and functionality of cloud platforms vary a lot and some of them are very professional and well supported/documented, such as Amazon EC2, some of them needs significant improvement for the general public to adopt cloud computing without professional training or knowledge about computing infrastructure; 4) the security is a big concern in cloud computing platform, with the sharing spirit of cloud computing, it is very hard to ensure higher level security, except a private cloud is built for a specific organization without public access, public cloud platform does not support FISMA medium level yet and may never be able to support FISMA high level; 5) HPC jobs needs of cloud computing is not well supported and only Amazon EC2 supports this well. The research is being taken by NASA and other agencies to consider cloud computing adoption. We hope the publication of the research would also benefit the public to adopt cloud computing.

A Secure and Verifiable Outsourced Access Control Scheme in Fog-Cloud Computing

PubMed Central

Fan, Kai; Wang, Junxiong; Wang, Xin; Li, Hui; Yang, Yintang

2017-01-01

With the rapid development of big data and Internet of things (IOT), the number of networking devices and data volume are increasing dramatically. Fog computing, which extends cloud computing to the edge of the network can effectively solve the bottleneck problems of data transmission and data storage. However, security and privacy challenges are also arising in the fog-cloud computing environment. Ciphertext-policy attribute-based encryption (CP-ABE) can be adopted to realize data access control in fog-cloud computing systems. In this paper, we propose a verifiable outsourced multi-authority access control scheme, named VO-MAACS. In our construction, most encryption and decryption computations are outsourced to fog devices and the computation results can be verified by using our verification method. Meanwhile, to address the revocation issue, we design an efficient user and attribute revocation method for it. Finally, analysis and simulation results show that our scheme is both secure and highly efficient. PMID:28737733

Cloud hosting of the IPython Notebook to Provide Collaborative Research Environments for Big Data Analysis

NASA Astrophysics Data System (ADS)

Kershaw, Philip; Lawrence, Bryan; Gomez-Dans, Jose; Holt, John

2015-04-01

We explore how the popular IPython Notebook computing system can be hosted on a cloud platform to provide a flexible virtual research hosting environment for Earth Observation data processing and analysis and how this approach can be expanded more broadly into a generic SaaS (Software as a Service) offering for the environmental sciences. OPTIRAD (OPTImisation environment for joint retrieval of multi-sensor RADiances) is a project funded by the European Space Agency to develop a collaborative research environment for Data Assimilation of Earth Observation products for land surface applications. Data Assimilation provides a powerful means to combine multiple sources of data and derive new products for this application domain. To be most effective, it requires close collaboration between specialists in this field, land surface modellers and end users of data generated. A goal of OPTIRAD then is to develop a collaborative research environment to engender shared working. Another significant challenge is that of data volume and complexity. Study of land surface requires high spatial and temporal resolutions, a relatively large number of variables and the application of algorithms which are computationally expensive. These problems can be addressed with the application of parallel processing techniques on specialist compute clusters. However, scientific users are often deterred by the time investment required to port their codes to these environments. Even when successfully achieved, it may be difficult to readily change or update. This runs counter to the scientific process of continuous experimentation, analysis and validation. The IPython Notebook provides users with a web-based interface to multiple interactive shells for the Python programming language. Code, documentation and graphical content can be saved and shared making it directly applicable to OPTIRAD's requirements for a shared working environment. Given the web interface it can be readily made into a hosted service with Wakari and Microsoft Azure being notable examples. Cloud-hosting of the Notebook allows the same familiar Python interface to be retained but backed by Cloud Computing attributes of scalability, elasticity and resource pooling. This combination makes it a powerful solution to address the needs of long-tail science users of Big Data: an intuitive interactive interface with which to access powerful compute resources. IPython Notebook can be hosted as a single user desktop environment but the recent development by the IPython community of JupyterHub enables it to be run as a multi-user hosting environment. In addition, IPython.parallel allows the exposition of parallel compute infrastructure through a Python interface. Applying these technologies in combination, a collaborative research environment has been developed for OPTIRAD on the UK JASMIN/CEMS facility's private cloud (http://jasmin.ac.uk). Based on this experience, a generic virtualised solution is under development suitable for use by the wider environmental science community - on both JASMIN and portable to third party cloud platforms.

Optimization of knowledge sharing through multi-forum using cloud computing architecture

NASA Astrophysics Data System (ADS)

Madapusi Vasudevan, Sriram; Sankaran, Srivatsan; Muthuswamy, Shanmugasundaram; Ram, N. Sankar

2011-12-01

Knowledge sharing is done through various knowledge sharing forums which requires multiple logins through multiple browser instances. Here a single Multi-Forum knowledge sharing concept is introduced which requires only one login session which makes user to connect multiple forums and display the data in a single browser window. Also few optimization techniques are introduced here to speed up the access time using cloud computing architecture.

A Secure and Efficient Audit Mechanism for Dynamic Shared Data in Cloud Storage

PubMed Central

2014-01-01

With popularization of cloud services, multiple users easily share and update their data through cloud storage. For data integrity and consistency in the cloud storage, the audit mechanisms were proposed. However, existing approaches have some security vulnerabilities and require a lot of computational overheads. This paper proposes a secure and efficient audit mechanism for dynamic shared data in cloud storage. The proposed scheme prevents a malicious cloud service provider from deceiving an auditor. Moreover, it devises a new index table management method and reduces the auditing cost by employing less complex operations. We prove the resistance against some attacks and show less computation cost and shorter time for auditing when compared with conventional approaches. The results present that the proposed scheme is secure and efficient for cloud storage services managing dynamic shared data. PMID:24959630

A secure and efficient audit mechanism for dynamic shared data in cloud storage.

PubMed

Kwon, Ohmin; Koo, Dongyoung; Shin, Yongjoo; Yoon, Hyunsoo

2014-01-01

With popularization of cloud services, multiple users easily share and update their data through cloud storage. For data integrity and consistency in the cloud storage, the audit mechanisms were proposed. However, existing approaches have some security vulnerabilities and require a lot of computational overheads. This paper proposes a secure and efficient audit mechanism for dynamic shared data in cloud storage. The proposed scheme prevents a malicious cloud service provider from deceiving an auditor. Moreover, it devises a new index table management method and reduces the auditing cost by employing less complex operations. We prove the resistance against some attacks and show less computation cost and shorter time for auditing when compared with conventional approaches. The results present that the proposed scheme is secure and efficient for cloud storage services managing dynamic shared data.

Public Auditing with Privacy Protection in a Multi-User Model of Cloud-Assisted Body Sensor Networks.

PubMed

Li, Song; Cui, Jie; Zhong, Hong; Liu, Lu

2017-05-05

Wireless Body Sensor Networks (WBSNs) are gaining importance in the era of the Internet of Things (IoT). The modern medical system is a particular area where the WBSN techniques are being increasingly adopted for various fundamental operations. Despite such increasing deployments of WBSNs, issues such as the infancy in the size, capabilities and limited data processing capacities of the sensor devices restrain their adoption in resource-demanding applications. Though providing computing and storage supplements from cloud servers can potentially enrich the capabilities of the WBSNs devices, data security is one of the prevailing issues that affects the reliability of cloud-assisted services. Sensitive applications such as modern medical systems demand assurance of the privacy of the users' medical records stored in distant cloud servers. Since it is economically impossible to set up private cloud servers for every client, auditing data security managed in the remote servers has necessarily become an integral requirement of WBSNs' applications relying on public cloud servers. To this end, this paper proposes a novel certificateless public auditing scheme with integrated privacy protection. The multi-user model in our scheme supports groups of users to store and share data, thus exhibiting the potential for WBSNs' deployments within community environments. Furthermore, our scheme enriches user experiences by offering public verifiability, forward security mechanisms and revocation of illegal group members. Experimental evaluations demonstrate the security effectiveness of our proposed scheme under the Random Oracle Model (ROM) by outperforming existing cloud-assisted WBSN models.

Bio and health informatics meets cloud : BioVLab as an example.

PubMed

Chae, Heejoon; Jung, Inuk; Lee, Hyungro; Marru, Suresh; Lee, Seong-Whan; Kim, Sun

2013-01-01

The exponential increase of genomic data brought by the advent of the next or the third generation sequencing (NGS) technologies and the dramatic drop in sequencing cost have driven biological and medical sciences to data-driven sciences. This revolutionary paradigm shift comes with challenges in terms of data transfer, storage, computation, and analysis of big bio/medical data. Cloud computing is a service model sharing a pool of configurable resources, which is a suitable workbench to address these challenges. From the medical or biological perspective, providing computing power and storage is the most attractive feature of cloud computing in handling the ever increasing biological data. As data increases in size, many research organizations start to experience the lack of computing power, which becomes a major hurdle in achieving research goals. In this paper, we review the features of publically available bio and health cloud systems in terms of graphical user interface, external data integration, security and extensibility of features. We then discuss about issues and limitations of current cloud systems and conclude with suggestion of a biological cloud environment concept, which can be defined as a total workbench environment assembling computational tools and databases for analyzing bio/medical big data in particular application domains.

Survey on Security Issues in Cloud Computing and Associated Mitigation Techniques

NASA Astrophysics Data System (ADS)

Bhadauria, Rohit; Sanyal, Sugata

2012-06-01

Cloud Computing holds the potential to eliminate the requirements for setting up of high-cost computing infrastructure for IT-based solutions and services that the industry uses. It promises to provide a flexible IT architecture, accessible through internet for lightweight portable devices. This would allow multi-fold increase in the capacity or capabilities of the existing and new software. In a cloud computing environment, the entire data reside over a set of networked resources, enabling the data to be accessed through virtual machines. Since these data-centers may lie in any corner of the world beyond the reach and control of users, there are multifarious security and privacy challenges that need to be understood and taken care of. Also, one can never deny the possibility of a server breakdown that has been witnessed, rather quite often in the recent times. There are various issues that need to be dealt with respect to security and privacy in a cloud computing scenario. This extensive survey paper aims to elaborate and analyze the numerous unresolved issues threatening the cloud computing adoption and diffusion affecting the various stake-holders linked to it.

Arc4nix: A cross-platform geospatial analytical library for cluster and cloud computing

NASA Astrophysics Data System (ADS)

Tang, Jingyin; Matyas, Corene J.

2018-02-01

Big Data in geospatial technology is a grand challenge for processing capacity. The ability to use a GIS for geospatial analysis on Cloud Computing and High Performance Computing (HPC) clusters has emerged as a new approach to provide feasible solutions. However, users lack the ability to migrate existing research tools to a Cloud Computing or HPC-based environment because of the incompatibility of the market-dominating ArcGIS software stack and Linux operating system. This manuscript details a cross-platform geospatial library "arc4nix" to bridge this gap. Arc4nix provides an application programming interface compatible with ArcGIS and its Python library "arcpy". Arc4nix uses a decoupled client-server architecture that permits geospatial analytical functions to run on the remote server and other functions to run on the native Python environment. It uses functional programming and meta-programming language to dynamically construct Python codes containing actual geospatial calculations, send them to a server and retrieve results. Arc4nix allows users to employ their arcpy-based script in a Cloud Computing and HPC environment with minimal or no modification. It also supports parallelizing tasks using multiple CPU cores and nodes for large-scale analyses. A case study of geospatial processing of a numerical weather model's output shows that arcpy scales linearly in a distributed environment. Arc4nix is open-source software.

OpenID Connect as a security service in cloud-based medical imaging systems.

PubMed

Ma, Weina; Sartipi, Kamran; Sharghigoorabi, Hassan; Koff, David; Bak, Peter

2016-04-01

The evolution of cloud computing is driving the next generation of medical imaging systems. However, privacy and security concerns have been consistently regarded as the major obstacles for adoption of cloud computing by healthcare domains. OpenID Connect, combining OpenID and OAuth together, is an emerging representational state transfer-based federated identity solution. It is one of the most adopted open standards to potentially become the de facto standard for securing cloud computing and mobile applications, which is also regarded as "Kerberos of cloud." We introduce OpenID Connect as an authentication and authorization service in cloud-based diagnostic imaging (DI) systems, and propose enhancements that allow for incorporating this technology within distributed enterprise environments. The objective of this study is to offer solutions for secure sharing of medical images among diagnostic imaging repository (DI-r) and heterogeneous picture archiving and communication systems (PACS) as well as Web-based and mobile clients in the cloud ecosystem. The main objective is to use OpenID Connect open-source single sign-on and authorization service and in a user-centric manner, while deploying DI-r and PACS to private or community clouds should provide equivalent security levels to traditional computing model.

Cloud access to interoperable IVOA-compliant VOSpace storage

NASA Astrophysics Data System (ADS)

Bertocco, S.; Dowler, P.; Gaudet, S.; Major, B.; Pasian, F.; Taffoni, G.

2018-07-01

Handling, processing and archiving the huge amount of data produced by the new generation of experiments and instruments in Astronomy and Astrophysics are among the more exciting challenges to address in designing the future data management infrastructures and computing services. We investigated the feasibility of a data management and computation infrastructure, available world-wide, with the aim of merging the FAIR data management provided by IVOA standards with the efficiency and reliability of a cloud approach. Our work involved the Canadian Advanced Network for Astronomy Research (CANFAR) infrastructure and the European EGI federated cloud (EFC). We designed and deployed a pilot data management and computation infrastructure that provides IVOA-compliant VOSpace storage resources and wide access to interoperable federated clouds. In this paper, we detail the main user requirements covered, the technical choices and the implemented solutions and we describe the resulting Hybrid cloud Worldwide infrastructure, its benefits and limitations.

A cloud-based X73 ubiquitous mobile healthcare system: design and implementation.

PubMed

Ji, Zhanlin; Ganchev, Ivan; O'Droma, Máirtín; Zhang, Xin; Zhang, Xueji

2014-01-01

Based on the user-centric paradigm for next generation networks, this paper describes a ubiquitous mobile healthcare (uHealth) system based on the ISO/IEEE 11073 personal health data (PHD) standards (X73) and cloud computing techniques. A number of design issues associated with the system implementation are outlined. The system includes a middleware on the user side, providing a plug-and-play environment for heterogeneous wireless sensors and mobile terminals utilizing different communication protocols and a distributed "big data" processing subsystem in the cloud. The design and implementation of this system are envisaged as an efficient solution for the next generation of uHealth systems.

On Using Home Networks and Cloud Computing for a Future Internet of Things

NASA Astrophysics Data System (ADS)

Niedermayer, Heiko; Holz, Ralph; Pahl, Marc-Oliver; Carle, Georg

In this position paper we state four requirements for a Future Internet and sketch our initial concept. The requirements: (1) more comfort, (2) integration of home networks, (3) resources like service clouds in the network, and (4) access anywhere on any machine. Future Internet needs future quality and future comfort. There need to be new possiblities for everyone. Our focus is on higher layers and related to the many overlay proposals. We consider them to run on top of a basic Future Internet core. A new user experience means to include all user devices. Home networks and services should be a fundamental part of the Future Internet. Home networks extend access and allow interaction with the environment. Cloud Computing can provide reliable resources beyond local boundaries. For access anywhere, we also need secure storage for data and profiles in the network, in particular for access with non-personal devices (Internet terminal, ticket machine, ...).

One-Click Data Analysis Software for Science Operations

NASA Astrophysics Data System (ADS)

Navarro, Vicente

2015-12-01

One of the important activities of ESA Science Operations Centre is to provide Data Analysis Software (DAS) to enable users and scientists to process data further to higher levels. During operations and post-operations, Data Analysis Software (DAS) is fully maintained and updated for new OS and library releases. Nonetheless, once a Mission goes into the "legacy" phase, there are very limited funds and long-term preservation becomes more and more difficult. Building on Virtual Machine (VM), Cloud computing and Software as a Service (SaaS) technologies, this project has aimed at providing long-term preservation of Data Analysis Software for the following missions: - PIA for ISO (1995) - SAS for XMM-Newton (1999) - Hipe for Herschel (2009) - EXIA for EXOSAT (1983) Following goals have guided the architecture: - Support for all operations, post-operations and archive/legacy phases. - Support for local (user's computer) and cloud environments (ESAC-Cloud, Amazon - AWS). - Support for expert users, requiring full capabilities. - Provision of a simple web-based interface. This talk describes the architecture, challenges, results and lessons learnt gathered in this project.

Cloud Computing Techniques for Space Mission Design

NASA Technical Reports Server (NTRS)

Arrieta, Juan; Senent, Juan

2014-01-01

The overarching objective of space mission design is to tackle complex problems producing better results, and faster. In developing the methods and tools to fulfill this objective, the user interacts with the different layers of a computing system.

Interactive analysis of geographically distributed population imaging data collections over light-path data networks

NASA Astrophysics Data System (ADS)

van Lew, Baldur; Botha, Charl P.; Milles, Julien R.; Vrooman, Henri A.; van de Giessen, Martijn; Lelieveldt, Boudewijn P. F.

2015-03-01

The cohort size required in epidemiological imaging genetics studies often mandates the pooling of data from multiple hospitals. Patient data, however, is subject to strict privacy protection regimes, and physical data storage may be legally restricted to a hospital network. To enable biomarker discovery, fast data access and interactive data exploration must be combined with high-performance computing resources, while respecting privacy regulations. We present a system using fast and inherently secure light-paths to access distributed data, thereby obviating the need for a central data repository. A secure private cloud computing framework facilitates interactive, computationally intensive exploration of this geographically distributed, privacy sensitive data. As a proof of concept, MRI brain imaging data hosted at two remote sites were processed in response to a user command at a third site. The system was able to automatically start virtual machines, run a selected processing pipeline and write results to a user accessible database, while keeping data locally stored in the hospitals. Individual tasks took approximately 50% longer compared to a locally hosted blade server but the cloud infrastructure reduced the total elapsed time by a factor of 40 using 70 virtual machines in the cloud. We demonstrated that the combination light-path and private cloud is a viable means of building an analysis infrastructure for secure data analysis. The system requires further work in the areas of error handling, load balancing and secure support of multiple users.

CAPER 3.0: A Scalable Cloud-Based System for Data-Intensive Analysis of Chromosome-Centric Human Proteome Project Data Sets.

PubMed

Yang, Shuai; Zhang, Xinlei; Diao, Lihong; Guo, Feifei; Wang, Dan; Liu, Zhongyang; Li, Honglei; Zheng, Junjie; Pan, Jingshan; Nice, Edouard C; Li, Dong; He, Fuchu

2015-09-04

The Chromosome-centric Human Proteome Project (C-HPP) aims to catalog genome-encoded proteins using a chromosome-by-chromosome strategy. As the C-HPP proceeds, the increasing requirement for data-intensive analysis of the MS/MS data poses a challenge to the proteomic community, especially small laboratories lacking computational infrastructure. To address this challenge, we have updated the previous CAPER browser into a higher version, CAPER 3.0, which is a scalable cloud-based system for data-intensive analysis of C-HPP data sets. CAPER 3.0 uses cloud computing technology to facilitate MS/MS-based peptide identification. In particular, it can use both public and private cloud, facilitating the analysis of C-HPP data sets. CAPER 3.0 provides a graphical user interface (GUI) to help users transfer data, configure jobs, track progress, and visualize the results comprehensively. These features enable users without programming expertise to easily conduct data-intensive analysis using CAPER 3.0. Here, we illustrate the usage of CAPER 3.0 with four specific mass spectral data-intensive problems: detecting novel peptides, identifying single amino acid variants (SAVs) derived from known missense mutations, identifying sample-specific SAVs, and identifying exon-skipping events. CAPER 3.0 is available at http://prodigy.bprc.ac.cn/caper3.

Service Migration from Cloud to Multi-tier Fog Nodes for Multimedia Dissemination with QoE Support

PubMed Central

Camargo, João; Rochol, Juergen; Gerla, Mario

2018-01-01

A wide range of multimedia services is expected to be offered for mobile users via various wireless access networks. Even the integration of Cloud Computing in such networks does not support an adequate Quality of Experience (QoE) in areas with high demands for multimedia contents. Fog computing has been conceptualized to facilitate the deployment of new services that cloud computing cannot provide, particularly those demanding QoE guarantees. These services are provided using fog nodes located at the network edge, which is capable of virtualizing their functions/applications. Service migration from the cloud to fog nodes can be actuated by request patterns and the timing issues. To the best of our knowledge, existing works on fog computing focus on architecture and fog node deployment issues. In this article, we describe the operational impacts and benefits associated with service migration from the cloud to multi-tier fog computing for video distribution with QoE support. Besides that, we perform the evaluation of such service migration of video services. Finally, we present potential research challenges and trends. PMID:29364172

Interoperating Cloud-based Virtual Farms

NASA Astrophysics Data System (ADS)

Bagnasco, S.; Colamaria, F.; Colella, D.; Casula, E.; Elia, D.; Franco, A.; Lusso, S.; Luparello, G.; Masera, M.; Miniello, G.; Mura, D.; Piano, S.; Vallero, S.; Venaruzzo, M.; Vino, G.

2015-12-01

The present work aims at optimizing the use of computing resources available at the grid Italian Tier-2 sites of the ALICE experiment at CERN LHC by making them accessible to interactive distributed analysis, thanks to modern solutions based on cloud computing. The scalability and elasticity of the computing resources via dynamic (“on-demand”) provisioning is essentially limited by the size of the computing site, reaching the theoretical optimum only in the asymptotic case of infinite resources. The main challenge of the project is to overcome this limitation by federating different sites through a distributed cloud facility. Storage capacities of the participating sites are seen as a single federated storage area, preventing the need of mirroring data across them: high data access efficiency is guaranteed by location-aware analysis software and storage interfaces, in a transparent way from an end-user perspective. Moreover, the interactive analysis on the federated cloud reduces the execution time with respect to grid batch jobs. The tests of the investigated solutions for both cloud computing and distributed storage on wide area network will be presented.

Service Migration from Cloud to Multi-tier Fog Nodes for Multimedia Dissemination with QoE Support.

PubMed

Rosário, Denis; Schimuneck, Matias; Camargo, João; Nobre, Jéferson; Both, Cristiano; Rochol, Juergen; Gerla, Mario

2018-01-24

A wide range of multimedia services is expected to be offered for mobile users via various wireless access networks. Even the integration of Cloud Computing in such networks does not support an adequate Quality of Experience (QoE) in areas with high demands for multimedia contents. Fog computing has been conceptualized to facilitate the deployment of new services that cloud computing cannot provide, particularly those demanding QoE guarantees. These services are provided using fog nodes located at the network edge, which is capable of virtualizing their functions/applications. Service migration from the cloud to fog nodes can be actuated by request patterns and the timing issues. To the best of our knowledge, existing works on fog computing focus on architecture and fog node deployment issues. In this article, we describe the operational impacts and benefits associated with service migration from the cloud to multi-tier fog computing for video distribution with QoE support. Besides that, we perform the evaluation of such service migration of video services. Finally, we present potential research challenges and trends.

User Inspired Management of Scientific Jobs in Grids and Clouds

ERIC Educational Resources Information Center

Withana, Eran Chinthaka

2011-01-01

From time-critical, real time computational experimentation to applications which process petabytes of data there is a continuing search for faster, more responsive computing platforms capable of supporting computational experimentation. Weather forecast models, for instance, process gigabytes of data to produce regional (mesoscale) predictions on…

Cloud flexibility using DIRAC interware

NASA Astrophysics Data System (ADS)

Fernandez Albor, Víctor; Seco Miguelez, Marcos; Fernandez Pena, Tomas; Mendez Muñoz, Victor; Saborido Silva, Juan Jose; Graciani Diaz, Ricardo

2014-06-01

Communities of different locations are running their computing jobs on dedicated infrastructures without the need to worry about software, hardware or even the site where their programs are going to be executed. Nevertheless, this usually implies that they are restricted to use certain types or versions of an Operating System because either their software needs an definite version of a system library or a specific platform is required by the collaboration to which they belong. On this scenario, if a data center wants to service software to incompatible communities, it has to split its physical resources among those communities. This splitting will inevitably lead to an underuse of resources because the data centers are bound to have periods where one or more of its subclusters are idle. It is, in this situation, where Cloud Computing provides the flexibility and reduction in computational cost that data centers are searching for. This paper describes a set of realistic tests that we ran on one of such implementations. The test comprise software from three different HEP communities (Auger, LHCb and QCD phenomelogists) and the Parsec Benchmark Suite running on one or more of three Linux flavors (SL5, Ubuntu 10.04 and Fedora 13). The implemented infrastructure has, at the cloud level, CloudStack that manages the virtual machines (VM) and the hosts on which they run, and, at the user level, the DIRAC framework along with a VM extension that will submit, monitorize and keep track of the user jobs and also requests CloudStack to start or stop the necessary VM's. In this infrastructure, the community software is distributed via the CernVM-FS, which has been proven to be a reliable and scalable software distribution system. With the resulting infrastructure, users are allowed to send their jobs transparently to the Data Center. The main purpose of this system is the creation of flexible cluster, multiplatform with an scalable method for software distribution for several VOs. Users from different communities do not need to care about the installation of the standard software that is available at the nodes, nor the operating system of the host machine, which is transparent to the user.

GES DISC Data Recipes in Jupyter Notebooks

NASA Astrophysics Data System (ADS)

Li, A.; Banavige, B.; Garimella, K.; Rice, J.; Shen, S.; Liu, Z.

2017-12-01

The Earth Science Data and Information System (ESDIS) Project manages twelve Distributed Active Archive Centers (DAACs) which are geographically dispersed across the United States. The DAACs are responsible for ingesting, processing, archiving, and distributing Earth science data produced from various sources (satellites, aircraft, field measurements, etc.). In response to projections of an exponential increase in data production, there has been a recent effort to prototype various DAAC activities in the cloud computing environment. This, in turn, led to the creation of an initiative, called the Cloud Analysis Toolkit to Enable Earth Science (CATEES), to develop a Python software package in order to transition Earth science data processing to the cloud. This project, in particular, supports CATEES and has two primary goals. One, transition data recipes created by the Goddard Earth Science Data and Information Service Center (GES DISC) DAAC into an interactive and educational environment using Jupyter Notebooks. Two, acclimate Earth scientists to cloud computing. To accomplish these goals, we create Jupyter Notebooks to compartmentalize the different steps of data analysis and help users obtain and parse data from the command line. We also develop a Docker container, comprised of Jupyter Notebooks, Python library dependencies, and command line tools, and configure it into an easy to deploy package. The end result is an end-to-end product that simulates the use case of end users working in the cloud computing environment.

Accelerating statistical image reconstruction algorithms for fan-beam x-ray CT using cloud computing

NASA Astrophysics Data System (ADS)

Srivastava, Somesh; Rao, A. Ravishankar; Sheinin, Vadim

2011-03-01

Statistical image reconstruction algorithms potentially offer many advantages to x-ray computed tomography (CT), e.g. lower radiation dose. But, their adoption in practical CT scanners requires extra computation power, which is traditionally provided by incorporating additional computing hardware (e.g. CPU-clusters, GPUs, FPGAs etc.) into a scanner. An alternative solution is to access the required computation power over the internet from a cloud computing service, which is orders-of-magnitude more cost-effective. This is because users only pay a small pay-as-you-go fee for the computation resources used (i.e. CPU time, storage etc.), and completely avoid purchase, maintenance and upgrade costs. In this paper, we investigate the benefits and shortcomings of using cloud computing for statistical image reconstruction. We parallelized the most time-consuming parts of our application, the forward and back projectors, using MapReduce, the standard parallelization library on clouds. From preliminary investigations, we found that a large speedup is possible at a very low cost. But, communication overheads inside MapReduce can limit the maximum speedup, and a better MapReduce implementation might become necessary in the future. All the experiments for this paper, including development and testing, were completed on the Amazon Elastic Compute Cloud (EC2) for less than $20.

Web-based Tsunami Early Warning System with instant Tsunami Propagation Calculations in the GPU Cloud

NASA Astrophysics Data System (ADS)

Hammitzsch, M.; Spazier, J.; Reißland, S.

2014-12-01

Usually, tsunami early warning and mitigation systems (TWS or TEWS) are based on several software components deployed in a client-server based infrastructure. The vast majority of systems importantly include desktop-based clients with a graphical user interface (GUI) for the operators in early warning centers. However, in times of cloud computing and ubiquitous computing the use of concepts and paradigms, introduced by continuously evolving approaches in information and communications technology (ICT), have to be considered even for early warning systems (EWS). Based on the experiences and the knowledge gained in three research projects - 'German Indonesian Tsunami Early Warning System' (GITEWS), 'Distant Early Warning System' (DEWS), and 'Collaborative, Complex, and Critical Decision-Support in Evolving Crises' (TRIDEC) - new technologies are exploited to implement a cloud-based and web-based prototype to open up new prospects for EWS. This prototype, named 'TRIDEC Cloud', merges several complementary external and in-house cloud-based services into one platform for automated background computation with graphics processing units (GPU), for web-mapping of hazard specific geospatial data, and for serving relevant functionality to handle, share, and communicate threat specific information in a collaborative and distributed environment. The prototype in its current version addresses tsunami early warning and mitigation. The integration of GPU accelerated tsunami simulation computations have been an integral part of this prototype to foster early warning with on-demand tsunami predictions based on actual source parameters. However, the platform is meant for researchers around the world to make use of the cloud-based GPU computation to analyze other types of geohazards and natural hazards and react upon the computed situation picture with a web-based GUI in a web browser at remote sites. The current website is an early alpha version for demonstration purposes to give the concept a whirl and to shape science's future. Further functionality, improvements and possible profound changes have to implemented successively based on the users' evolving needs.

USMC Installations Command Information Environment: Opportunities and Analysis for Integration of First Responder Communications

DTIC Science & Technology

2014-09-01

becoming a more and more prevalent technology in the business world today. According to Syal and Goswami (2012), cloud technology is seen as a...use of computing resources, applications, and personal files without reliance on a single computer or system ( Syal & Goswami, 2012). By operating in...cloud services largely being web-based, which can be retrieved through most systems with access to the Internet ( Syal & Goswami, 2012). The end user can

A resource-sharing model based on a repeated game in fog computing.

PubMed

Sun, Yan; Zhang, Nan

2017-03-01

With the rapid development of cloud computing techniques, the number of users is undergoing exponential growth. It is difficult for traditional data centers to perform many tasks in real time because of the limited bandwidth of resources. The concept of fog computing is proposed to support traditional cloud computing and to provide cloud services. In fog computing, the resource pool is composed of sporadic distributed resources that are more flexible and movable than a traditional data center. In this paper, we propose a fog computing structure and present a crowd-funding algorithm to integrate spare resources in the network. Furthermore, to encourage more resource owners to share their resources with the resource pool and to supervise the resource supporters as they actively perform their tasks, we propose an incentive mechanism in our algorithm. Simulation results show that our proposed incentive mechanism can effectively reduce the SLA violation rate and accelerate the completion of tasks.

Grids and clouds in the Czech NGI

NASA Astrophysics Data System (ADS)

Kundrát, Jan; Adam, Martin; Adamová, Dagmar; Chudoba, Jiří; Kouba, Tomáš; Lokajíček, Miloš; Mikula, Alexandr; Říkal, Václav; Švec, Jan; Vohnout, Rudolf

2016-09-01

There are several infrastructure operators within the Czech Republic NGI (National Grid Initiative) which provide users with access to high-performance computing facilities over a grid and cloud interface. This article focuses on those where the primary author has personal first-hand experience. We cover some operational issues as well as the history of these facilities.

Secure and Resilient Cloud Computing for the Department of Defense

DTIC Science & Technology

2015-11-16

platform as a service (PaaS), and software as a service ( SaaS )—that target system administrators, developers, and end-users respectively (see Table 2...interfaces (API) and services Medium Amazon Elastic MapReduce, MathWorks Cloud, Red Hat OpenShift SaaS Full-fledged applications Low Google gMail

A Science Cloud: OneSpaceNet

NASA Astrophysics Data System (ADS)

Morikawa, Y.; Murata, K. T.; Watari, S.; Kato, H.; Yamamoto, K.; Inoue, S.; Tsubouchi, K.; Fukazawa, K.; Kimura, E.; Tatebe, O.; Shimojo, S.

2010-12-01

Main methodologies of Solar-Terrestrial Physics (STP) so far are theoretical, experimental and observational, and computer simulation approaches. Recently "informatics" is expected as a new (fourth) approach to the STP studies. Informatics is a methodology to analyze large-scale data (observation data and computer simulation data) to obtain new findings using a variety of data processing techniques. At NICT (National Institute of Information and Communications Technology, Japan) we are now developing a new research environment named "OneSpaceNet". The OneSpaceNet is a cloud-computing environment specialized for science works, which connects many researchers with high-speed network (JGN: Japan Gigabit Network). The JGN is a wide-area back-born network operated by NICT; it provides 10G network and many access points (AP) over Japan. The OneSpaceNet also provides with rich computer resources for research studies, such as super-computers, large-scale data storage area, licensed applications, visualization devices (like tiled display wall: TDW), database/DBMS, cluster computers (4-8 nodes) for data processing and communication devices. What is amazing in use of the science cloud is that a user simply prepares a terminal (low-cost PC). Once connecting the PC to JGN2plus, the user can make full use of the rich resources of the science cloud. Using communication devices, such as video-conference system, streaming and reflector servers, and media-players, the users on the OneSpaceNet can make research communications as if they belong to a same (one) laboratory: they are members of a virtual laboratory. The specification of the computer resources on the OneSpaceNet is as follows: The size of data storage we have developed so far is almost 1PB. The number of the data files managed on the cloud storage is getting larger and now more than 40,000,000. What is notable is that the disks forming the large-scale storage are distributed to 5 data centers over Japan (but the storage system performs as one disk). There are three supercomputers allocated on the cloud, one from Tokyo, one from Osaka and the other from Nagoya. One's simulation job data on any supercomputers are saved on the cloud data storage (same directory); it is a kind of virtual computing environment. The tiled display wall has 36 panels acting as one display; the pixel (resolution) size of it is as large as 18000x4300. This size is enough to preview or analyze the large-scale computer simulation data. It also allows us to take a look of multiple (e.g., 100 pictures) on one screen together with many researchers. In our talk we also present a brief report of the initial results using the OneSpaceNet for Global MHD simulations as an example of successful use of our science cloud; (i) Ultra-high time resolution visualization of Global MHD simulations on the large-scale storage and parallel processing system on the cloud, (ii) Database of real-time Global MHD simulation and statistic analyses of the data, and (iii) 3D Web service of Global MHD simulations.

OpenID Connect as a security service in cloud-based medical imaging systems

PubMed Central

Ma, Weina; Sartipi, Kamran; Sharghigoorabi, Hassan; Koff, David; Bak, Peter

2016-01-01

Abstract. The evolution of cloud computing is driving the next generation of medical imaging systems. However, privacy and security concerns have been consistently regarded as the major obstacles for adoption of cloud computing by healthcare domains. OpenID Connect, combining OpenID and OAuth together, is an emerging representational state transfer-based federated identity solution. It is one of the most adopted open standards to potentially become the de facto standard for securing cloud computing and mobile applications, which is also regarded as “Kerberos of cloud.” We introduce OpenID Connect as an authentication and authorization service in cloud-based diagnostic imaging (DI) systems, and propose enhancements that allow for incorporating this technology within distributed enterprise environments. The objective of this study is to offer solutions for secure sharing of medical images among diagnostic imaging repository (DI-r) and heterogeneous picture archiving and communication systems (PACS) as well as Web-based and mobile clients in the cloud ecosystem. The main objective is to use OpenID Connect open-source single sign-on and authorization service and in a user-centric manner, while deploying DI-r and PACS to private or community clouds should provide equivalent security levels to traditional computing model. PMID:27340682

Research on cloud-based remote measurement and analysis system

NASA Astrophysics Data System (ADS)

Gao, Zhiqiang; He, Lingsong; Su, Wei; Wang, Can; Zhang, Changfan

2015-02-01

The promising potential of cloud computing and its convergence with technologies such as cloud storage, cloud push, mobile computing allows for creation and delivery of newer type of cloud service. Combined with the thought of cloud computing, this paper presents a cloud-based remote measurement and analysis system. This system mainly consists of three parts: signal acquisition client, web server deployed on the cloud service, and remote client. This system is a special website developed using asp.net and Flex RIA technology, which solves the selective contradiction between two monitoring modes, B/S and C/S. This platform supplies customer condition monitoring and data analysis service by Internet, which was deployed on the cloud server. Signal acquisition device is responsible for data (sensor data, audio, video, etc.) collection and pushes the monitoring data to the cloud storage database regularly. Data acquisition equipment in this system is only conditioned with the function of data collection and network function such as smartphone and smart sensor. This system's scale can adjust dynamically according to the amount of applications and users, so it won't cause waste of resources. As a representative case study, we developed a prototype system based on Ali cloud service using the rotor test rig as the research object. Experimental results demonstrate that the proposed system architecture is feasible.

Helix Nebula: Enabling federation of existing data infrastructures and data services to an overarching cross-domain e-infrastructure

NASA Astrophysics Data System (ADS)

Lengert, Wolfgang; Farres, Jordi; Lanari, Riccardo; Casu, Francesco; Manunta, Michele; Lassalle-Balier, Gerard

2014-05-01

Helix Nebula has established a growing public private partnership of more than 30 commercial cloud providers, SMEs, and publicly funded research organisations and e-infrastructures. The Helix Nebula strategy is to establish a federated cloud service across Europe. Three high-profile flagships, sponsored by CERN (high energy physics), EMBL (life sciences) and ESA/DLR/CNES/CNR (earth science), have been deployed and extensively tested within this federated environment. The commitments behind these initial flagships have created a critical mass that attracts suppliers and users to the initiative, to work together towards an "Information as a Service" market place. Significant progress in implementing the following 4 programmatic goals (as outlined in the strategic Plan Ref.1) has been achieved: × Goal #1 Establish a Cloud Computing Infrastructure for the European Research Area (ERA) serving as a platform for innovation and evolution of the overall infrastructure. × Goal #2 Identify and adopt suitable policies for trust, security and privacy on a European-level can be provided by the European Cloud Computing framework and infrastructure. × Goal #3 Create a light-weight governance structure for the future European Cloud Computing Infrastructure that involves all the stakeholders and can evolve over time as the infrastructure, services and user-base grows. × Goal #4 Define a funding scheme involving the three stake-holder groups (service suppliers, users, EC and national funding agencies) into a Public-Private-Partnership model to implement a Cloud Computing Infrastructure that delivers a sustainable business environment adhering to European level policies. Now in 2014 a first version of this generic cross-domain e-infrastructure is ready to go into operations building on federation of European industry and contributors (data, tools, knowledge, ...). This presentation describes how Helix Nebula is being used in the domain of earth science focusing on geohazards. The so called "Supersite Exploitation Platform" (SSEP) provides scientists an overarching federated e-infrastructure with a very fast access to (i) large volume of data (EO/non-space data), (ii) computing resources (e.g. hybrid cloud/grid), (iii) processing software (e.g. toolboxes, RTMs, retrieval baselines, visualization routines), and (iv) general platform capabilities (e.g. user management and access control, accounting, information portal, collaborative tools, social networks etc.). In this federation each data provider remains in full control of the implementation of its data policy. This presentation outlines the Architecture (technical and services) supporting very heterogeneous science domains as well as the procedures for new-comers to join the Helix Nebula Market Place. Ref.1 http://cds.cern.ch/record/1374172/files/CERN-OPEN-2011-036.pdf

A Cloud-Based X73 Ubiquitous Mobile Healthcare System: Design and Implementation

PubMed Central

Ji, Zhanlin; O'Droma, Máirtín; Zhang, Xin; Zhang, Xueji

2014-01-01

Based on the user-centric paradigm for next generation networks, this paper describes a ubiquitous mobile healthcare (uHealth) system based on the ISO/IEEE 11073 personal health data (PHD) standards (X73) and cloud computing techniques. A number of design issues associated with the system implementation are outlined. The system includes a middleware on the user side, providing a plug-and-play environment for heterogeneous wireless sensors and mobile terminals utilizing different communication protocols and a distributed “big data” processing subsystem in the cloud. The design and implementation of this system are envisaged as an efficient solution for the next generation of uHealth systems. PMID:24737958

A Cloud Computing Based Patient Centric Medical Information System

NASA Astrophysics Data System (ADS)

Agarwal, Ankur; Henehan, Nathan; Somashekarappa, Vivek; Pandya, A. S.; Kalva, Hari; Furht, Borko

This chapter discusses an emerging concept of a cloud computing based Patient Centric Medical Information System framework that will allow various authorized users to securely access patient records from various Care Delivery Organizations (CDOs) such as hospitals, urgent care centers, doctors, laboratories, imaging centers among others, from any location. Such a system must seamlessly integrate all patient records including images such as CT-SCANS and MRI'S which can easily be accessed from any location and reviewed by any authorized user. In such a scenario the storage and transmission of medical records will have be conducted in a totally secure and safe environment with a very high standard of data integrity, protecting patient privacy and complying with all Health Insurance Portability and Accountability Act (HIPAA) regulations.

Public Auditing with Privacy Protection in a Multi-User Model of Cloud-Assisted Body Sensor Networks

PubMed Central

Li, Song; Cui, Jie; Zhong, Hong; Liu, Lu

2017-01-01

Wireless Body Sensor Networks (WBSNs) are gaining importance in the era of the Internet of Things (IoT). The modern medical system is a particular area where the WBSN techniques are being increasingly adopted for various fundamental operations. Despite such increasing deployments of WBSNs, issues such as the infancy in the size, capabilities and limited data processing capacities of the sensor devices restrain their adoption in resource-demanding applications. Though providing computing and storage supplements from cloud servers can potentially enrich the capabilities of the WBSNs devices, data security is one of the prevailing issues that affects the reliability of cloud-assisted services. Sensitive applications such as modern medical systems demand assurance of the privacy of the users’ medical records stored in distant cloud servers. Since it is economically impossible to set up private cloud servers for every client, auditing data security managed in the remote servers has necessarily become an integral requirement of WBSNs’ applications relying on public cloud servers. To this end, this paper proposes a novel certificateless public auditing scheme with integrated privacy protection. The multi-user model in our scheme supports groups of users to store and share data, thus exhibiting the potential for WBSNs’ deployments within community environments. Furthermore, our scheme enriches user experiences by offering public verifiability, forward security mechanisms and revocation of illegal group members. Experimental evaluations demonstrate the security effectiveness of our proposed scheme under the Random Oracle Model (ROM) by outperforming existing cloud-assisted WBSN models. PMID:28475110

Identifying Key Features, Cutting Edge Cloud Resources, and Artificial Intelligence Tools to Achieve User-Friendly Water Science in the Cloud

NASA Astrophysics Data System (ADS)

Pierce, S. A.

2017-12-01

Decision making for groundwater systems is becoming increasingly important, as shifting water demands increasingly impact aquifers. As buffer systems, aquifers provide room for resilient responses and augment the actual timeframe for hydrological response. Yet the pace impacts, climate shifts, and degradation of water resources is accelerating. To meet these new drivers, groundwater science is transitioning toward the emerging field of Integrated Water Resources Management, or IWRM. IWRM incorporates a broad array of dimensions, methods, and tools to address problems that tend to be complex. Computational tools and accessible cyberinfrastructure (CI) are needed to cross the chasm between science and society. Fortunately cloud computing environments, such as the new Jetstream system, are evolving rapidly. While still targeting scientific user groups systems such as, Jetstream, offer configurable cyberinfrastructure to enable interactive computing and data analysis resources on demand. The web-based interfaces allow researchers to rapidly customize virtual machines, modify computing architecture and increase the usability and access for broader audiences to advanced compute environments. The result enables dexterous configurations and opening up opportunities for IWRM modelers to expand the reach of analyses, number of case studies, and quality of engagement with stakeholders and decision makers. The acute need to identify improved IWRM solutions paired with advanced computational resources refocuses the attention of IWRM researchers on applications, workflows, and intelligent systems that are capable of accelerating progress. IWRM must address key drivers of community concern, implement transdisciplinary methodologies, adapt and apply decision support tools in order to effectively support decisions about groundwater resource management. This presentation will provide an overview of advanced computing services in the cloud using integrated groundwater management case studies to highlight how Cloud CI streamlines the process for setting up an interactive decision support system. Moreover, advances in artificial intelligence offer new techniques for old problems from integrating data to adaptive sensing or from interactive dashboards to optimizing multi-attribute problems. The combination of scientific expertise, flexible cloud computing solutions, and intelligent systems opens new research horizons.

BlueSky Cloud Framework: An E-Learning Framework Embracing Cloud Computing

NASA Astrophysics Data System (ADS)

Dong, Bo; Zheng, Qinghua; Qiao, Mu; Shu, Jian; Yang, Jie

Currently, E-Learning has grown into a widely accepted way of learning. With the huge growth of users, services, education contents and resources, E-Learning systems are facing challenges of optimizing resource allocations, dealing with dynamic concurrency demands, handling rapid storage growth requirements and cost controlling. In this paper, an E-Learning framework based on cloud computing is presented, namely BlueSky cloud framework. Particularly, the architecture and core components of BlueSky cloud framework are introduced. In BlueSky cloud framework, physical machines are virtualized, and allocated on demand for E-Learning systems. Moreover, BlueSky cloud framework combines with traditional middleware functions (such as load balancing and data caching) to serve for E-Learning systems as a general architecture. It delivers reliable, scalable and cost-efficient services to E-Learning systems, and E-Learning organizations can establish systems through these services in a simple way. BlueSky cloud framework solves the challenges faced by E-Learning, and improves the performance, availability and scalability of E-Learning systems.

Archive Management of NASA Earth Observation Data to Support Cloud Analysis

NASA Technical Reports Server (NTRS)

Lynnes, Christopher; Baynes, Kathleen; McInerney, Mark A.

2017-01-01

NASA collects, processes and distributes petabytes of Earth Observation (EO) data from satellites, aircraft, in situ instruments and model output, with an order of magnitude increase expected by 2024. Cloud-based web object storage (WOS) of these data can simplify the execution of such an increase. More importantly, it can also facilitate user analysis of those volumes by making the data available to the massively parallel computing power in the cloud. However, storing EO data in cloud WOS has a ripple effect throughout the NASA archive system with unexpected challenges and opportunities. One challenge is modifying data servicing software (such as Web Coverage Service servers) to access and subset data that are no longer on a directly accessible file system, but rather in cloud WOS. Opportunities include refactoring of the archive software to a cloud-native architecture; virtualizing data products by computing on demand; and reorganizing data to be more analysis-friendly.

Using cloud models of heartbeats as the entity identifier to secure mobile devices.

PubMed

Fu, Donglai; Liu, Yanhua

2017-01-01

Mobile devices are extensively used to store more private and often sensitive information. Therefore, it is important to protect them against unauthorised access. Authentication ensures that authorised users can use mobile devices. However, traditional authentication methods, such as numerical or graphic passwords, are vulnerable to passive attacks. For example, an adversary can steal the password by snooping from a shorter distance. To avoid these problems, this study presents a biometric approach that uses cloud models of heartbeats as the entity identifier to secure mobile devices. Here, it is identified that these concepts including cloud model or cloud have nothing to do with cloud computing. The cloud model appearing in the study is the cognitive model. In the proposed method, heartbeats are collected by two ECG electrodes that are connected to one mobile device. The backward normal cloud generator is used to generate ECG standard cloud models characterising the heartbeat template. When a user tries to have access to their mobile device, cloud models regenerated by fresh heartbeats will be compared with ECG standard cloud models to determine if the current user can use this mobile device. This authentication method was evaluated from three aspects including accuracy, authentication time and energy consumption. The proposed method gives 86.04% of true acceptance rate with 2.73% of false acceptance rate. One authentication can be done in 6s, and this processing consumes about 2000 mW of power.

Scheduling multimedia services in cloud computing environment

NASA Astrophysics Data System (ADS)

Liu, Yunchang; Li, Chunlin; Luo, Youlong; Shao, Yanling; Zhang, Jing

2018-02-01

Currently, security is a critical factor for multimedia services running in the cloud computing environment. As an effective mechanism, trust can improve security level and mitigate attacks within cloud computing environments. Unfortunately, existing scheduling strategy for multimedia service in the cloud computing environment do not integrate trust mechanism when making scheduling decisions. In this paper, we propose a scheduling scheme for multimedia services in multi clouds. At first, a novel scheduling architecture is presented. Then, We build a trust model including both subjective trust and objective trust to evaluate the trust degree of multimedia service providers. By employing Bayesian theory, the subjective trust degree between multimedia service providers and users is obtained. According to the attributes of QoS, the objective trust degree of multimedia service providers is calculated. Finally, a scheduling algorithm integrating trust of entities is proposed by considering the deadline, cost and trust requirements of multimedia services. The scheduling algorithm heuristically hunts for reasonable resource allocations and satisfies the requirement of trust and meets deadlines for the multimedia services. Detailed simulated experiments demonstrate the effectiveness and feasibility of the proposed trust scheduling scheme.

Phenomenology tools on cloud infrastructures using OpenStack

NASA Astrophysics Data System (ADS)

Campos, I.; Fernández-del-Castillo, E.; Heinemeyer, S.; Lopez-Garcia, A.; Pahlen, F.; Borges, G.

2013-04-01

We present a new environment for computations in particle physics phenomenology employing recent developments in cloud computing. On this environment users can create and manage "virtual" machines on which the phenomenology codes/tools can be deployed easily in an automated way. We analyze the performance of this environment based on "virtual" machines versus the utilization of physical hardware. In this way we provide a qualitative result for the influence of the host operating system on the performance of a representative set of applications for phenomenology calculations.

Facilitating NASA Earth Science Data Processing Using Nebula Cloud Computing

NASA Astrophysics Data System (ADS)

Chen, A.; Pham, L.; Kempler, S.; Theobald, M.; Esfandiari, A.; Campino, J.; Vollmer, B.; Lynnes, C.

2011-12-01

Cloud Computing technology has been used to offer high-performance and low-cost computing and storage resources for both scientific problems and business services. Several cloud computing services have been implemented in the commercial arena, e.g. Amazon's EC2 & S3, Microsoft's Azure, and Google App Engine. There are also some research and application programs being launched in academia and governments to utilize Cloud Computing. NASA launched the Nebula Cloud Computing platform in 2008, which is an Infrastructure as a Service (IaaS) to deliver on-demand distributed virtual computers. Nebula users can receive required computing resources as a fully outsourced service. NASA Goddard Earth Science Data and Information Service Center (GES DISC) migrated several GES DISC's applications to the Nebula as a proof of concept, including: a) The Simple, Scalable, Script-based Science Processor for Measurements (S4PM) for processing scientific data; b) the Atmospheric Infrared Sounder (AIRS) data process workflow for processing AIRS raw data; and c) the GES-DISC Interactive Online Visualization ANd aNalysis Infrastructure (GIOVANNI) for online access to, analysis, and visualization of Earth science data. This work aims to evaluate the practicability and adaptability of the Nebula. The initial work focused on the AIRS data process workflow to evaluate the Nebula. The AIRS data process workflow consists of a series of algorithms being used to process raw AIRS level 0 data and output AIRS level 2 geophysical retrievals. Migrating the entire workflow to the Nebula platform is challenging, but practicable. After installing several supporting libraries and the processing code itself, the workflow is able to process AIRS data in a similar fashion to its current (non-cloud) configuration. We compared the performance of processing 2 days of AIRS level 0 data through level 2 using a Nebula virtual computer and a local Linux computer. The result shows that Nebula has significantly better performance than the local machine. Much of the difference was due to newer equipment in the Nebula than the legacy computer, which is suggestive of a potential economic advantage beyond elastic power, i.e., access to up-to-date hardware vs. legacy hardware that must be maintained past its prime to amortize the cost. In addition to a trade study of advantages and challenges of porting complex processing to the cloud, a tutorial was developed to enable further progress in utilizing the Nebula for Earth Science applications and understanding better the potential for Cloud Computing in further data- and computing-intensive Earth Science research. In particular, highly bursty computing such as that experienced in the user-demand-driven Giovanni system may become more tractable in a Cloud environment. Our future work will continue to focus on migrating more GES DISC's applications/instances, e.g. Giovanni instances, to the Nebula platform and making matured migrated applications to be in operation on the Nebula.

The AIST Managed Cloud Environment

NASA Astrophysics Data System (ADS)

Cook, S.

2016-12-01

ESTO is currently in the process of developing and implementing the AIST Managed Cloud Environment (AMCE) to offer cloud computing services to ESTO-funded PIs to conduct their project research. AIST will provide projects access to a cloud computing framework that incorporates NASA security, technical, and financial standards, on which project can freely store, run, and process data. Currently, many projects led by research groups outside of NASA do not have the awareness of requirements or the resources to implement NASA standards into their research, which limits the likelihood of infusing the work into NASA applications. Offering this environment to PIs will allow them to conduct their project research using the many benefits of cloud computing. In addition to the well-known cost and time savings that it allows, it also provides scalability and flexibility. The AMCE will facilitate infusion and end user access by ensuring standardization and security. This approach will ultimately benefit ESTO, the science community, and the research, allowing the technology developments to have quicker and broader applications.

The AMCE (AIST Managed Cloud Environment)

NASA Astrophysics Data System (ADS)

Cook, S.

2017-12-01

ESTO has developed and implemented the AIST Managed Cloud Environment (AMCE) to offer cloud computing services to SMD-funded PIs to conduct their project research. AIST will provide projects access to a cloud computing framework that incorporates NASA security, technical, and financial standards, on which project can freely store, run, and process data. Currently, many projects led by research groups outside of NASA do not have the awareness of requirements or the resources to implement NASA standards into their research, which limits the likelihood of infusing the work into NASA applications. Offering this environment to PIs allows them to conduct their project research using the many benefits of cloud computing. In addition to the well-known cost and time savings that it allows, it also provides scalability and flexibility. The AMCE facilitates infusion and end user access by ensuring standardization and security. This approach will ultimately benefit ESTO, the science community, and the research, allowing the technology developments to have quicker and broader applications.

A hybrid optical switch architecture to integrate IP into optical networks to provide flexible and intelligent bandwidth on demand for cloud computing

NASA Astrophysics Data System (ADS)

Yang, Wei; Hall, Trevor J.

2013-12-01

The Internet is entering an era of cloud computing to provide more cost effective, eco-friendly and reliable services to consumer and business users. As a consequence, the nature of the Internet traffic has been fundamentally transformed from a pure packet-based pattern to today's predominantly flow-based pattern. Cloud computing has also brought about an unprecedented growth in the Internet traffic. In this paper, a hybrid optical switch architecture is presented to deal with the flow-based Internet traffic, aiming to offer flexible and intelligent bandwidth on demand to improve fiber capacity utilization. The hybrid optical switch is capable of integrating IP into optical networks for cloud-based traffic with predictable performance, for which the delay performance of the electronic module in the hybrid optical switch architecture is evaluated through simulation.

Secure Genomic Computation through Site-Wise Encryption

PubMed Central

Zhao, Yongan; Wang, XiaoFeng; Tang, Haixu

2015-01-01

Commercial clouds provide on-demand IT services for big-data analysis, which have become an attractive option for users who have no access to comparable infrastructure. However, utilizing these services for human genome analysis is highly risky, as human genomic data contains identifiable information of human individuals and their disease susceptibility. Therefore, currently, no computation on personal human genomic data is conducted on public clouds. To address this issue, here we present a site-wise encryption approach to encrypt whole human genome sequences, which can be subject to secure searching of genomic signatures on public clouds. We implemented this method within the Hadoop framework, and tested it on the case of searching disease markers retrieved from the ClinVar database against patients’ genomic sequences. The secure search runs only one order of magnitude slower than the simple search without encryption, indicating our method is ready to be used for secure genomic computation on public clouds. PMID:26306278

Secure Genomic Computation through Site-Wise Encryption.

PubMed

Zhao, Yongan; Wang, XiaoFeng; Tang, Haixu

2015-01-01

Commercial clouds provide on-demand IT services for big-data analysis, which have become an attractive option for users who have no access to comparable infrastructure. However, utilizing these services for human genome analysis is highly risky, as human genomic data contains identifiable information of human individuals and their disease susceptibility. Therefore, currently, no computation on personal human genomic data is conducted on public clouds. To address this issue, here we present a site-wise encryption approach to encrypt whole human genome sequences, which can be subject to secure searching of genomic signatures on public clouds. We implemented this method within the Hadoop framework, and tested it on the case of searching disease markers retrieved from the ClinVar database against patients' genomic sequences. The secure search runs only one order of magnitude slower than the simple search without encryption, indicating our method is ready to be used for secure genomic computation on public clouds.

A Cloud-Computing Service for Environmental Geophysics and Seismic Data Processing

NASA Astrophysics Data System (ADS)

Heilmann, B. Z.; Maggi, P.; Piras, A.; Satta, G.; Deidda, G. P.; Bonomi, E.

2012-04-01

Cloud computing is establishing worldwide as a new high performance computing paradigm that offers formidable possibilities to industry and science. The presented cloud-computing portal, part of the Grida3 project, provides an innovative approach to seismic data processing by combining open-source state-of-the-art processing software and cloud-computing technology, making possible the effective use of distributed computation and data management with administratively distant resources. We substituted the user-side demanding hardware and software requirements by remote access to high-performance grid-computing facilities. As a result, data processing can be done quasi in real-time being ubiquitously controlled via Internet by a user-friendly web-browser interface. Besides the obvious advantages over locally installed seismic-processing packages, the presented cloud-computing solution creates completely new possibilities for scientific education, collaboration, and presentation of reproducible results. The web-browser interface of our portal is based on the commercially supported grid portal EnginFrame, an open framework based on Java, XML, and Web Services. We selected the hosted applications with the objective to allow the construction of typical 2D time-domain seismic-imaging workflows as used for environmental studies and, originally, for hydrocarbon exploration. For data visualization and pre-processing, we chose the free software package Seismic Un*x. We ported tools for trace balancing, amplitude gaining, muting, frequency filtering, dip filtering, deconvolution and rendering, with a customized choice of options as services onto the cloud-computing portal. For structural imaging and velocity-model building, we developed a grid version of the Common-Reflection-Surface stack, a data-driven imaging method that requires no user interaction at run time such as manual picking in prestack volumes or velocity spectra. Due to its high level of automation, CRS stacking can benefit largely from the hardware parallelism provided by the cloud deployment. The resulting output, post-stack section, coherence, and NMO-velocity panels are used to generate a smooth migration-velocity model. Residual static corrections are calculated as a by-product of the stack and can be applied iteratively. As a final step, a time migrated subsurface image is obtained by a parallelized Kirchhoff time migration scheme. Processing can be done step-by-step or using a graphical workflow editor that can launch a series of pipelined tasks. The status of the submitted jobs is monitored by a dedicated service. All results are stored in project directories, where they can be downloaded of viewed directly in the browser. Currently, the portal has access to three research clusters having a total number of 70 nodes with 4 cores each. They are shared with four other cloud-computing applications bundled within the GRIDA3 project. To demonstrate the functionality of our "seismic cloud lab", we will present results obtained for three different types of data, all taken from hydrogeophysical studies: (1) a seismic reflection data set, made of compressional waves from explosive sources, recorded in Muravera, Sardinia; (2) a shear-wave data set from, Sardinia; (3) a multi-offset Ground-Penetrating-Radar data set from Larreule, France. The presented work was funded by the government of the Autonomous Region of Sardinia and by the Italian Ministry of Research and Education.

QoS-aware health monitoring system using cloud-based WBANs.

PubMed

Almashaqbeh, Ghada; Hayajneh, Thaier; Vasilakos, Athanasios V; Mohd, Bassam J

2014-10-01

Wireless Body Area Networks (WBANs) are amongst the best options for remote health monitoring. However, as standalone systems WBANs have many limitations due to the large amount of processed data, mobility of monitored users, and the network coverage area. Integrating WBANs with cloud computing provides effective solutions to these problems and promotes the performance of WBANs based systems. Accordingly, in this paper we propose a cloud-based real-time remote health monitoring system for tracking the health status of non-hospitalized patients while practicing their daily activities. Compared with existing cloud-based WBAN frameworks, we divide the cloud into local one, that includes the monitored users and local medical staff, and a global one that includes the outer world. The performance of the proposed framework is optimized by reducing congestion, interference, and data delivery delay while supporting users' mobility. Several novel techniques and algorithms are proposed to accomplish our objective. First, the concept of data classification and aggregation is utilized to avoid clogging the network with unnecessary data traffic. Second, a dynamic channel assignment policy is developed to distribute the WBANs associated with the users on the available frequency channels to manage interference. Third, a delay-aware routing metric is proposed to be used by the local cloud in its multi-hop communication to speed up the reporting process of the health-related data. Fourth, the delay-aware metric is further utilized by the association protocols used by the WBANs to connect with the local cloud. Finally, the system with all the proposed techniques and algorithms is evaluated using extensive ns-2 simulations. The simulation results show superior performance of the proposed architecture in optimizing the end-to-end delay, handling the increased interference levels, maximizing the network capacity, and tracking user's mobility.

Realistic natural atmospheric phenomena and weather effects for interactive virtual environments

NASA Astrophysics Data System (ADS)

McLoughlin, Leigh

Clouds and the weather are important aspects of any natural outdoor scene, but existing dynamic techniques within computer graphics only offer the simplest of cloud representations. The problem that this work looks to address is how to provide a means of simulating clouds and weather features such as precipitation, that are suitable for virtual environments. Techniques for cloud simulation are available within the area of meteorology, but numerical weather prediction systems are computationally expensive, give more numerical accuracy than we require for graphics and are restricted to the laws of physics. Within computer graphics, we often need to direct and adjust physical features or to bend reality to meet artistic goals, which is a key difference between the subjects of computer graphics and physical science. Pure physically-based simulations, however, evolve their solutions according to pre-set rules and are notoriously difficult to control. The challenge then is for the solution to be computationally lightweight and able to be directed in some measure while at the same time producing believable results. This work presents a lightweight physically-based cloud simulation scheme that simulates the dynamic properties of cloud formation and weather effects. The system simulates water vapour, cloud water, cloud ice, rain, snow and hail. The water model incorporates control parameters and the cloud model uses an arbitrary vertical temperature profile, with a tool described to allow the user to define this. The result of this work is that clouds can now be simulated in near real-time complete with precipitation. The temperature profile and tool then provide a means of directing the resulting formation..

Architectural Principles and Experimentation of Distributed High Performance Virtual Clusters

ERIC Educational Resources Information Center

Younge, Andrew J.

2016-01-01

With the advent of virtualization and Infrastructure-as-a-Service (IaaS), the broader scientific computing community is considering the use of clouds for their scientific computing needs. This is due to the relative scalability, ease of use, advanced user environment customization abilities, and the many novel computing paradigms available for…

Cloud CPFP: a shotgun proteomics data analysis pipeline using cloud and high performance computing.

PubMed

Trudgian, David C; Mirzaei, Hamid

2012-12-07

We have extended the functionality of the Central Proteomics Facilities Pipeline (CPFP) to allow use of remote cloud and high performance computing (HPC) resources for shotgun proteomics data processing. CPFP has been modified to include modular local and remote scheduling for data processing jobs. The pipeline can now be run on a single PC or server, a local cluster, a remote HPC cluster, and/or the Amazon Web Services (AWS) cloud. We provide public images that allow easy deployment of CPFP in its entirety in the AWS cloud. This significantly reduces the effort necessary to use the software, and allows proteomics laboratories to pay for compute time ad hoc, rather than obtaining and maintaining expensive local server clusters. Alternatively the Amazon cloud can be used to increase the throughput of a local installation of CPFP as necessary. We demonstrate that cloud CPFP allows users to process data at higher speed than local installations but with similar cost and lower staff requirements. In addition to the computational improvements, the web interface to CPFP is simplified, and other functionalities are enhanced. The software is under active development at two leading institutions and continues to be released under an open-source license at http://cpfp.sourceforge.net.

Visual Analysis of Cloud Computing Performance Using Behavioral Lines.

PubMed

Muelder, Chris; Zhu, Biao; Chen, Wei; Zhang, Hongxin; Ma, Kwan-Liu

2016-02-29

Cloud computing is an essential technology to Big Data analytics and services. A cloud computing system is often comprised of a large number of parallel computing and storage devices. Monitoring the usage and performance of such a system is important for efficient operations, maintenance, and security. Tracing every application on a large cloud system is untenable due to scale and privacy issues. But profile data can be collected relatively efficiently by regularly sampling the state of the system, including properties such as CPU load, memory usage, network usage, and others, creating a set of multivariate time series for each system. Adequate tools for studying such large-scale, multidimensional data are lacking. In this paper, we present a visual based analysis approach to understanding and analyzing the performance and behavior of cloud computing systems. Our design is based on similarity measures and a layout method to portray the behavior of each compute node over time. When visualizing a large number of behavioral lines together, distinct patterns often appear suggesting particular types of performance bottleneck. The resulting system provides multiple linked views, which allow the user to interactively explore the data by examining the data or a selected subset at different levels of detail. Our case studies, which use datasets collected from two different cloud systems, show that this visual based approach is effective in identifying trends and anomalies of the systems.

CovalentDock Cloud: a web server for automated covalent docking.

PubMed

Ouyang, Xuchang; Zhou, Shuo; Ge, Zemei; Li, Runtao; Kwoh, Chee Keong

2013-07-01

Covalent binding is an important mechanism for many drugs to gain its function. We developed a computational algorithm to model this chemical event and extended it to a web server, the CovalentDock Cloud, to make it accessible directly online without any local installation and configuration. It provides a simple yet user-friendly web interface to perform covalent docking experiments and analysis online. The web server accepts the structures of both the ligand and the receptor uploaded by the user or retrieved from online databases with valid access id. It identifies the potential covalent binding patterns, carries out the covalent docking experiments and provides visualization of the result for user analysis. This web server is free and open to all users at http://docking.sce.ntu.edu.sg/.

CovalentDock Cloud: a web server for automated covalent docking

PubMed Central

Ouyang, Xuchang; Zhou, Shuo; Ge, Zemei; Li, Runtao; Kwoh, Chee Keong

2013-01-01

Covalent binding is an important mechanism for many drugs to gain its function. We developed a computational algorithm to model this chemical event and extended it to a web server, the CovalentDock Cloud, to make it accessible directly online without any local installation and configuration. It provides a simple yet user-friendly web interface to perform covalent docking experiments and analysis online. The web server accepts the structures of both the ligand and the receptor uploaded by the user or retrieved from online databases with valid access id. It identifies the potential covalent binding patterns, carries out the covalent docking experiments and provides visualization of the result for user analysis. This web server is free and open to all users at http://docking.sce.ntu.edu.sg/. PMID:23677616

Design and Implementation of a Cloud Computing Adoption Decision Tool: Generating a Cloud Road.

PubMed

Bildosola, Iñaki; Río-Belver, Rosa; Cilleruelo, Ernesto; Garechana, Gaizka

2015-01-01

Migrating to cloud computing is one of the current enterprise challenges. This technology provides a new paradigm based on "on-demand payment" for information and communication technologies. In this sense, the small and medium enterprise is supposed to be the most interested, since initial investments are avoided and the technology allows gradual implementation. However, even if the characteristics and capacities have been widely discussed, entry into the cloud is still lacking in terms of practical, real frameworks. This paper aims at filling this gap, presenting a real tool already implemented and tested, which can be used as a cloud computing adoption decision tool. This tool uses diagnosis based on specific questions to gather the required information and subsequently provide the user with valuable information to deploy the business within the cloud, specifically in the form of Software as a Service (SaaS) solutions. This information allows the decision makers to generate their particular Cloud Road. A pilot study has been carried out with enterprises at a local level with a two-fold objective: to ascertain the degree of knowledge on cloud computing and to identify the most interesting business areas and their related tools for this technology. As expected, the results show high interest and low knowledge on this subject and the tool presented aims to readdress this mismatch, insofar as possible.

Design and Implementation of a Cloud Computing Adoption Decision Tool: Generating a Cloud Road

PubMed Central

Bildosola, Iñaki; Río-Belver, Rosa; Cilleruelo, Ernesto; Garechana, Gaizka

2015-01-01

Migrating to cloud computing is one of the current enterprise challenges. This technology provides a new paradigm based on “on-demand payment” for information and communication technologies. In this sense, the small and medium enterprise is supposed to be the most interested, since initial investments are avoided and the technology allows gradual implementation. However, even if the characteristics and capacities have been widely discussed, entry into the cloud is still lacking in terms of practical, real frameworks. This paper aims at filling this gap, presenting a real tool already implemented and tested, which can be used as a cloud computing adoption decision tool. This tool uses diagnosis based on specific questions to gather the required information and subsequently provide the user with valuable information to deploy the business within the cloud, specifically in the form of Software as a Service (SaaS) solutions. This information allows the decision makers to generate their particular Cloud Road. A pilot study has been carried out with enterprises at a local level with a two-fold objective: to ascertain the degree of knowledge on cloud computing and to identify the most interesting business areas and their related tools for this technology. As expected, the results show high interest and low knowledge on this subject and the tool presented aims to readdress this mismatch, insofar as possible. PMID:26230400

Evaluating virtual hosted desktops for graphics-intensive astronomy

NASA Astrophysics Data System (ADS)

Meade, B. F.; Fluke, C. J.

2018-04-01

Visualisation of data is critical to understanding astronomical phenomena. Today, many instruments produce datasets that are too big to be downloaded to a local computer, yet many of the visualisation tools used by astronomers are deployed only on desktop computers. Cloud computing is increasingly used to provide a computation and simulation platform in astronomy, but it also offers great potential as a visualisation platform. Virtual hosted desktops, with graphics processing unit (GPU) acceleration, allow interactive, graphics-intensive desktop applications to operate co-located with astronomy datasets stored in remote data centres. By combining benchmarking and user experience testing, with a cohort of 20 astronomers, we investigate the viability of replacing physical desktop computers with virtual hosted desktops. In our work, we compare two Apple MacBook computers (one old and one new, representing hardware and opposite ends of the useful lifetime) with two virtual hosted desktops: one commercial (Amazon Web Services) and one in a private research cloud (the Australian NeCTAR Research Cloud). For two-dimensional image-based tasks and graphics-intensive three-dimensional operations - typical of astronomy visualisation workflows - we found that benchmarks do not necessarily provide the best indication of performance. When compared to typical laptop computers, virtual hosted desktops can provide a better user experience, even with lower performing graphics cards. We also found that virtual hosted desktops are equally simple to use, provide greater flexibility in choice of configuration, and may actually be a more cost-effective option for typical usage profiles.

Improving ATLAS grid site reliability with functional tests using HammerCloud

NASA Astrophysics Data System (ADS)

Elmsheuser, Johannes; Legger, Federica; Medrano Llamas, Ramon; Sciacca, Gianfranco; van der Ster, Dan

2012-12-01

With the exponential growth of LHC (Large Hadron Collider) data in 2011, and more coming in 2012, distributed computing has become the established way to analyse collider data. The ATLAS grid infrastructure includes almost 100 sites worldwide, ranging from large national computing centers to smaller university clusters. These facilities are used for data reconstruction and simulation, which are centrally managed by the ATLAS production system, and for distributed user analysis. To ensure the smooth operation of such a complex system, regular tests of all sites are necessary to validate the site capability of successfully executing user and production jobs. We report on the development, optimization and results of an automated functional testing suite using the HammerCloud framework. Functional tests are short lightweight applications covering typical user analysis and production schemes, which are periodically submitted to all ATLAS grid sites. Results from those tests are collected and used to evaluate site performances. Sites that fail or are unable to run the tests are automatically excluded from the PanDA brokerage system, therefore avoiding user or production jobs to be sent to problematic sites.

Efficiency and Flexibility of Fingerprint Scheme Using Partial Encryption and Discrete Wavelet Transform to Verify User in Cloud Computing.

PubMed

Yassin, Ali A

2014-01-01

Now, the security of digital images is considered more and more essential and fingerprint plays the main role in the world of image. Furthermore, fingerprint recognition is a scheme of biometric verification that applies pattern recognition techniques depending on image of fingerprint individually. In the cloud environment, an adversary has the ability to intercept information and must be secured from eavesdroppers. Unluckily, encryption and decryption functions are slow and they are often hard. Fingerprint techniques required extra hardware and software; it is masqueraded by artificial gummy fingers (spoof attacks). Additionally, when a large number of users are being verified at the same time, the mechanism will become slow. In this paper, we employed each of the partial encryptions of user's fingerprint and discrete wavelet transform to obtain a new scheme of fingerprint verification. Moreover, our proposed scheme can overcome those problems; it does not require cost, reduces the computational supplies for huge volumes of fingerprint images, and resists well-known attacks. In addition, experimental results illustrate that our proposed scheme has a good performance of user's fingerprint verification.

Efficiency and Flexibility of Fingerprint Scheme Using Partial Encryption and Discrete Wavelet Transform to Verify User in Cloud Computing

PubMed Central

Yassin, Ali A.

2014-01-01

Now, the security of digital images is considered more and more essential and fingerprint plays the main role in the world of image. Furthermore, fingerprint recognition is a scheme of biometric verification that applies pattern recognition techniques depending on image of fingerprint individually. In the cloud environment, an adversary has the ability to intercept information and must be secured from eavesdroppers. Unluckily, encryption and decryption functions are slow and they are often hard. Fingerprint techniques required extra hardware and software; it is masqueraded by artificial gummy fingers (spoof attacks). Additionally, when a large number of users are being verified at the same time, the mechanism will become slow. In this paper, we employed each of the partial encryptions of user's fingerprint and discrete wavelet transform to obtain a new scheme of fingerprint verification. Moreover, our proposed scheme can overcome those problems; it does not require cost, reduces the computational supplies for huge volumes of fingerprint images, and resists well-known attacks. In addition, experimental results illustrate that our proposed scheme has a good performance of user's fingerprint verification. PMID:27355051

MONET: multidimensional radiative cloud scene model

NASA Astrophysics Data System (ADS)

Chervet, Patrick

1999-12-01

All cloud fields exhibit variable structures (bulge) and heterogeneities in water distributions. With the development of multidimensional radiative models by the atmospheric community, it is now possible to describe horizontal heterogeneities of the cloud medium, to study these influences on radiative quantities. We have developed a complete radiative cloud scene generator, called MONET (French acronym for: MOdelisation des Nuages En Tridim.) to compute radiative cloud scene from visible to infrared wavelengths for various viewing and solar conditions, different spatial scales, and various locations on the Earth. MONET is composed of two parts: a cloud medium generator (CSSM -- Cloud Scene Simulation Model) developed by the Air Force Research Laboratory, and a multidimensional radiative code (SHDOM -- Spherical Harmonic Discrete Ordinate Method) developed at the University of Colorado by Evans. MONET computes images for several scenario defined by user inputs: date, location, viewing angles, wavelength, spatial resolution, meteorological conditions (atmospheric profiles, cloud types)... For the same cloud scene, we can output different viewing conditions, or/and various wavelengths. Shadowing effects on clouds or grounds are taken into account. This code is useful to study heterogeneity effects on satellite data for various cloud types and spatial resolutions, and to determine specifications of new imaging sensor.

Cloud computing for context-aware enhanced m-Health services.

PubMed

Fernandez-Llatas, Carlos; Pileggi, Salvatore F; Ibañez, Gema; Valero, Zoe; Sala, Pilar

2015-01-01

m-Health services are increasing its presence in our lives due to the high penetration of new smartphone devices. This new scenario proposes new challenges in terms of information accessibility that require new paradigms which enable the new applications to access the data in a continuous and ubiquitous way, ensuring the privacy required depending on the kind of data accessed. This paper proposes an architecture based on cloud computing paradigms in order to empower new m-Health applications to enrich their results by providing secure access to user data.

A Hierarchical Auction-Based Mechanism for Real-Time Resource Allocation in Cloud Robotic Systems.

PubMed

Wang, Lujia; Liu, Ming; Meng, Max Q-H

2017-02-01

Cloud computing enables users to share computing resources on-demand. The cloud computing framework cannot be directly mapped to cloud robotic systems with ad hoc networks since cloud robotic systems have additional constraints such as limited bandwidth and dynamic structure. However, most multirobotic applications with cooperative control adopt this decentralized approach to avoid a single point of failure. Robots need to continuously update intensive data to execute tasks in a coordinated manner, which implies real-time requirements. Thus, a resource allocation strategy is required, especially in such resource-constrained environments. This paper proposes a hierarchical auction-based mechanism, namely link quality matrix (LQM) auction, which is suitable for ad hoc networks by introducing a link quality indicator. The proposed algorithm produces a fast and robust method that is accurate and scalable. It reduces both global communication and unnecessary repeated computation. The proposed method is designed for firm real-time resource retrieval for physical multirobot systems. A joint surveillance scenario empirically validates the proposed mechanism by assessing several practical metrics. The results show that the proposed LQM auction outperforms state-of-the-art algorithms for resource allocation.

The diverse use of clouds by CMS

DOE PAGES

Andronis, Anastasios; Bauer, Daniela; Chaze, Olivier; ...

2015-12-23

The resources CMS is using are increasingly being offered as clouds. In Run 2 of the LHC the majority of CMS CERN resources, both in Meyrin and at the Wigner Computing Centre, will be presented as cloud resources on which CMS will have to build its own infrastructure. This infrastructure will need to run all of the CMS workflows including: Tier 0, production and user analysis. In addition, the CMS High Level Trigger will provide a compute resource comparable in scale to the total offered by the CMS Tier 1 sites, when it is not running as part of themore » trigger system. During these periods a cloud infrastructure will be overlaid on this resource, making it accessible for general CMS use. Finally, CMS is starting to utilise cloud resources being offered by individual institutes and is gaining experience to facilitate the use of opportunistically available cloud resources. Lastly, we present a snap shot of this infrastructure and its operation at the time of the CHEP2015 conference.« less

Detecting Distributed SQL Injection Attacks in a Eucalyptus Cloud Environment

NASA Technical Reports Server (NTRS)

Kebert, Alan; Barnejee, Bikramjit; Solano, Juan; Solano, Wanda

2013-01-01

The cloud computing environment offers malicious users the ability to spawn multiple instances of cloud nodes that are similar to virtual machines, except that they can have separate external IP addresses. In this paper we demonstrate how this ability can be exploited by an attacker to distribute his/her attack, in particular SQL injection attacks, in such a way that an intrusion detection system (IDS) could fail to identify this attack. To demonstrate this, we set up a small private cloud, established a vulnerable website in one instance, and placed an IDS within the cloud to monitor the network traffic. We found that an attacker could quite easily defeat the IDS by periodically altering its IP address. To detect such an attacker, we propose to use multi-agent plan recognition, where the multiple source IPs are considered as different agents who are mounting a collaborative attack. We show that such a formulation of this problem yields a more sophisticated approach to detecting SQL injection attacks within a cloud computing environment.

Archive Management of NASA Earth Observation Data to Support Cloud Analysis

NASA Technical Reports Server (NTRS)

Lynnes, Christopher; Baynes, Kathleen; McInerney, Mark

2017-01-01

NASA collects, processes and distributes petabytes of Earth Observation (EO) data from satellites, aircraft, in situ instruments and model output, with an order of magnitude increase expected by 2024. Cloud-based web object storage (WOS) of these data can simplify the execution of such an increase. More importantly, it can also facilitate user analysis of those volumes by making the data available to the massively parallel computing power in the cloud. However, storing EO data in cloud WOS has a ripple effect throughout the NASA archive system with unexpected challenges and opportunities. One challenge is modifying data servicing software (such as Web Coverage Service servers) to access and subset data that are no longer on a directly accessible file system, but rather in cloud WOS. Opportunities include refactoring of the archive software to a cloud-native architecture; virtualizing data products by computing on demand; and reorganizing data to be more analysis-friendly. Reviewed by Mark McInerney ESDIS Deputy Project Manager.

The direction of cloud computing for Malaysian education sector in 21st century

NASA Astrophysics Data System (ADS)

Jaafar, Jazurainifariza; Rahman, M. Nordin A.; Kadir, M. Fadzil A.; Shamsudin, Syadiah Nor; Saany, Syarilla Iryani A.

2017-08-01

In 21st century, technology has turned learning environment into a new way of education to make learning systems more effective and systematic. Nowadays, education institutions are faced many challenges to ensure the teaching and learning process is running smoothly and manageable. Some of challenges in the current education management are lack of integrated systems, high cost of maintenance, difficulty of configuration and deployment as well as complexity of storage provision. Digital learning is an instructional practice that use technology to make learning experience more effective, provides education process more systematic and attractive. Digital learning can be considered as one of the prominent application that implemented under cloud computing environment. Cloud computing is a type of network resources that provides on-demands services where the users can access applications inside it at any location and no time border. It also promises for minimizing the cost of maintenance and provides a flexible of data storage capacity. The aim of this article is to review the definition and types of cloud computing for improving digital learning management as required in the 21st century education. The analysis of digital learning context focused on primary school in Malaysia. Types of cloud applications and services in education sector are also discussed in the article. Finally, gap analysis and direction of cloud computing in education sector for facing the 21st century challenges are suggested.

Cloud Computing and Its Applications in GIS

NASA Astrophysics Data System (ADS)

Kang, Cao

2011-12-01

Cloud computing is a novel computing paradigm that offers highly scalable and highly available distributed computing services. The objectives of this research are to: 1. analyze and understand cloud computing and its potential for GIS; 2. discover the feasibilities of migrating truly spatial GIS algorithms to distributed computing infrastructures; 3. explore a solution to host and serve large volumes of raster GIS data efficiently and speedily. These objectives thus form the basis for three professional articles. The first article is entitled "Cloud Computing and Its Applications in GIS". This paper introduces the concept, structure, and features of cloud computing. Features of cloud computing such as scalability, parallelization, and high availability make it a very capable computing paradigm. Unlike High Performance Computing (HPC), cloud computing uses inexpensive commodity computers. The uniform administration systems in cloud computing make it easier to use than GRID computing. Potential advantages of cloud-based GIS systems such as lower barrier to entry are consequently presented. Three cloud-based GIS system architectures are proposed: public cloud- based GIS systems, private cloud-based GIS systems and hybrid cloud-based GIS systems. Public cloud-based GIS systems provide the lowest entry barriers for users among these three architectures, but their advantages are offset by data security and privacy related issues. Private cloud-based GIS systems provide the best data protection, though they have the highest entry barriers. Hybrid cloud-based GIS systems provide a compromise between these extremes. The second article is entitled "A cloud computing algorithm for the calculation of Euclidian distance for raster GIS". Euclidean distance is a truly spatial GIS algorithm. Classical algorithms such as the pushbroom and growth ring techniques require computational propagation through the entire raster image, which makes it incompatible with the distributed nature of cloud computing. This paper presents a parallel Euclidean distance algorithm that works seamlessly with the distributed nature of cloud computing infrastructures. The mechanism of this algorithm is to subdivide a raster image into sub-images and wrap them with a one pixel deep edge layer of individually computed distance information. Each sub-image is then processed by a separate node, after which the resulting sub-images are reassembled into the final output. It is shown that while any rectangular sub-image shape can be used, those approximating squares are computationally optimal. This study also serves as a demonstration of this subdivide and layer-wrap strategy, which would enable the migration of many truly spatial GIS algorithms to cloud computing infrastructures. However, this research also indicates that certain spatial GIS algorithms such as cost distance cannot be migrated by adopting this mechanism, which presents significant challenges for the development of cloud-based GIS systems. The third article is entitled "A Distributed Storage Schema for Cloud Computing based Raster GIS Systems". This paper proposes a NoSQL Database Management System (NDDBMS) based raster GIS data storage schema. NDDBMS has good scalability and is able to use distributed commodity computers, which make it superior to Relational Database Management Systems (RDBMS) in a cloud computing environment. In order to provide optimized data service performance, the proposed storage schema analyzes the nature of commonly used raster GIS data sets. It discriminates two categories of commonly used data sets, and then designs corresponding data storage models for both categories. As a result, the proposed storage schema is capable of hosting and serving enormous volumes of raster GIS data speedily and efficiently on cloud computing infrastructures. In addition, the scheme also takes advantage of the data compression characteristics of Quadtrees, thus promoting efficient data storage. Through this assessment of cloud computing technology, the exploration of the challenges and solutions to the migration of GIS algorithms to cloud computing infrastructures, and the examination of strategies for serving large amounts of GIS data in a cloud computing infrastructure, this dissertation lends support to the feasibility of building a cloud-based GIS system. However, there are still challenges that need to be addressed before a full-scale functional cloud-based GIS system can be successfully implemented. (Abstract shortened by UMI.)

Satellite Cloud and Radiative Property Processing and Distribution System on the NASA Langley ASDC OpenStack and OpenShift Cloud Platform

NASA Astrophysics Data System (ADS)

Nguyen, L.; Chee, T.; Palikonda, R.; Smith, W. L., Jr.; Bedka, K. M.; Spangenberg, D.; Vakhnin, A.; Lutz, N. E.; Walter, J.; Kusterer, J.

2017-12-01

Cloud Computing offers new opportunities for large-scale scientific data producers to utilize Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) IT resources to process and deliver data products in an operational environment where timely delivery, reliability, and availability are critical. The NASA Langley Research Center Atmospheric Science Data Center (ASDC) is building and testing a private and public facing cloud for users in the Science Directorate to utilize as an everyday production environment. The NASA SatCORPS (Satellite ClOud and Radiation Property Retrieval System) team processes and derives near real-time (NRT) global cloud products from operational geostationary (GEO) satellite imager datasets. To deliver these products, we will utilize the public facing cloud and OpenShift to deploy a load-balanced webserver for data storage, access, and dissemination. The OpenStack private cloud will host data ingest and computational capabilities for SatCORPS processing. This paper will discuss the SatCORPS migration towards, and usage of, the ASDC Cloud Services in an operational environment. Detailed lessons learned from use of prior cloud providers, specifically the Amazon Web Services (AWS) GovCloud and the Government Cloud administered by the Langley Managed Cloud Environment (LMCE) will also be discussed.

Fog computing job scheduling optimization based on bees swarm

NASA Astrophysics Data System (ADS)

Bitam, Salim; Zeadally, Sherali; Mellouk, Abdelhamid

2018-04-01

Fog computing is a new computing architecture, composed of a set of near-user edge devices called fog nodes, which collaborate together in order to perform computational services such as running applications, storing an important amount of data, and transmitting messages. Fog computing extends cloud computing by deploying digital resources at the premise of mobile users. In this new paradigm, management and operating functions, such as job scheduling aim at providing high-performance, cost-effective services requested by mobile users and executed by fog nodes. We propose a new bio-inspired optimization approach called Bees Life Algorithm (BLA) aimed at addressing the job scheduling problem in the fog computing environment. Our proposed approach is based on the optimized distribution of a set of tasks among all the fog computing nodes. The objective is to find an optimal tradeoff between CPU execution time and allocated memory required by fog computing services established by mobile users. Our empirical performance evaluation results demonstrate that the proposal outperforms the traditional particle swarm optimization and genetic algorithm in terms of CPU execution time and allocated memory.

STORMSeq: an open-source, user-friendly pipeline for processing personal genomics data in the cloud.

PubMed

Karczewski, Konrad J; Fernald, Guy Haskin; Martin, Alicia R; Snyder, Michael; Tatonetti, Nicholas P; Dudley, Joel T

2014-01-01

The increasing public availability of personal complete genome sequencing data has ushered in an era of democratized genomics. However, read mapping and variant calling software is constantly improving and individuals with personal genomic data may prefer to customize and update their variant calls. Here, we describe STORMSeq (Scalable Tools for Open-Source Read Mapping), a graphical interface cloud computing solution that does not require a parallel computing environment or extensive technical experience. This customizable and modular system performs read mapping, read cleaning, and variant calling and annotation. At present, STORMSeq costs approximately $2 and 5-10 hours to process a full exome sequence and $30 and 3-8 days to process a whole genome sequence. We provide this open-access and open-source resource as a user-friendly interface in Amazon EC2.

Centralized Duplicate Removal Video Storage System with Privacy Preservation in IoT.

PubMed

Yan, Hongyang; Li, Xuan; Wang, Yu; Jia, Chunfu

2018-06-04

In recent years, the Internet of Things (IoT) has found wide application and attracted much attention. Since most of the end-terminals in IoT have limited capabilities for storage and computing, it has become a trend to outsource the data from local to cloud computing. To further reduce the communication bandwidth and storage space, data deduplication has been widely adopted to eliminate the redundant data. However, since data collected in IoT are sensitive and closely related to users' personal information, the privacy protection of users' information becomes a challenge. As the channels, like the wireless channels between the terminals and the cloud servers in IoT, are public and the cloud servers are not fully trusted, data have to be encrypted before being uploaded to the cloud. However, encryption makes the performance of deduplication by the cloud server difficult because the ciphertext will be different even if the underlying plaintext is identical. In this paper, we build a centralized privacy-preserving duplicate removal storage system, which supports both file-level and block-level deduplication. In order to avoid the leakage of statistical information of data, Intel Software Guard Extensions (SGX) technology is utilized to protect the deduplication process on the cloud server. The results of the experimental analysis demonstrate that the new scheme can significantly improve the deduplication efficiency and enhance the security. It is envisioned that the duplicated removal system with privacy preservation will be of great use in the centralized storage environment of IoT.

Load Balancing in Cloud Computing Environment Using Improved Weighted Round Robin Algorithm for Nonpreemptive Dependent Tasks.

PubMed

Devi, D Chitra; Uthariaraj, V Rhymend

2016-01-01

Cloud computing uses the concepts of scheduling and load balancing to migrate tasks to underutilized VMs for effectively sharing the resources. The scheduling of the nonpreemptive tasks in the cloud computing environment is an irrecoverable restraint and hence it has to be assigned to the most appropriate VMs at the initial placement itself. Practically, the arrived jobs consist of multiple interdependent tasks and they may execute the independent tasks in multiple VMs or in the same VM's multiple cores. Also, the jobs arrive during the run time of the server in varying random intervals under various load conditions. The participating heterogeneous resources are managed by allocating the tasks to appropriate resources by static or dynamic scheduling to make the cloud computing more efficient and thus it improves the user satisfaction. Objective of this work is to introduce and evaluate the proposed scheduling and load balancing algorithm by considering the capabilities of each virtual machine (VM), the task length of each requested job, and the interdependency of multiple tasks. Performance of the proposed algorithm is studied by comparing with the existing methods.

Load Balancing in Cloud Computing Environment Using Improved Weighted Round Robin Algorithm for Nonpreemptive Dependent Tasks

PubMed Central

Devi, D. Chitra; Uthariaraj, V. Rhymend

2016-01-01

Cloud computing uses the concepts of scheduling and load balancing to migrate tasks to underutilized VMs for effectively sharing the resources. The scheduling of the nonpreemptive tasks in the cloud computing environment is an irrecoverable restraint and hence it has to be assigned to the most appropriate VMs at the initial placement itself. Practically, the arrived jobs consist of multiple interdependent tasks and they may execute the independent tasks in multiple VMs or in the same VM's multiple cores. Also, the jobs arrive during the run time of the server in varying random intervals under various load conditions. The participating heterogeneous resources are managed by allocating the tasks to appropriate resources by static or dynamic scheduling to make the cloud computing more efficient and thus it improves the user satisfaction. Objective of this work is to introduce and evaluate the proposed scheduling and load balancing algorithm by considering the capabilities of each virtual machine (VM), the task length of each requested job, and the interdependency of multiple tasks. Performance of the proposed algorithm is studied by comparing with the existing methods. PMID:26955656

Net-Centric Warfare 2.0: Cloud Computing and the New Age of War

DTIC Science & Technology

2009-02-01

Ellyssa Kroski, author of The Hive Mind: Folksonomies and User-based Tagging, writes: “With the advent of social software and Web 2.0, we usher in a new... Folksonomies and User-Based Tagging, (2007), http://infotangle.blogsome. com/2005/12/07/the-hive-mind- folksonomies -and-user-based-tagging/. 45 Office...www.govexec.com/dailyfed/1007/101007cdam1.htm (accessed February 4, 2009). Kroski, Ellyssa. "The Hive Mind: Folksonomies and User-Based Tagging

RAP: RNA-Seq Analysis Pipeline, a new cloud-based NGS web application.

PubMed

D'Antonio, Mattia; D'Onorio De Meo, Paolo; Pallocca, Matteo; Picardi, Ernesto; D'Erchia, Anna Maria; Calogero, Raffaele A; Castrignanò, Tiziana; Pesole, Graziano

2015-01-01

The study of RNA has been dramatically improved by the introduction of Next Generation Sequencing platforms allowing massive and cheap sequencing of selected RNA fractions, also providing information on strand orientation (RNA-Seq). The complexity of transcriptomes and of their regulative pathways make RNA-Seq one of most complex field of NGS applications, addressing several aspects of the expression process (e.g. identification and quantification of expressed genes and transcripts, alternative splicing and polyadenylation, fusion genes and trans-splicing, post-transcriptional events, etc.). In order to provide researchers with an effective and friendly resource for analyzing RNA-Seq data, we present here RAP (RNA-Seq Analysis Pipeline), a cloud computing web application implementing a complete but modular analysis workflow. This pipeline integrates both state-of-the-art bioinformatics tools for RNA-Seq analysis and in-house developed scripts to offer to the user a comprehensive strategy for data analysis. RAP is able to perform quality checks (adopting FastQC and NGS QC Toolkit), identify and quantify expressed genes and transcripts (with Tophat, Cufflinks and HTSeq), detect alternative splicing events (using SpliceTrap) and chimeric transcripts (with ChimeraScan). This pipeline is also able to identify splicing junctions and constitutive or alternative polyadenylation sites (implementing custom analysis modules) and call for statistically significant differences in genes and transcripts expression, splicing pattern and polyadenylation site usage (using Cuffdiff2 and DESeq). Through a user friendly web interface, the RAP workflow can be suitably customized by the user and it is automatically executed on our cloud computing environment. This strategy allows to access to bioinformatics tools and computational resources without specific bioinformatics and IT skills. RAP provides a set of tabular and graphical results that can be helpful to browse, filter and export analyzed data, according to the user needs.

Cloud-based NEXRAD Data Processing and Analysis for Hydrologic Applications

NASA Astrophysics Data System (ADS)

Seo, B. C.; Demir, I.; Keem, M.; Goska, R.; Weber, J.; Krajewski, W. F.

2016-12-01

The real-time and full historical archive of NEXRAD Level II data, covering the entire United States from 1991 to present, recently became available on Amazon cloud S3. This provides a new opportunity to rebuild the Hydro-NEXRAD software system that enabled users to access vast amounts of NEXRAD radar data in support of a wide range of research. The system processes basic radar data (Level II) and delivers radar-rainfall products based on the user's custom selection of features such as space and time domain, river basin, rainfall product space and time resolution, and rainfall estimation algorithms. The cloud-based new system can eliminate prior challenges faced by Hydro-NEXRAD data acquisition and processing: (1) temporal and spatial limitation arising from the limited data storage; (2) archive (past) data ingestion and format conversion; and (3) separate data processing flow for the past and real-time Level II data. To enhance massive data processing and computational efficiency, the new system is implemented and tested for the Iowa domain. This pilot study begins by ingesting rainfall metadata and implementing Hydro-NEXRAD capabilities on the cloud using the new polarimetric features, as well as the existing algorithm modules and scripts. The authors address the reliability and feasibility of cloud computation and processing, followed by an assessment of response times from an interactive web-based system.

Cloud Service Selection Using Multicriteria Decision Analysis

PubMed Central

Anuar, Nor Badrul; Shiraz, Muhammad; Haque, Israat Tanzeena

2014-01-01

Cloud computing (CC) has recently been receiving tremendous attention from the IT industry and academic researchers. CC leverages its unique services to cloud customers in a pay-as-you-go, anytime, anywhere manner. Cloud services provide dynamically scalable services through the Internet on demand. Therefore, service provisioning plays a key role in CC. The cloud customer must be able to select appropriate services according to his or her needs. Several approaches have been proposed to solve the service selection problem, including multicriteria decision analysis (MCDA). MCDA enables the user to choose from among a number of available choices. In this paper, we analyze the application of MCDA to service selection in CC. We identify and synthesize several MCDA techniques and provide a comprehensive analysis of this technology for general readers. In addition, we present a taxonomy derived from a survey of the current literature. Finally, we highlight several state-of-the-art practical aspects of MCDA implementation in cloud computing service selection. The contributions of this study are four-fold: (a) focusing on the state-of-the-art MCDA techniques, (b) highlighting the comparative analysis and suitability of several MCDA methods, (c) presenting a taxonomy through extensive literature review, and (d) analyzing and summarizing the cloud computing service selections in different scenarios. PMID:24696645

Cloud service selection using multicriteria decision analysis.

PubMed

Whaiduzzaman, Md; Gani, Abdullah; Anuar, Nor Badrul; Shiraz, Muhammad; Haque, Mohammad Nazmul; Haque, Israat Tanzeena

2014-01-01

Cloud computing (CC) has recently been receiving tremendous attention from the IT industry and academic researchers. CC leverages its unique services to cloud customers in a pay-as-you-go, anytime, anywhere manner. Cloud services provide dynamically scalable services through the Internet on demand. Therefore, service provisioning plays a key role in CC. The cloud customer must be able to select appropriate services according to his or her needs. Several approaches have been proposed to solve the service selection problem, including multicriteria decision analysis (MCDA). MCDA enables the user to choose from among a number of available choices. In this paper, we analyze the application of MCDA to service selection in CC. We identify and synthesize several MCDA techniques and provide a comprehensive analysis of this technology for general readers. In addition, we present a taxonomy derived from a survey of the current literature. Finally, we highlight several state-of-the-art practical aspects of MCDA implementation in cloud computing service selection. The contributions of this study are four-fold: (a) focusing on the state-of-the-art MCDA techniques, (b) highlighting the comparative analysis and suitability of several MCDA methods, (c) presenting a taxonomy through extensive literature review, and (d) analyzing and summarizing the cloud computing service selections in different scenarios.

User Metrics in NASA Earth Science Data Systems

NASA Technical Reports Server (NTRS)

Lynnes, Chris

2018-01-01

This presentation the collection and use of user metrics in NASA's Earth Science data systems. A variety of collection methods is discussed, with particular emphasis given to the American Customer Satisfaction Index (ASCI). User sentiment on potential use of cloud computing is presented, with generally positive responses. The presentation also discusses various forms of automatically collected metrics, including an example of the relative usage of different functions within the Giovanni analysis system.

Secure Nearest Neighbor Query on Crowd-Sensing Data

PubMed Central

Cheng, Ke; Wang, Liangmin; Zhong, Hong

2016-01-01

Nearest neighbor queries are fundamental in location-based services, and secure nearest neighbor queries mainly focus on how to securely and quickly retrieve the nearest neighbor in the outsourced cloud server. However, the previous big data system structure has changed because of the crowd-sensing data. On the one hand, sensing data terminals as the data owner are numerous and mistrustful, while, on the other hand, in most cases, the terminals find it difficult to finish many safety operation due to computation and storage capability constraints. In light of they Multi Owners and Multi Users (MOMU) situation in the crowd-sensing data cloud environment, this paper presents a secure nearest neighbor query scheme based on the proxy server architecture, which is constructed by protocols of secure two-party computation and secure Voronoi diagram algorithm. It not only preserves the data confidentiality and query privacy but also effectively resists the collusion between the cloud server and the data owners or users. Finally, extensive theoretical and experimental evaluations are presented to show that our proposed scheme achieves a superior balance between the security and query performance compared to other schemes. PMID:27669253

Secure Nearest Neighbor Query on Crowd-Sensing Data.

PubMed

Cheng, Ke; Wang, Liangmin; Zhong, Hong

2016-09-22

Nearest neighbor queries are fundamental in location-based services, and secure nearest neighbor queries mainly focus on how to securely and quickly retrieve the nearest neighbor in the outsourced cloud server. However, the previous big data system structure has changed because of the crowd-sensing data. On the one hand, sensing data terminals as the data owner are numerous and mistrustful, while, on the other hand, in most cases, the terminals find it difficult to finish many safety operation due to computation and storage capability constraints. In light of they Multi Owners and Multi Users (MOMU) situation in the crowd-sensing data cloud environment, this paper presents a secure nearest neighbor query scheme based on the proxy server architecture, which is constructed by protocols of secure two-party computation and secure Voronoi diagram algorithm. It not only preserves the data confidentiality and query privacy but also effectively resists the collusion between the cloud server and the data owners or users. Finally, extensive theoretical and experimental evaluations are presented to show that our proposed scheme achieves a superior balance between the security and query performance compared to other schemes.

Experimental demonstration of multi-dimensional resources integration for service provisioning in cloud radio over fiber network

NASA Astrophysics Data System (ADS)

Yang, Hui; Zhang, Jie; Ji, Yuefeng; He, Yongqi; Lee, Young

2016-07-01

Cloud radio access network (C-RAN) becomes a promising scenario to accommodate high-performance services with ubiquitous user coverage and real-time cloud computing in 5G area. However, the radio network, optical network and processing unit cloud have been decoupled from each other, so that their resources are controlled independently. Traditional architecture cannot implement the resource optimization and scheduling for the high-level service guarantee due to the communication obstacle among them with the growing number of mobile internet users. In this paper, we report a study on multi-dimensional resources integration (MDRI) for service provisioning in cloud radio over fiber network (C-RoFN). A resources integrated provisioning (RIP) scheme using an auxiliary graph is introduced based on the proposed architecture. The MDRI can enhance the responsiveness to dynamic end-to-end user demands and globally optimize radio frequency, optical network and processing resources effectively to maximize radio coverage. The feasibility of the proposed architecture is experimentally verified on OpenFlow-based enhanced SDN testbed. The performance of RIP scheme under heavy traffic load scenario is also quantitatively evaluated to demonstrate the efficiency of the proposal based on MDRI architecture in terms of resource utilization, path blocking probability, network cost and path provisioning latency, compared with other provisioning schemes.

Experimental demonstration of multi-dimensional resources integration for service provisioning in cloud radio over fiber network.

PubMed

Yang, Hui; Zhang, Jie; Ji, Yuefeng; He, Yongqi; Lee, Young

2016-07-28

Cloud radio access network (C-RAN) becomes a promising scenario to accommodate high-performance services with ubiquitous user coverage and real-time cloud computing in 5G area. However, the radio network, optical network and processing unit cloud have been decoupled from each other, so that their resources are controlled independently. Traditional architecture cannot implement the resource optimization and scheduling for the high-level service guarantee due to the communication obstacle among them with the growing number of mobile internet users. In this paper, we report a study on multi-dimensional resources integration (MDRI) for service provisioning in cloud radio over fiber network (C-RoFN). A resources integrated provisioning (RIP) scheme using an auxiliary graph is introduced based on the proposed architecture. The MDRI can enhance the responsiveness to dynamic end-to-end user demands and globally optimize radio frequency, optical network and processing resources effectively to maximize radio coverage. The feasibility of the proposed architecture is experimentally verified on OpenFlow-based enhanced SDN testbed. The performance of RIP scheme under heavy traffic load scenario is also quantitatively evaluated to demonstrate the efficiency of the proposal based on MDRI architecture in terms of resource utilization, path blocking probability, network cost and path provisioning latency, compared with other provisioning schemes.

Experimental demonstration of multi-dimensional resources integration for service provisioning in cloud radio over fiber network

PubMed Central

Yang, Hui; Zhang, Jie; Ji, Yuefeng; He, Yongqi; Lee, Young

2016-01-01

Cloud radio access network (C-RAN) becomes a promising scenario to accommodate high-performance services with ubiquitous user coverage and real-time cloud computing in 5G area. However, the radio network, optical network and processing unit cloud have been decoupled from each other, so that their resources are controlled independently. Traditional architecture cannot implement the resource optimization and scheduling for the high-level service guarantee due to the communication obstacle among them with the growing number of mobile internet users. In this paper, we report a study on multi-dimensional resources integration (MDRI) for service provisioning in cloud radio over fiber network (C-RoFN). A resources integrated provisioning (RIP) scheme using an auxiliary graph is introduced based on the proposed architecture. The MDRI can enhance the responsiveness to dynamic end-to-end user demands and globally optimize radio frequency, optical network and processing resources effectively to maximize radio coverage. The feasibility of the proposed architecture is experimentally verified on OpenFlow-based enhanced SDN testbed. The performance of RIP scheme under heavy traffic load scenario is also quantitatively evaluated to demonstrate the efficiency of the proposal based on MDRI architecture in terms of resource utilization, path blocking probability, network cost and path provisioning latency, compared with other provisioning schemes. PMID:27465296

Auspice: Automatic Service Planning in Cloud/Grid Environments

NASA Astrophysics Data System (ADS)

Chiu, David; Agrawal, Gagan

Recent scientific advances have fostered a mounting number of services and data sets available for utilization. These resources, though scattered across disparate locations, are often loosely coupled both semantically and operationally. This loosely coupled relationship implies the possibility of linking together operations and data sets to answer queries. This task, generally known as automatic service composition, therefore abstracts the process of complex scientific workflow planning from the user. We have been exploring a metadata-driven approach toward automatic service workflow composition, among other enabling mechanisms, in our system, Auspice: Automatic Service Planning in Cloud/Grid Environments. In this paper, we present a complete overview of our system's unique features and outlooks for future deployment as the Cloud computing paradigm becomes increasingly eminent in enabling scientific computing.

Making Spatial Statistics Service Accessible On Cloud Platform

NASA Astrophysics Data System (ADS)

Mu, X.; Wu, J.; Li, T.; Zhong, Y.; Gao, X.

2014-04-01

Web service can bring together applications running on diverse platforms, users can access and share various data, information and models more effectively and conveniently from certain web service platform. Cloud computing emerges as a paradigm of Internet computing in which dynamical, scalable and often virtualized resources are provided as services. With the rampant growth of massive data and restriction of net, traditional web services platforms have some prominent problems existing in development such as calculation efficiency, maintenance cost and data security. In this paper, we offer a spatial statistics service based on Microsoft cloud. An experiment was carried out to evaluate the availability and efficiency of this service. The results show that this spatial statistics service is accessible for the public conveniently with high processing efficiency.

RAP: RNA-Seq Analysis Pipeline, a new cloud-based NGS web application

PubMed Central

2015-01-01

Background The study of RNA has been dramatically improved by the introduction of Next Generation Sequencing platforms allowing massive and cheap sequencing of selected RNA fractions, also providing information on strand orientation (RNA-Seq). The complexity of transcriptomes and of their regulative pathways make RNA-Seq one of most complex field of NGS applications, addressing several aspects of the expression process (e.g. identification and quantification of expressed genes and transcripts, alternative splicing and polyadenylation, fusion genes and trans-splicing, post-transcriptional events, etc.). Moreover, the huge volume of data generated by NGS platforms introduces unprecedented computational and technological challenges to efficiently analyze and store sequence data and results. Methods In order to provide researchers with an effective and friendly resource for analyzing RNA-Seq data, we present here RAP (RNA-Seq Analysis Pipeline), a cloud computing web application implementing a complete but modular analysis workflow. This pipeline integrates both state-of-the-art bioinformatics tools for RNA-Seq analysis and in-house developed scripts to offer to the user a comprehensive strategy for data analysis. RAP is able to perform quality checks (adopting FastQC and NGS QC Toolkit), identify and quantify expressed genes and transcripts (with Tophat, Cufflinks and HTSeq), detect alternative splicing events (using SpliceTrap) and chimeric transcripts (with ChimeraScan). This pipeline is also able to identify splicing junctions and constitutive or alternative polyadenylation sites (implementing custom analysis modules) and call for statistically significant differences in genes and transcripts expression, splicing pattern and polyadenylation site usage (using Cuffdiff2 and DESeq). Results Through a user friendly web interface, the RAP workflow can be suitably customized by the user and it is automatically executed on our cloud computing environment. This strategy allows to access to bioinformatics tools and computational resources without specific bioinformatics and IT skills. RAP provides a set of tabular and graphical results that can be helpful to browse, filter and export analyzed data, according to the user needs. PMID:26046471

A New User Interface for On-Demand Customizable Data Products for Sensors in a SensorWeb

NASA Technical Reports Server (NTRS)

Mandl, Daniel; Cappelaere, Pat; Frye, Stuart; Sohlberg, Rob; Ly, Vuong; Chien, Steve; Sullivan, Don

2011-01-01

A SensorWeb is a set of sensors, which can consist of ground, airborne and space-based sensors interoperating in an automated or autonomous collaborative manner. The NASA SensorWeb toolbox, developed at NASA/GSFC in collaboration with NASA/JPL, NASA/Ames and other partners, is a set of software and standards that (1) enables users to create virtual private networks of sensors over open networks; (2) provides the capability to orchestrate their actions; (3) provides the capability to customize the output data products and (4) enables automated delivery of the data products to the users desktop. A recent addition to the SensorWeb Toolbox is a new user interface, together with web services co-resident with the sensors, to enable rapid creation, loading and execution of new algorithms for processing sensor data. The web service along with the user interface follows the Open Geospatial Consortium (OGC) standard called Web Coverage Processing Service (WCPS). This presentation will detail the prototype that was built and how the WCPS was tested against a HyspIRI flight testbed and an elastic computation cloud on the ground with EO-1 data. HyspIRI is a future NASA decadal mission. The elastic computation cloud stores EO-1 data and runs software similar to Amazon online shopping.

Key Lessons in Building "Data Commons": The Open Science Data Cloud Ecosystem

NASA Astrophysics Data System (ADS)

Patterson, M.; Grossman, R.; Heath, A.; Murphy, M.; Wells, W.

2015-12-01

Cloud computing technology has created a shift around data and data analysis by allowing researchers to push computation to data as opposed to having to pull data to an individual researcher's computer. Subsequently, cloud-based resources can provide unique opportunities to capture computing environments used both to access raw data in its original form and also to create analysis products which may be the source of data for tables and figures presented in research publications. Since 2008, the Open Cloud Consortium (OCC) has operated the Open Science Data Cloud (OSDC), which provides scientific researchers with computational resources for storing, sharing, and analyzing large (terabyte and petabyte-scale) scientific datasets. OSDC has provided compute and storage services to over 750 researchers in a wide variety of data intensive disciplines. Recently, internal users have logged about 2 million core hours each month. The OSDC also serves the research community by colocating these resources with access to nearly a petabyte of public scientific datasets in a variety of fields also accessible for download externally by the public. In our experience operating these resources, researchers are well served by "data commons," meaning cyberinfrastructure that colocates data archives, computing, and storage infrastructure and supports essential tools and services for working with scientific data. In addition to the OSDC public data commons, the OCC operates a data commons in collaboration with NASA and is developing a data commons for NOAA datasets. As cloud-based infrastructures for distributing and computing over data become more pervasive, we ask, "What does it mean to publish data in a data commons?" Here we present the OSDC perspective and discuss several services that are key in architecting data commons, including digital identifier services.

MOLNs: A CLOUD PLATFORM FOR INTERACTIVE, REPRODUCIBLE, AND SCALABLE SPATIAL STOCHASTIC COMPUTATIONAL EXPERIMENTS IN SYSTEMS BIOLOGY USING PyURDME.

PubMed

Drawert, Brian; Trogdon, Michael; Toor, Salman; Petzold, Linda; Hellander, Andreas

2016-01-01

Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools and a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments.

Biomedical cloud computing with Amazon Web Services.

PubMed

Fusaro, Vincent A; Patil, Prasad; Gafni, Erik; Wall, Dennis P; Tonellato, Peter J

2011-08-01

In this overview to biomedical computing in the cloud, we discussed two primary ways to use the cloud (a single instance or cluster), provided a detailed example using NGS mapping, and highlighted the associated costs. While many users new to the cloud may assume that entry is as straightforward as uploading an application and selecting an instance type and storage options, we illustrated that there is substantial up-front effort required before an application can make full use of the cloud's vast resources. Our intention was to provide a set of best practices and to illustrate how those apply to a typical application pipeline for biomedical informatics, but also general enough for extrapolation to other types of computational problems. Our mapping example was intended to illustrate how to develop a scalable project and not to compare and contrast alignment algorithms for read mapping and genome assembly. Indeed, with a newer aligner such as Bowtie, it is possible to map the entire African genome using one m2.2xlarge instance in 48 hours for a total cost of approximately $48 in computation time. In our example, we were not concerned with data transfer rates, which are heavily influenced by the amount of available bandwidth, connection latency, and network availability. When transferring large amounts of data to the cloud, bandwidth limitations can be a major bottleneck, and in some cases it is more efficient to simply mail a storage device containing the data to AWS (http://aws.amazon.com/importexport/). More information about cloud computing, detailed cost analysis, and security can be found in references.

An innovative privacy preserving technique for incremental datasets on cloud computing.

PubMed

Aldeen, Yousra Abdul Alsahib S; Salleh, Mazleena; Aljeroudi, Yazan

2016-08-01

Cloud computing (CC) is a magnificent service-based delivery with gigantic computer processing power and data storage across connected communications channels. It imparted overwhelming technological impetus in the internet (web) mediated IT industry, where users can easily share private data for further analysis and mining. Furthermore, user affable CC services enable to deploy sundry applications economically. Meanwhile, simple data sharing impelled various phishing attacks and malware assisted security threats. Some privacy sensitive applications like health services on cloud that are built with several economic and operational benefits necessitate enhanced security. Thus, absolute cyberspace security and mitigation against phishing blitz became mandatory to protect overall data privacy. Typically, diverse applications datasets are anonymized with better privacy to owners without providing all secrecy requirements to the newly added records. Some proposed techniques emphasized this issue by re-anonymizing the datasets from the scratch. The utmost privacy protection over incremental datasets on CC is far from being achieved. Certainly, the distribution of huge datasets volume across multiple storage nodes limits the privacy preservation. In this view, we propose a new anonymization technique to attain better privacy protection with high data utility over distributed and incremental datasets on CC. The proficiency of data privacy preservation and improved confidentiality requirements is demonstrated through performance evaluation. Copyright © 2016 Elsevier Inc. All rights reserved.

NASA/MSFC multilayer diffusion models and computer program for operational prediction of toxic fuel hazards

NASA Technical Reports Server (NTRS)

Dumbauld, R. K.; Bjorklund, J. R.; Bowers, J. F.

1973-01-01

The NASA/MSFC multilayer diffusion models are discribed which are used in applying meteorological information to the estimation of toxic fuel hazards resulting from the launch of rocket vehicle and from accidental cold spills and leaks of toxic fuels. Background information, definitions of terms, description of the multilayer concept are presented along with formulas for determining the buoyant rise of hot exhaust clouds or plumes from conflagrations, and descriptions of the multilayer diffusion models. A brief description of the computer program is given, and sample problems and their solutions are included. Derivations of the cloud rise formulas, users instructions, and computer program output lists are also included.

Sentinel-1 Archive and Processing in the Cloud using the Hybrid Pluggable Processing Pipeline (HyP3) at the ASF DAAC

NASA Astrophysics Data System (ADS)

Arko, S. A.; Hogenson, R.; Geiger, A.; Herrmann, J.; Buechler, B.; Hogenson, K.

2016-12-01

In the coming years there will be an unprecedented amount of SAR data available on a free and open basis to research and operational users around the globe. The Alaska Satellite Facility (ASF) DAAC hosts, through an international agreement, data from the Sentinel-1 spacecraft and will be hosting data from the upcoming NASA ISRO SAR (NISAR) mission. To more effectively manage and exploit these vast datasets, ASF DAAC has begun moving portions of the archive to the cloud and utilizing cloud services to provide higher-level processing on the data. The Hybrid Pluggable Processing Pipeline (HyP3) project is designed to support higher-level data processing in the cloud and extend the capabilities of researchers to larger scales. Built upon a set of core Amazon cloud services, the HyP3 system allows users to request data processing using a number of canned algorithms or their own algorithms once they have been uploaded to the cloud. The HyP3 system automatically accesses the ASF cloud-based archive through the DAAC RESTful application programming interface and processes the data on Amazon's elastic compute cluster (EC2). Final products are distributed through Amazon's simple storage service (S3) and are available for user download. This presentation will provide an overview of ASF DAAC's activities moving the Sentinel-1 archive into the cloud and developing the integrated HyP3 system, covering both the benefits and difficulties of working in the cloud. Additionally, we will focus on the utilization of HyP3 for higher-level processing of SAR data. Two example algorithms, for sea-ice tracking and change detection, will be discussed as well as the mechanism for integrating new algorithms into the pipeline for community use.

Design and implementation of a cloud based lithography illumination pupil processing application

NASA Astrophysics Data System (ADS)

Zhang, Youbao; Ma, Xinghua; Zhu, Jing; Zhang, Fang; Huang, Huijie

2017-02-01

Pupil parameters are important parameters to evaluate the quality of lithography illumination system. In this paper, a cloud based full-featured pupil processing application is implemented. A web browser is used for the UI (User Interface), the websocket protocol and JSON format are used for the communication between the client and the server, and the computing part is implemented in the server side, where the application integrated a variety of high quality professional libraries, such as image processing libraries libvips and ImageMagic, automatic reporting system latex, etc., to support the program. The cloud based framework takes advantage of server's superior computing power and rich software collections, and the program could run anywhere there is a modern browser due to its web UI design. Compared to the traditional way of software operation model: purchased, licensed, shipped, downloaded, installed, maintained, and upgraded, the new cloud based approach, which is no installation, easy to use and maintenance, opens up a new way. Cloud based application probably is the future of the software development.

A New Cloud Architecture of Virtual Trusted Platform Modules

NASA Astrophysics Data System (ADS)

Liu, Dongxi; Lee, Jack; Jang, Julian; Nepal, Surya; Zic, John

We propose and implement a cloud architecture of virtual Trusted Platform Modules (TPMs) to improve the usability of TPMs. In this architecture, virtual TPMs can be obtained from the TPM cloud on demand. Hence, the TPM functionality is available for applications that do not have physical TPMs in their local platforms. Moreover, the TPM cloud allows users to access their keys and data in the same virtual TPM even if they move to untrusted platforms. The TPM cloud is easy to access for applications in different languages since cloud computing delivers services in standard protocols. The functionality of the TPM cloud is demonstrated by applying it to implement the Needham-Schroeder public-key protocol for web authentications, such that the strong security provided by TPMs is integrated into high level applications. The chain of trust based on the TPM cloud is discussed and the security properties of the virtual TPMs in the cloud is analyzed.

An Interface for Biomedical Big Data Processing on the Tianhe-2 Supercomputer.

PubMed

Yang, Xi; Wu, Chengkun; Lu, Kai; Fang, Lin; Zhang, Yong; Li, Shengkang; Guo, Guixin; Du, YunFei

2017-12-01

Big data, cloud computing, and high-performance computing (HPC) are at the verge of convergence. Cloud computing is already playing an active part in big data processing with the help of big data frameworks like Hadoop and Spark. The recent upsurge of high-performance computing in China provides extra possibilities and capacity to address the challenges associated with big data. In this paper, we propose Orion-a big data interface on the Tianhe-2 supercomputer-to enable big data applications to run on Tianhe-2 via a single command or a shell script. Orion supports multiple users, and each user can launch multiple tasks. It minimizes the effort needed to initiate big data applications on the Tianhe-2 supercomputer via automated configuration. Orion follows the "allocate-when-needed" paradigm, and it avoids the idle occupation of computational resources. We tested the utility and performance of Orion using a big genomic dataset and achieved a satisfactory performance on Tianhe-2 with very few modifications to existing applications that were implemented in Hadoop/Spark. In summary, Orion provides a practical and economical interface for big data processing on Tianhe-2.

An interactive web-based system using cloud for large-scale visual analytics

NASA Astrophysics Data System (ADS)

Kaseb, Ahmed S.; Berry, Everett; Rozolis, Erik; McNulty, Kyle; Bontrager, Seth; Koh, Youngsol; Lu, Yung-Hsiang; Delp, Edward J.

2015-03-01

Network cameras have been growing rapidly in recent years. Thousands of public network cameras provide tremendous amount of visual information about the environment. There is a need to analyze this valuable information for a better understanding of the world around us. This paper presents an interactive web-based system that enables users to execute image analysis and computer vision techniques on a large scale to analyze the data from more than 65,000 worldwide cameras. This paper focuses on how to use both the system's website and Application Programming Interface (API). Given a computer program that analyzes a single frame, the user needs to make only slight changes to the existing program and choose the cameras to analyze. The system handles the heterogeneity of the geographically distributed cameras, e.g. different brands, resolutions. The system allocates and manages Amazon EC2 and Windows Azure cloud resources to meet the analysis requirements.

STORMSeq: An Open-Source, User-Friendly Pipeline for Processing Personal Genomics Data in the Cloud

PubMed Central

Karczewski, Konrad J.; Fernald, Guy Haskin; Martin, Alicia R.; Snyder, Michael; Tatonetti, Nicholas P.; Dudley, Joel T.

2014-01-01

The increasing public availability of personal complete genome sequencing data has ushered in an era of democratized genomics. However, read mapping and variant calling software is constantly improving and individuals with personal genomic data may prefer to customize and update their variant calls. Here, we describe STORMSeq (Scalable Tools for Open-Source Read Mapping), a graphical interface cloud computing solution that does not require a parallel computing environment or extensive technical experience. This customizable and modular system performs read mapping, read cleaning, and variant calling and annotation. At present, STORMSeq costs approximately $2 and 5–10 hours to process a full exome sequence and $30 and 3–8 days to process a whole genome sequence. We provide this open-access and open-source resource as a user-friendly interface in Amazon EC2. PMID:24454756

cadcVOFS: A FUSE Based File System Layer for VOSpace

NASA Astrophysics Data System (ADS)

Kavelaars, J.; Dowler, P.; Jenkins, D.; Hill, N.; Damian, A.

2012-09-01

The CADC is now making extensive use of the VOSpace protocol for user managed storage. The VOSpace standard allows a diverse set of rich data services to be delivered to users via a simple protocol. We have recently developed the cadcVOFS, a FUSE based file-system layer for VOSpace. cadcVOFS provides a filesystem layer on-top of VOSpace so that standard Unix tools (such as ‘find’, ‘emacs’, ‘awk’ etc) can be used directly on the data objects stored in VOSpace. Once mounted the VOSpace appears as a network storage volume inside the operating system. Within the CADC Cloud Computing project (CANFAR) we have used VOSpace as the method for retrieving and storing processing inputs and products. The abstraction of storage is an important component of Cloud Computing and the high use level of our VOSpace service reflects this.

Developing a Cloud-Based Online Geospatial Information Sharing and Geoprocessing Platform to Facilitate Collaborative Education and Research

NASA Astrophysics Data System (ADS)

Yang, Z. L.; Cao, J.; Hu, K.; Gui, Z. P.; Wu, H. Y.; You, L.

2016-06-01

Efficient online discovering and applying geospatial information resources (GIRs) is critical in Earth Science domain as while for cross-disciplinary applications. However, to achieve it is challenging due to the heterogeneity, complexity and privacy of online GIRs. In this article, GeoSquare, a collaborative online geospatial information sharing and geoprocessing platform, was developed to tackle this problem. Specifically, (1) GIRs registration and multi-view query functions allow users to publish and discover GIRs more effectively. (2) Online geoprocessing and real-time execution status checking help users process data and conduct analysis without pre-installation of cumbersome professional tools on their own machines. (3) A service chain orchestration function enables domain experts to contribute and share their domain knowledge with community members through workflow modeling. (4) User inventory management allows registered users to collect and manage their own GIRs, monitor their execution status, and track their own geoprocessing histories. Besides, to enhance the flexibility and capacity of GeoSquare, distributed storage and cloud computing technologies are employed. To support interactive teaching and training, GeoSquare adopts the rich internet application (RIA) technology to create user-friendly graphical user interface (GUI). Results show that GeoSquare can integrate and foster collaboration between dispersed GIRs, computing resources and people. Subsequently, educators and researchers can share and exchange resources in an efficient and harmonious way.

Templet Web: the use of volunteer computing approach in PaaS-style cloud

NASA Astrophysics Data System (ADS)

Vostokin, Sergei; Artamonov, Yuriy; Tsarev, Daniil

2018-03-01

This article presents the Templet Web cloud service. The service is designed for high-performance scientific computing automation. The use of high-performance technology is specifically required by new fields of computational science such as data mining, artificial intelligence, machine learning, and others. Cloud technologies provide a significant cost reduction for high-performance scientific applications. The main objectives to achieve this cost reduction in the Templet Web service design are: (a) the implementation of "on-demand" access; (b) source code deployment management; (c) high-performance computing programs development automation. The distinctive feature of the service is the approach mainly used in the field of volunteer computing, when a person who has access to a computer system delegates his access rights to the requesting user. We developed an access procedure, algorithms, and software for utilization of free computational resources of the academic cluster system in line with the methods of volunteer computing. The Templet Web service has been in operation for five years. It has been successfully used for conducting laboratory workshops and solving research problems, some of which are considered in this article. The article also provides an overview of research directions related to service development.

Detecting Abnormal Machine Characteristics in Cloud Infrastructures

NASA Technical Reports Server (NTRS)

Bhaduri, Kanishka; Das, Kamalika; Matthews, Bryan L.

2011-01-01

In the cloud computing environment resources are accessed as services rather than as a product. Monitoring this system for performance is crucial because of typical pay-peruse packages bought by the users for their jobs. With the huge number of machines currently in the cloud system, it is often extremely difficult for system administrators to keep track of all machines using distributed monitoring programs such as Ganglia1 which lacks system health assessment and summarization capabilities. To overcome this problem, we propose a technique for automated anomaly detection using machine performance data in the cloud. Our algorithm is entirely distributed and runs locally on each computing machine on the cloud in order to rank the machines in order of their anomalous behavior for given jobs. There is no need to centralize any of the performance data for the analysis and at the end of the analysis, our algorithm generates error reports, thereby allowing the system administrators to take corrective actions. Experiments performed on real data sets collected for different jobs validate the fact that our algorithm has a low overhead for tracking anomalous machines in a cloud infrastructure.

Cloud Computing Technologies Facilitate Earth Research

NASA Technical Reports Server (NTRS)

2015-01-01

Under a Space Act Agreement, NASA partnered with Seattle-based Amazon Web Services to make the agency's climate and Earth science satellite data publicly available on the company's servers. Users can access the data for free, but they can also pay to use Amazon's computing services to analyze and visualize information using the same software available to NASA researchers.

Hybrid Pluggable Processing Pipeline (HyP3): Programmatic Access to Cloud-Based Processing of SAR Data

NASA Astrophysics Data System (ADS)

Weeden, R.; Horn, W. B.; Dimarchi, H.; Arko, S. A.; Hogenson, K.

2017-12-01

A problem often faced by Earth science researchers is the question of how to scale algorithms that were developed against few datasets and take them to regional or global scales. This problem only gets worse as we look to a future with larger and larger datasets becoming available. One significant hurdle can be having the processing and storage resources available for such a task, not to mention the administration of those resources. As a processing environment, the cloud offers nearly unlimited potential for compute and storage, with limited administration required. The goal of the Hybrid Pluggable Processing Pipeline (HyP3) project was to demonstrate the utility of the Amazon cloud to process large amounts of data quickly and cost effectively. Principally built by three undergraduate students at the ASF DAAC, the HyP3 system relies on core Amazon cloud services such as Lambda, Relational Database Service (RDS), Elastic Compute Cloud (EC2), Simple Storage Service (S3), and Elastic Beanstalk. HyP3 provides an Application Programming Interface (API) through which users can programmatically interface with the HyP3 system; allowing them to monitor and control processing jobs running in HyP3, and retrieve the generated HyP3 products when completed. This presentation will focus on the development techniques and enabling technologies that were used in developing the HyP3 system. Data and process flow, from new subscription through to order completion will be shown, highlighting the benefits of the cloud for each step. Because the HyP3 system can be accessed directly from a user's Python scripts, powerful applications leveraging SAR products can be put together fairly easily. This is the true power of HyP3; allowing people to programmatically leverage the power of the cloud.

Simulation Platform: a cloud-based online simulation environment.

PubMed

Yamazaki, Tadashi; Ikeno, Hidetoshi; Okumura, Yoshihiro; Satoh, Shunji; Kamiyama, Yoshimi; Hirata, Yutaka; Inagaki, Keiichiro; Ishihara, Akito; Kannon, Takayuki; Usui, Shiro

2011-09-01

For multi-scale and multi-modal neural modeling, it is needed to handle multiple neural models described at different levels seamlessly. Database technology will become more important for these studies, specifically for downloading and handling the neural models seamlessly and effortlessly. To date, conventional neuroinformatics databases have solely been designed to archive model files, but the databases should provide a chance for users to validate the models before downloading them. In this paper, we report our on-going project to develop a cloud-based web service for online simulation called "Simulation Platform". Simulation Platform is a cloud of virtual machines running GNU/Linux. On a virtual machine, various software including developer tools such as compilers and libraries, popular neural simulators such as GENESIS, NEURON and NEST, and scientific software such as Gnuplot, R and Octave, are pre-installed. When a user posts a request, a virtual machine is assigned to the user, and the simulation starts on that machine. The user remotely accesses to the machine through a web browser and carries out the simulation, without the need to install any software but a web browser on the user's own computer. Therefore, Simulation Platform is expected to eliminate impediments to handle multiple neural models that require multiple software. Copyright © 2011 Elsevier Ltd. All rights reserved.

Reprint of: Simulation Platform: a cloud-based online simulation environment.

PubMed

Yamazaki, Tadashi; Ikeno, Hidetoshi; Okumura, Yoshihiro; Satoh, Shunji; Kamiyama, Yoshimi; Hirata, Yutaka; Inagaki, Keiichiro; Ishihara, Akito; Kannon, Takayuki; Usui, Shiro

2011-11-01

For multi-scale and multi-modal neural modeling, it is needed to handle multiple neural models described at different levels seamlessly. Database technology will become more important for these studies, specifically for downloading and handling the neural models seamlessly and effortlessly. To date, conventional neuroinformatics databases have solely been designed to archive model files, but the databases should provide a chance for users to validate the models before downloading them. In this paper, we report our on-going project to develop a cloud-based web service for online simulation called "Simulation Platform". Simulation Platform is a cloud of virtual machines running GNU/Linux. On a virtual machine, various software including developer tools such as compilers and libraries, popular neural simulators such as GENESIS, NEURON and NEST, and scientific software such as Gnuplot, R and Octave, are pre-installed. When a user posts a request, a virtual machine is assigned to the user, and the simulation starts on that machine. The user remotely accesses to the machine through a web browser and carries out the simulation, without the need to install any software but a web browser on the user's own computer. Therefore, Simulation Platform is expected to eliminate impediments to handle multiple neural models that require multiple software. Copyright © 2011 Elsevier Ltd. All rights reserved.

A case study of tuning MapReduce for efficient Bioinformatics in the cloud

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shi, Lizhen; Wang, Zhong; Yu, Weikuan

The combination of the Hadoop MapReduce programming model and cloud computing allows biological scientists to analyze next-generation sequencing (NGS) data in a timely and cost-effective manner. Cloud computing platforms remove the burden of IT facility procurement and management from end users and provide ease of access to Hadoop clusters. However, biological scientists are still expected to choose appropriate Hadoop parameters for running their jobs. More importantly, the available Hadoop tuning guidelines are either obsolete or too general to capture the particular characteristics of bioinformatics applications. In this paper, we aim to minimize the cloud computing cost spent on bioinformatics datamore » analysis by optimizing the extracted significant Hadoop parameters. When using MapReduce-based bioinformatics tools in the cloud, the default settings often lead to resource underutilization and wasteful expenses. We choose k-mer counting, a representative application used in a large number of NGS data analysis tools, as our study case. Experimental results show that, with the fine-tuned parameters, we achieve a total of 4× speedup compared with the original performance (using the default settings). Finally, this paper presents an exemplary case for tuning MapReduce-based bioinformatics applications in the cloud, and documents the key parameters that could lead to significant performance benefits.« less

Crowd Sensing-Enabling Security Service Recommendation for Social Fog Computing Systems

PubMed Central

Wu, Jun; Su, Zhou; Li, Jianhua

2017-01-01

Fog computing, shifting intelligence and resources from the remote cloud to edge networks, has the potential of providing low-latency for the communication from sensing data sources to users. For the objects from the Internet of Things (IoT) to the cloud, it is a new trend that the objects establish social-like relationships with each other, which efficiently brings the benefits of developed sociality to a complex environment. As fog service become more sophisticated, it will become more convenient for fog users to share their own services, resources, and data via social networks. Meanwhile, the efficient social organization can enable more flexible, secure, and collaborative networking. Aforementioned advantages make the social network a potential architecture for fog computing systems. In this paper, we design an architecture for social fog computing, in which the services of fog are provisioned based on “friend” relationships. To the best of our knowledge, this is the first attempt at an organized fog computing system-based social model. Meanwhile, social networking enhances the complexity and security risks of fog computing services, creating difficulties of security service recommendations in social fog computing. To address this, we propose a novel crowd sensing-enabling security service provisioning method to recommend security services accurately in social fog computing systems. Simulation results show the feasibilities and efficiency of the crowd sensing-enabling security service recommendation method for social fog computing systems. PMID:28758943

Crowd Sensing-Enabling Security Service Recommendation for Social Fog Computing Systems.

PubMed

Wu, Jun; Su, Zhou; Wang, Shen; Li, Jianhua

2017-07-30

Fog computing, shifting intelligence and resources from the remote cloud to edge networks, has the potential of providing low-latency for the communication from sensing data sources to users. For the objects from the Internet of Things (IoT) to the cloud, it is a new trend that the objects establish social-like relationships with each other, which efficiently brings the benefits of developed sociality to a complex environment. As fog service become more sophisticated, it will become more convenient for fog users to share their own services, resources, and data via social networks. Meanwhile, the efficient social organization can enable more flexible, secure, and collaborative networking. Aforementioned advantages make the social network a potential architecture for fog computing systems. In this paper, we design an architecture for social fog computing, in which the services of fog are provisioned based on "friend" relationships. To the best of our knowledge, this is the first attempt at an organized fog computing system-based social model. Meanwhile, social networking enhances the complexity and security risks of fog computing services, creating difficulties of security service recommendations in social fog computing. To address this, we propose a novel crowd sensing-enabling security service provisioning method to recommend security services accurately in social fog computing systems. Simulation results show the feasibilities and efficiency of the crowd sensing-enabling security service recommendation method for social fog computing systems.

CloudNeo: a cloud pipeline for identifying patient-specific tumor neoantigens.

PubMed

Bais, Preeti; Namburi, Sandeep; Gatti, Daniel M; Zhang, Xinyu; Chuang, Jeffrey H

2017-10-01

We present CloudNeo, a cloud-based computational workflow for identifying patient-specific tumor neoantigens from next generation sequencing data. Tumor-specific mutant peptides can be detected by the immune system through their interactions with the human leukocyte antigen complex, and neoantigen presence has recently been shown to correlate with anti T-cell immunity and efficacy of checkpoint inhibitor therapy. However computing capabilities to identify neoantigens from genomic sequencing data are a limiting factor for understanding their role. This challenge has grown as cancer datasets become increasingly abundant, making them cumbersome to store and analyze on local servers. Our cloud-based pipeline provides scalable computation capabilities for neoantigen identification while eliminating the need to invest in local infrastructure for data transfer, storage or compute. The pipeline is a Common Workflow Language (CWL) implementation of human leukocyte antigen (HLA) typing using Polysolver or HLAminer combined with custom scripts for mutant peptide identification and NetMHCpan for neoantigen prediction. We have demonstrated the efficacy of these pipelines on Amazon cloud instances through the Seven Bridges Genomics implementation of the NCI Cancer Genomics Cloud, which provides graphical interfaces for running and editing, infrastructure for workflow sharing and version tracking, and access to TCGA data. The CWL implementation is at: https://github.com/TheJacksonLaboratory/CloudNeo. For users who have obtained licenses for all internal software, integrated versions in CWL and on the Seven Bridges Cancer Genomics Cloud platform (https://cgc.sbgenomics.com/, recommended version) can be obtained by contacting the authors. jeff.chuang@jax.org. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

Toward a Proof of Concept Cloud Framework for Physics Applications on Blue Gene Supercomputers

NASA Astrophysics Data System (ADS)

Dreher, Patrick; Scullin, William; Vouk, Mladen

2015-09-01

Traditional high performance supercomputers are capable of delivering large sustained state-of-the-art computational resources to physics applications over extended periods of time using batch processing mode operating environments. However, today there is an increasing demand for more complex workflows that involve large fluctuations in the levels of HPC physics computational requirements during the simulations. Some of the workflow components may also require a richer set of operating system features and schedulers than normally found in a batch oriented HPC environment. This paper reports on progress toward a proof of concept design that implements a cloud framework onto BG/P and BG/Q platforms at the Argonne Leadership Computing Facility. The BG/P implementation utilizes the Kittyhawk utility and the BG/Q platform uses an experimental heterogeneous FusedOS operating system environment. Both platforms use the Virtual Computing Laboratory as the cloud computing system embedded within the supercomputer. This proof of concept design allows a cloud to be configured so that it can capitalize on the specialized infrastructure capabilities of a supercomputer and the flexible cloud configurations without resorting to virtualization. Initial testing of the proof of concept system is done using the lattice QCD MILC code. These types of user reconfigurable environments have the potential to deliver experimental schedulers and operating systems within a working HPC environment for physics computations that may be different from the native OS and schedulers on production HPC supercomputers.

NGScloud: RNA-seq analysis of non-model species using cloud computing.

PubMed

Mora-Márquez, Fernando; Vázquez-Poletti, José Luis; López de Heredia, Unai

2018-05-03

RNA-seq analysis usually requires large computing infrastructures. NGScloud is a bioinformatic system developed to analyze RNA-seq data using the cloud computing services of Amazon that permit the access to ad hoc computing infrastructure scaled according to the complexity of the experiment, so its costs and times can be optimized. The application provides a user-friendly front-end to operate Amazon's hardware resources, and to control a workflow of RNA-seq analysis oriented to non-model species, incorporating the cluster concept, which allows parallel runs of common RNA-seq analysis programs in several virtual machines for faster analysis. NGScloud is freely available at https://github.com/GGFHF/NGScloud/. A manual detailing installation and how-to-use instructions is available with the distribution. unai.lopezdeheredia@upm.es.

Continuous-variable quantum computing on encrypted data.

PubMed

Marshall, Kevin; Jacobsen, Christian S; Schäfermeier, Clemens; Gehring, Tobias; Weedbrook, Christian; Andersen, Ulrik L

2016-12-14

The ability to perform computations on encrypted data is a powerful tool for protecting a client's privacy, especially in today's era of cloud and distributed computing. In terms of privacy, the best solutions that classical techniques can achieve are unfortunately not unconditionally secure in the sense that they are dependent on a hacker's computational power. Here we theoretically investigate, and experimentally demonstrate with Gaussian displacement and squeezing operations, a quantum solution that achieves the security of a user's privacy using the practical technology of continuous variables. We demonstrate losses of up to 10 km both ways between the client and the server and show that security can still be achieved. Our approach offers a number of practical benefits (from a quantum perspective) that could one day allow the potential widespread adoption of this quantum technology in future cloud-based computing networks.

Continuous-variable quantum computing on encrypted data

PubMed Central

Marshall, Kevin; Jacobsen, Christian S.; Schäfermeier, Clemens; Gehring, Tobias; Weedbrook, Christian; Andersen, Ulrik L.

2016-01-01

The ability to perform computations on encrypted data is a powerful tool for protecting a client's privacy, especially in today's era of cloud and distributed computing. In terms of privacy, the best solutions that classical techniques can achieve are unfortunately not unconditionally secure in the sense that they are dependent on a hacker's computational power. Here we theoretically investigate, and experimentally demonstrate with Gaussian displacement and squeezing operations, a quantum solution that achieves the security of a user's privacy using the practical technology of continuous variables. We demonstrate losses of up to 10 km both ways between the client and the server and show that security can still be achieved. Our approach offers a number of practical benefits (from a quantum perspective) that could one day allow the potential widespread adoption of this quantum technology in future cloud-based computing networks. PMID:27966528

Continuous-variable quantum computing on encrypted data

NASA Astrophysics Data System (ADS)

Marshall, Kevin; Jacobsen, Christian S.; Schäfermeier, Clemens; Gehring, Tobias; Weedbrook, Christian; Andersen, Ulrik L.

2016-12-01

The ability to perform computations on encrypted data is a powerful tool for protecting a client's privacy, especially in today's era of cloud and distributed computing. In terms of privacy, the best solutions that classical techniques can achieve are unfortunately not unconditionally secure in the sense that they are dependent on a hacker's computational power. Here we theoretically investigate, and experimentally demonstrate with Gaussian displacement and squeezing operations, a quantum solution that achieves the security of a user's privacy using the practical technology of continuous variables. We demonstrate losses of up to 10 km both ways between the client and the server and show that security can still be achieved. Our approach offers a number of practical benefits (from a quantum perspective) that could one day allow the potential widespread adoption of this quantum technology in future cloud-based computing networks.

Cloud BioLinux: pre-configured and on-demand bioinformatics computing for the genomics community.

PubMed

Krampis, Konstantinos; Booth, Tim; Chapman, Brad; Tiwari, Bela; Bicak, Mesude; Field, Dawn; Nelson, Karen E

2012-03-19

A steep drop in the cost of next-generation sequencing during recent years has made the technology affordable to the majority of researchers, but downstream bioinformatic analysis still poses a resource bottleneck for smaller laboratories and institutes that do not have access to substantial computational resources. Sequencing instruments are typically bundled with only the minimal processing and storage capacity required for data capture during sequencing runs. Given the scale of sequence datasets, scientific value cannot be obtained from acquiring a sequencer unless it is accompanied by an equal investment in informatics infrastructure. Cloud BioLinux is a publicly accessible Virtual Machine (VM) that enables scientists to quickly provision on-demand infrastructures for high-performance bioinformatics computing using cloud platforms. Users have instant access to a range of pre-configured command line and graphical software applications, including a full-featured desktop interface, documentation and over 135 bioinformatics packages for applications including sequence alignment, clustering, assembly, display, editing, and phylogeny. Each tool's functionality is fully described in the documentation directly accessible from the graphical interface of the VM. Besides the Amazon EC2 cloud, we have started instances of Cloud BioLinux on a private Eucalyptus cloud installed at the J. Craig Venter Institute, and demonstrated access to the bioinformatic tools interface through a remote connection to EC2 instances from a local desktop computer. Documentation for using Cloud BioLinux on EC2 is available from our project website, while a Eucalyptus cloud image and VirtualBox Appliance is also publicly available for download and use by researchers with access to private clouds. Cloud BioLinux provides a platform for developing bioinformatics infrastructures on the cloud. An automated and configurable process builds Virtual Machines, allowing the development of highly customized versions from a shared code base. This shared community toolkit enables application specific analysis platforms on the cloud by minimizing the effort required to prepare and maintain them.

Cloud BioLinux: pre-configured and on-demand bioinformatics computing for the genomics community

PubMed Central

2012-01-01

Background A steep drop in the cost of next-generation sequencing during recent years has made the technology affordable to the majority of researchers, but downstream bioinformatic analysis still poses a resource bottleneck for smaller laboratories and institutes that do not have access to substantial computational resources. Sequencing instruments are typically bundled with only the minimal processing and storage capacity required for data capture during sequencing runs. Given the scale of sequence datasets, scientific value cannot be obtained from acquiring a sequencer unless it is accompanied by an equal investment in informatics infrastructure. Results Cloud BioLinux is a publicly accessible Virtual Machine (VM) that enables scientists to quickly provision on-demand infrastructures for high-performance bioinformatics computing using cloud platforms. Users have instant access to a range of pre-configured command line and graphical software applications, including a full-featured desktop interface, documentation and over 135 bioinformatics packages for applications including sequence alignment, clustering, assembly, display, editing, and phylogeny. Each tool's functionality is fully described in the documentation directly accessible from the graphical interface of the VM. Besides the Amazon EC2 cloud, we have started instances of Cloud BioLinux on a private Eucalyptus cloud installed at the J. Craig Venter Institute, and demonstrated access to the bioinformatic tools interface through a remote connection to EC2 instances from a local desktop computer. Documentation for using Cloud BioLinux on EC2 is available from our project website, while a Eucalyptus cloud image and VirtualBox Appliance is also publicly available for download and use by researchers with access to private clouds. Conclusions Cloud BioLinux provides a platform for developing bioinformatics infrastructures on the cloud. An automated and configurable process builds Virtual Machines, allowing the development of highly customized versions from a shared code base. This shared community toolkit enables application specific analysis platforms on the cloud by minimizing the effort required to prepare and maintain them. PMID:22429538

The structure of the clouds distributed operating system

NASA Technical Reports Server (NTRS)

Dasgupta, Partha; Leblanc, Richard J., Jr.

1989-01-01

A novel system architecture, based on the object model, is the central structuring concept used in the Clouds distributed operating system. This architecture makes Clouds attractive over a wide class of machines and environments. Clouds is a native operating system, designed and implemented at Georgia Tech. and runs on a set of generated purpose computers connected via a local area network. The system architecture of Clouds is composed of a system-wide global set of persistent (long-lived) virtual address spaces, called objects that contain persistent data and code. The object concept is implemented at the operating system level, thus presenting a single level storage view to the user. Lightweight treads carry computational activity through the code stored in the objects. The persistent objects and threads gives rise to a programming environment composed of shared permanent memory, dispensing with the need for hardware-derived concepts such as the file systems and message systems. Though the hardware may be distributed and may have disks and networks, the Clouds provides the applications with a logically centralized system, based on a shared, structured, single level store. The current design of Clouds uses a minimalist philosophy with respect to both the kernel and the operating system. That is, the kernel and the operating system support a bare minimum of functionality. Clouds also adheres to the concept of separation of policy and mechanism. Most low-level operating system services are implemented above the kernel and most high level services are implemented at the user level. From the measured performance of using the kernel mechanisms, we are able to demonstrate that efficient implementations are feasible for the object model on commercially available hardware. Clouds provides a rich environment for conducting research in distributed systems. Some of the topics addressed in this paper include distributed programming environments, consistency of persistent data and fault-tolerance.

An efficient framework for modeling clouds from Landsat8 images

NASA Astrophysics Data System (ADS)

Yuan, Chunqiang; Guo, Jing

2015-03-01

Cloud plays an important role in creating realistic outdoor scenes for video game and flight simulation applications. Classic methods have been proposed for cumulus cloud modeling. However, these methods are not flexible for modeling large cloud scenes with hundreds of clouds in that the user must repeatedly model each cloud and adjust its various properties. This paper presents a meteorologically based method to reconstruct cumulus clouds from high resolution Landsat8 satellite images. From these input satellite images, the clouds are first segmented from the background. Then, the cloud top surface is estimated from the temperature of the infrared image. After that, under a mild assumption of flat base for cumulus cloud, the base height of each cloud is computed by averaging the top height for pixels on the cloud edge. Then, the extinction is generated from the visible image. Finally, we enrich the initial shapes of clouds using a fractal method and represent the recovered clouds as a particle system. The experimental results demonstrate our method can yield realistic cloud scenes resembling those in the satellite images.

The Czech National Grid Infrastructure

NASA Astrophysics Data System (ADS)

Chudoba, J.; Křenková, I.; Mulač, M.; Ruda, M.; Sitera, J.

2017-10-01

The Czech National Grid Infrastructure is operated by MetaCentrum, a CESNET department responsible for coordinating and managing activities related to distributed computing. CESNET as the Czech National Research and Education Network (NREN) provides many e-infrastructure services, which are used by 94% of the scientific and research community in the Czech Republic. Computing and storage resources owned by different organizations are connected by fast enough network to provide transparent access to all resources. We describe in more detail the computing infrastructure, which is based on several different technologies and covers grid, cloud and map-reduce environment. While the largest part of CPUs is still accessible via distributed torque servers, providing environment for long batch jobs, part of infrastructure is available via standard EGI tools in EGI, subset of NGI resources is provided into EGI FedCloud environment with cloud interface and there is also Hadoop cluster provided by the same e-infrastructure.A broad spectrum of computing servers is offered; users can choose from standard 2 CPU servers to large SMP machines with up to 6 TB of RAM or servers with GPU cards. Different groups have different priorities on various resources, resource owners can even have an exclusive access. The software is distributed via AFS. Storage servers offering up to tens of terabytes of disk space to individual users are connected via NFS4 on top of GPFS and access to long term HSM storage with peta-byte capacity is also provided. Overview of available resources and recent statistics of usage will be given.

The Namibia Early Flood Warning System, A CEOS Pilot Project

NASA Technical Reports Server (NTRS)

Mandl, Daniel; Frye, Stuart; Cappelaere, Pat; Sohlberg, Robert; Handy, Matthew; Grossman, Robert

2012-01-01

Over the past year few years, an international collaboration has developed a pilot project under the auspices of Committee on Earth Observation Satellite (CEOS) Disasters team. The overall team consists of civilian satellite agencies. For this pilot effort, the development team consists of NASA, Canadian Space Agency, Univ. of Maryland, Univ. of Colorado, Univ. of Oklahoma, Ukraine Space Research Institute and Joint Research Center(JRC) for European Commission. This development team collaborates with regional , national and international agencies to deliver end-to-end disaster coverage. In particular, the team in collaborating on this effort with the Namibia Department of Hydrology to begin in Namibia . However, the ultimate goal is to expand the functionality to provide early warning over the South Africa region. The initial collaboration was initiated by United Nations Office of Outer Space Affairs and CEOS Working Group for Information Systems and Services (WGISS). The initial driver was to demonstrate international interoperability using various space agency sensors and models along with regional in-situ ground sensors. In 2010, the team created a preliminary semi-manual system to demonstrate moving and combining key data streams and delivering the data to the Namibia Department of Hydrology during their flood season which typically is January through April. In this pilot, a variety of moderate resolution and high resolution satellite flood imagery was rapidly delivered and used in conjunction with flood predictive models in Namibia. This was collected in conjunction with ground measurements and was used to examine how to create a customized flood early warning system. During the first year, the team made use of SensorWeb technology to gather various sensor data which was used to monitor flood waves traveling down basins originating in Angola, but eventually flooding villages in Namibia. The team made use of standardized interfaces such as those articulated under the Open Cloud Consortium (OGC) Sensor Web Enablement (SWE) set of web services was good [1][2]. However, it was discovered that in order to make a system like this functional, there were many performance issues. Data sets were large and located in a variety of location behind firewalls and had to be accessed across open networks, so security was an issue. Furthermore, the network access acted as bottleneck to transfer map products to where they are needed. Finally, during disasters, many users and computer processes act in parallel and thus it was very easy to overload the single string of computers stitched together in a virtual system that was initially developed. To address some of these performance issues, the team partnered with the Open Cloud Consortium (OCC) who supplied a Computation Cloud located at the University of Illinois at Chicago and some manpower to administer this Cloud. The Flood SensorWeb [3] system was interfaced to the Cloud to provide a high performance user interface and product development engine. Figure 1 shows the functional diagram of the Flood SensorWeb. Figure 2 shows some of the functionality of the Computation Cloud that was integrated. A significant portion of the original system was ported to the Cloud and during the past year, technical issues were resolved which included web access to the Cloud, security over the open Internet, beginning experiments on how to handle surge capacity by using the virtual machines in the cloud in parallel, using tiling techniques to render large data sets as layers on map, interfaces to allow user to customize the data processing/product chain and other performance enhancing techniques. The conclusion reached from the effort and this presentation is that defining the interoperability standards in a small fraction of the work. For example, once open web service standards were defined, many users could not make use of the standards due to security restrictions. Furthermore, once an interoperable sysm is functional, then a surge of users can render a system unusable, especially in the disaster domain.

Secure and robust cloud computing for high-throughput forensic microsatellite sequence analysis and databasing.

PubMed

Bailey, Sarah F; Scheible, Melissa K; Williams, Christopher; Silva, Deborah S B S; Hoggan, Marina; Eichman, Christopher; Faith, Seth A

2017-11-01

Next-generation Sequencing (NGS) is a rapidly evolving technology with demonstrated benefits for forensic genetic applications, and the strategies to analyze and manage the massive NGS datasets are currently in development. Here, the computing, data storage, connectivity, and security resources of the Cloud were evaluated as a model for forensic laboratory systems that produce NGS data. A complete front-to-end Cloud system was developed to upload, process, and interpret raw NGS data using a web browser dashboard. The system was extensible, demonstrating analysis capabilities of autosomal and Y-STRs from a variety of NGS instrumentation (Illumina MiniSeq and MiSeq, and Oxford Nanopore MinION). NGS data for STRs were concordant with standard reference materials previously characterized with capillary electrophoresis and Sanger sequencing. The computing power of the Cloud was implemented with on-demand auto-scaling to allow multiple file analysis in tandem. The system was designed to store resulting data in a relational database, amenable to downstream sample interpretations and databasing applications following the most recent guidelines in nomenclature for sequenced alleles. Lastly, a multi-layered Cloud security architecture was tested and showed that industry standards for securing data and computing resources were readily applied to the NGS system without disadvantageous effects for bioinformatic analysis, connectivity or data storage/retrieval. The results of this study demonstrate the feasibility of using Cloud-based systems for secured NGS data analysis, storage, databasing, and multi-user distributed connectivity. Copyright © 2017 Elsevier B.V. All rights reserved.

MC-GenomeKey: a multicloud system for the detection and annotation of genomic variants.

PubMed

Elshazly, Hatem; Souilmi, Yassine; Tonellato, Peter J; Wall, Dennis P; Abouelhoda, Mohamed

2017-01-20

Next Generation Genome sequencing techniques became affordable for massive sequencing efforts devoted to clinical characterization of human diseases. However, the cost of providing cloud-based data analysis of the mounting datasets remains a concerning bottleneck for providing cost-effective clinical services. To address this computational problem, it is important to optimize the variant analysis workflow and the used analysis tools to reduce the overall computational processing time, and concomitantly reduce the processing cost. Furthermore, it is important to capitalize on the use of the recent development in the cloud computing market, which have witnessed more providers competing in terms of products and prices. In this paper, we present a new package called MC-GenomeKey (Multi-Cloud GenomeKey) that efficiently executes the variant analysis workflow for detecting and annotating mutations using cloud resources from different commercial cloud providers. Our package supports Amazon, Google, and Azure clouds, as well as, any other cloud platform based on OpenStack. Our package allows different scenarios of execution with different levels of sophistication, up to the one where a workflow can be executed using a cluster whose nodes come from different clouds. MC-GenomeKey also supports scenarios to exploit the spot instance model of Amazon in combination with the use of other cloud platforms to provide significant cost reduction. To the best of our knowledge, this is the first solution that optimizes the execution of the workflow using computational resources from different cloud providers. MC-GenomeKey provides an efficient multicloud based solution to detect and annotate mutations. The package can run in different commercial cloud platforms, which enables the user to seize the best offers. The package also provides a reliable means to make use of the low-cost spot instance model of Amazon, as it provides an efficient solution to the sudden termination of spot machines as a result of a sudden price increase. The package has a web-interface and it is available for free for academic use.

SU-E-T-628: A Cloud Computing Based Multi-Objective Optimization Method for Inverse Treatment Planning.

PubMed

Na, Y; Suh, T; Xing, L

2012-06-01

Multi-objective (MO) plan optimization entails generation of an enormous number of IMRT or VMAT plans constituting the Pareto surface, which presents a computationally challenging task. The purpose of this work is to overcome the hurdle by developing an efficient MO method using emerging cloud computing platform. As a backbone of cloud computing for optimizing inverse treatment planning, Amazon Elastic Compute Cloud with a master node (17.1 GB memory, 2 virtual cores, 420 GB instance storage, 64-bit platform) is used. The master node is able to scale seamlessly a number of working group instances, called workers, based on the user-defined setting account for MO functions in clinical setting. Each worker solved the objective function with an efficient sparse decomposition method. The workers are automatically terminated if there are finished tasks. The optimized plans are archived to the master node to generate the Pareto solution set. Three clinical cases have been planned using the developed MO IMRT and VMAT planning tools to demonstrate the advantages of the proposed method. The target dose coverage and critical structure sparing of plans are comparable obtained using the cloud computing platform are identical to that obtained using desktop PC (Intel Xeon® CPU 2.33GHz, 8GB memory). It is found that the MO planning speeds up the processing of obtaining the Pareto set substantially for both types of plans. The speedup scales approximately linearly with the number of nodes used for computing. With the use of N nodes, the computational time is reduced by the fitting model, 0.2+2.3/N, with r̂2>0.99, on average of the cases making real-time MO planning possible. A cloud computing infrastructure is developed for MO optimization. The algorithm substantially improves the speed of inverse plan optimization. The platform is valuable for both MO planning and future off- or on-line adaptive re-planning. © 2012 American Association of Physicists in Medicine.

iScreen: world's first cloud-computing web server for virtual screening and de novo drug design based on TCM database@Taiwan

NASA Astrophysics Data System (ADS)

Tsai, Tsung-Ying; Chang, Kai-Wei; Chen, Calvin Yu-Chian

2011-06-01

The rapidly advancing researches on traditional Chinese medicine (TCM) have greatly intrigued pharmaceutical industries worldwide. To take initiative in the next generation of drug development, we constructed a cloud-computing system for TCM intelligent screening system (iScreen) based on TCM Database@Taiwan. iScreen is compacted web server for TCM docking and followed by customized de novo drug design. We further implemented a protein preparation tool that both extract protein of interest from a raw input file and estimate the size of ligand bind site. In addition, iScreen is designed in user-friendly graphic interface for users who have less experience with the command line systems. For customized docking, multiple docking services, including standard, in-water, pH environment, and flexible docking modes are implemented. Users can download first 200 TCM compounds of best docking results. For TCM de novo drug design, iScreen provides multiple molecular descriptors for a user's interest. iScreen is the world's first web server that employs world's largest TCM database for virtual screening and de novo drug design. We believe our web server can lead TCM research to a new era of drug development. The TCM docking and screening server is available at http://iScreen.cmu.edu.tw/.

iScreen: world's first cloud-computing web server for virtual screening and de novo drug design based on TCM database@Taiwan.

PubMed

Tsai, Tsung-Ying; Chang, Kai-Wei; Chen, Calvin Yu-Chian

2011-06-01

The rapidly advancing researches on traditional Chinese medicine (TCM) have greatly intrigued pharmaceutical industries worldwide. To take initiative in the next generation of drug development, we constructed a cloud-computing system for TCM intelligent screening system (iScreen) based on TCM Database@Taiwan. iScreen is compacted web server for TCM docking and followed by customized de novo drug design. We further implemented a protein preparation tool that both extract protein of interest from a raw input file and estimate the size of ligand bind site. In addition, iScreen is designed in user-friendly graphic interface for users who have less experience with the command line systems. For customized docking, multiple docking services, including standard, in-water, pH environment, and flexible docking modes are implemented. Users can download first 200 TCM compounds of best docking results. For TCM de novo drug design, iScreen provides multiple molecular descriptors for a user's interest. iScreen is the world's first web server that employs world's largest TCM database for virtual screening and de novo drug design. We believe our web server can lead TCM research to a new era of drug development. The TCM docking and screening server is available at http://iScreen.cmu.edu.tw/.

Local storage federation through XRootD architecture for interactive distributed analysis

NASA Astrophysics Data System (ADS)

Colamaria, F.; Colella, D.; Donvito, G.; Elia, D.; Franco, A.; Luparello, G.; Maggi, G.; Miniello, G.; Vallero, S.; Vino, G.

2015-12-01

A cloud-based Virtual Analysis Facility (VAF) for the ALICE experiment at the LHC has been deployed in Bari. Similar facilities are currently running in other Italian sites with the aim to create a federation of interoperating farms able to provide their computing resources for interactive distributed analysis. The use of cloud technology, along with elastic provisioning of computing resources as an alternative to the grid for running data intensive analyses, is the main challenge of these facilities. One of the crucial aspects of the user-driven analysis execution is the data access. A local storage facility has the disadvantage that the stored data can be accessed only locally, i.e. from within the single VAF. To overcome such a limitation a federated infrastructure, which provides full access to all the data belonging to the federation independently from the site where they are stored, has been set up. The federation architecture exploits both cloud computing and XRootD technologies, in order to provide a dynamic, easy-to-use and well performing solution for data handling. It should allow the users to store the files and efficiently retrieve the data, since it implements a dynamic distributed cache among many datacenters in Italy connected to one another through the high-bandwidth national network. Details on the preliminary architecture implementation and performance studies are discussed.

GPU-based cloud service for Smith-Waterman algorithm using frequency distance filtration scheme.

PubMed

Lee, Sheng-Ta; Lin, Chun-Yuan; Hung, Che Lun

2013-01-01

As the conventional means of analyzing the similarity between a query sequence and database sequences, the Smith-Waterman algorithm is feasible for a database search owing to its high sensitivity. However, this algorithm is still quite time consuming. CUDA programming can improve computations efficiently by using the computational power of massive computing hardware as graphics processing units (GPUs). This work presents a novel Smith-Waterman algorithm with a frequency-based filtration method on GPUs rather than merely accelerating the comparisons yet expending computational resources to handle such unnecessary comparisons. A user friendly interface is also designed for potential cloud server applications with GPUs. Additionally, two data sets, H1N1 protein sequences (query sequence set) and human protein database (database set), are selected, followed by a comparison of CUDA-SW and CUDA-SW with the filtration method, referred to herein as CUDA-SWf. Experimental results indicate that reducing unnecessary sequence alignments can improve the computational time by up to 41%. Importantly, by using CUDA-SWf as a cloud service, this application can be accessed from any computing environment of a device with an Internet connection without time constraints.

Cross layer optimization for cloud-based radio over optical fiber networks

NASA Astrophysics Data System (ADS)

Shao, Sujie; Guo, Shaoyong; Qiu, Xuesong; Yang, Hui; Meng, Luoming

2016-07-01

To adapt the 5G communication, the cloud radio access network is a paradigm introduced by operators which aggregates all base stations computational resources into a cloud BBU pool. The interaction between RRH and BBU or resource schedule among BBUs in cloud have become more frequent and complex with the development of system scale and user requirement. It can promote the networking demand among RRHs and BBUs, and force to form elastic optical fiber switching and networking. In such network, multiple stratum resources of radio, optical and BBU processing unit have interweaved with each other. In this paper, we propose a novel multiple stratum optimization (MSO) architecture for cloud-based radio over optical fiber networks (C-RoFN) with software defined networking. Additionally, a global evaluation strategy (GES) is introduced in the proposed architecture. MSO can enhance the responsiveness to end-to-end user demands and globally optimize radio frequency, optical spectrum and BBU processing resources effectively to maximize radio coverage. The feasibility and efficiency of the proposed architecture with GES strategy are experimentally verified on OpenFlow-enabled testbed in terms of resource occupation and path provisioning latency.

MOLNs: A CLOUD PLATFORM FOR INTERACTIVE, REPRODUCIBLE, AND SCALABLE SPATIAL STOCHASTIC COMPUTATIONAL EXPERIMENTS IN SYSTEMS BIOLOGY USING PyURDME

PubMed Central

Drawert, Brian; Trogdon, Michael; Toor, Salman; Petzold, Linda; Hellander, Andreas

2017-01-01

Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools and a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments. PMID:28190948

ScipionCloud: An integrative and interactive gateway for large scale cryo electron microscopy image processing on commercial and academic clouds.

PubMed

Cuenca-Alba, Jesús; Del Cano, Laura; Gómez Blanco, Josué; de la Rosa Trevín, José Miguel; Conesa Mingo, Pablo; Marabini, Roberto; S Sorzano, Carlos Oscar; Carazo, Jose María

2017-10-01

New instrumentation for cryo electron microscopy (cryoEM) has significantly increased data collection rate as well as data quality, creating bottlenecks at the image processing level. Current image processing model of moving the acquired images from the data source (electron microscope) to desktops or local clusters for processing is encountering many practical limitations. However, computing may also take place in distributed and decentralized environments. In this way, cloud is a new form of accessing computing and storage resources on demand. Here, we evaluate on how this new computational paradigm can be effectively used by extending our current integrative framework for image processing, creating ScipionCloud. This new development has resulted in a full installation of Scipion both in public and private clouds, accessible as public "images", with all the required preinstalled cryoEM software, just requiring a Web browser to access all Graphical User Interfaces. We have profiled the performance of different configurations on Amazon Web Services and the European Federated Cloud, always on architectures incorporating GPU's, and compared them with a local facility. We have also analyzed the economical convenience of different scenarios, so cryoEM scientists have a clearer picture of the setup that is best suited for their needs and budgets. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Secure Skyline Queries on Cloud Platform.

PubMed

Liu, Jinfei; Yang, Juncheng; Xiong, Li; Pei, Jian

2017-04-01

Outsourcing data and computation to cloud server provides a cost-effective way to support large scale data storage and query processing. However, due to security and privacy concerns, sensitive data (e.g., medical records) need to be protected from the cloud server and other unauthorized users. One approach is to outsource encrypted data to the cloud server and have the cloud server perform query processing on the encrypted data only. It remains a challenging task to support various queries over encrypted data in a secure and efficient way such that the cloud server does not gain any knowledge about the data, query, and query result. In this paper, we study the problem of secure skyline queries over encrypted data. The skyline query is particularly important for multi-criteria decision making but also presents significant challenges due to its complex computations. We propose a fully secure skyline query protocol on data encrypted using semantically-secure encryption. As a key subroutine, we present a new secure dominance protocol, which can be also used as a building block for other queries. Finally, we provide both serial and parallelized implementations and empirically study the protocols in terms of efficiency and scalability under different parameter settings, verifying the feasibility of our proposed solutions.

Trust models for efficient communication in Mobile Cloud Computing and their applications to e-Commerce

NASA Astrophysics Data System (ADS)

Pop, Florin; Dobre, Ciprian; Mocanu, Bogdan-Costel; Citoteanu, Oana-Maria; Xhafa, Fatos

2016-11-01

Managing the large dimensions of data processed in distributed systems that are formed by datacentres and mobile devices has become a challenging issue with an important impact on the end-user. Therefore, the management process of such systems can be achieved efficiently by using uniform overlay networks, interconnected through secure and efficient routing protocols. The aim of this article is to advance our previous work with a novel trust model based on a reputation metric that actively uses the social links between users and the model of interaction between them. We present and evaluate an adaptive model for the trust management in structured overlay networks, based on a Mobile Cloud architecture and considering a honeycomb overlay. Such a model can be useful for supporting advanced mobile market-share e-Commerce platforms, where users collaborate and exchange reliable information about, for example, products of interest and supporting ad-hoc business campaigns

FRR: fair remote retrieval of outsourced private medical records in electronic health networks.

PubMed

Wang, Huaqun; Wu, Qianhong; Qin, Bo; Domingo-Ferrer, Josep

2014-08-01

Cloud computing is emerging as the next-generation IT architecture. However, cloud computing also raises security and privacy concerns since the users have no physical control over the outsourced data. This paper focuses on fairly retrieving encrypted private medical records outsourced to remote untrusted cloud servers in the case of medical accidents and disputes. Our goal is to enable an independent committee to fairly recover the original private medical records so that medical investigation can be carried out in a convincing way. We achieve this goal with a fair remote retrieval (FRR) model in which either t investigation committee members cooperatively retrieve the original medical data or none of them can get any information on the medical records. We realize the first FRR scheme by exploiting fair multi-member key exchange and homomorphic privately verifiable tags. Based on the standard computational Diffie-Hellman (CDH) assumption, our scheme is provably secure in the random oracle model (ROM). A detailed performance analysis and experimental results show that our scheme is efficient in terms of communication and computation. Copyright © 2014 Elsevier Inc. All rights reserved.

Geospatial Data as a Service: The GEOGLAM Rangelands and Pasture Productivity Map Experience

NASA Astrophysics Data System (ADS)

Evans, B. J. K.; Antony, J.; Guerschman, J. P.; Larraondo, P. R.; Richards, C. J.

2017-12-01

Empowering end-users like pastoralists, land management specialists and land policy makers in the use of earth observation data for both day-to-day and seasonal planning needs both interactive delivery of multiple geospatial datasets and the capability of supporting on-the-fly dynamic queries while simultaneously fostering a community around the effort. The use of and wide adoption of large data archives, like those produced by earth observation missions, are often limited by compute and storage capabilities of the remote user. We demonstrate that wide-scale use of large data archives can be facilitated by end-users dynamically requesting value-added products using open standards (WCS, WMS, WPS), with compute running in the cloud or dedicated data-centres and visualizing outputs on web-front ends. As an example, we will demonstrate how a tool called GSKY can empower a remote end-user by providing the data delivery and analytics capabilities for the GEOGLAM Rangelands and Pasture Productivity (RAPP) Map tool. The GEOGLAM RAPP initiative from the Group on Earth Observations (GEO) and its Agricultural Monitoring subgroup aims at providing practical tools to end-users focusing on the important role of rangelands and pasture systems in providing food production security from both agricultural crops and animal protein. Figure 1, is a screen capture from the RAPP Map interface for an important pasture area in the Namibian rangelands. The RAPP Map has been in production for six months and has garnered significant interest from groups and users all over the world. GSKY, being formulated around the theme of Open Geospatial Data-as-a-Service capabilities uses distributed computing and storage to facilitate this. It works behind the scenes, accepting OGC standard requests in WCS, WMS and WPS. Results from these requests are rendered on a web-front end. In this way, the complexities of data locality and compute execution are masked from an end user. On-the-fly computation of products such as NDVI, Leaf Area Index, vegetation cover and others from original source data including MODIS are achived, with Landsat and Sentinel-2 on the horizon. Innovative use of cloud computing and storage along with flexible front-ends, allow the democratization of data dissemination and we hope better outcomes for the planet.

OS2: Oblivious similarity based searching for encrypted data outsourced to an untrusted domain

PubMed Central

Pervez, Zeeshan; Ahmad, Mahmood; Khattak, Asad Masood; Ramzan, Naeem

2017-01-01

Public cloud storage services are becoming prevalent and myriad data sharing, archiving and collaborative services have emerged which harness the pay-as-you-go business model of public cloud. To ensure privacy and confidentiality often encrypted data is outsourced to such services, which further complicates the process of accessing relevant data by using search queries. Search over encrypted data schemes solve this problem by exploiting cryptographic primitives and secure indexing to identify outsourced data that satisfy the search criteria. Almost all of these schemes rely on exact matching between the encrypted data and search criteria. A few schemes which extend the notion of exact matching to similarity based search, lack realism as those schemes rely on trusted third parties or due to increase storage and computational complexity. In this paper we propose Oblivious Similarity based Search (OS2) for encrypted data. It enables authorized users to model their own encrypted search queries which are resilient to typographical errors. Unlike conventional methodologies, OS2 ranks the search results by using similarity measure offering a better search experience than exact matching. It utilizes encrypted bloom filter and probabilistic homomorphic encryption to enable authorized users to access relevant data without revealing results of search query evaluation process to the untrusted cloud service provider. Encrypted bloom filter based search enables OS2 to reduce search space to potentially relevant encrypted data avoiding unnecessary computation on public cloud. The efficacy of OS2 is evaluated on Google App Engine for various bloom filter lengths on different cloud configurations. PMID:28692697

[Formula: see text]: Oblivious similarity based searching for encrypted data outsourced to an untrusted domain.

PubMed

Pervez, Zeeshan; Ahmad, Mahmood; Khattak, Asad Masood; Ramzan, Naeem; Khan, Wajahat Ali

2017-01-01

Public cloud storage services are becoming prevalent and myriad data sharing, archiving and collaborative services have emerged which harness the pay-as-you-go business model of public cloud. To ensure privacy and confidentiality often encrypted data is outsourced to such services, which further complicates the process of accessing relevant data by using search queries. Search over encrypted data schemes solve this problem by exploiting cryptographic primitives and secure indexing to identify outsourced data that satisfy the search criteria. Almost all of these schemes rely on exact matching between the encrypted data and search criteria. A few schemes which extend the notion of exact matching to similarity based search, lack realism as those schemes rely on trusted third parties or due to increase storage and computational complexity. In this paper we propose Oblivious Similarity based Search ([Formula: see text]) for encrypted data. It enables authorized users to model their own encrypted search queries which are resilient to typographical errors. Unlike conventional methodologies, [Formula: see text] ranks the search results by using similarity measure offering a better search experience than exact matching. It utilizes encrypted bloom filter and probabilistic homomorphic encryption to enable authorized users to access relevant data without revealing results of search query evaluation process to the untrusted cloud service provider. Encrypted bloom filter based search enables [Formula: see text] to reduce search space to potentially relevant encrypted data avoiding unnecessary computation on public cloud. The efficacy of [Formula: see text] is evaluated on Google App Engine for various bloom filter lengths on different cloud configurations.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vang, Leng; Prescott, Steven R; Smith, Curtis

In collaborating scientific research arena it is important to have an environment where analysts have access to a shared of information documents, software tools and be able to accurately maintain and track historical changes in models. A new cloud-based environment would be accessible remotely from anywhere regardless of computing platforms given that the platform has available of Internet access and proper browser capabilities. Information stored at this environment would be restricted based on user assigned credentials. This report reviews development of a Cloud-based Architecture Capabilities (CAC) as a web portal for PRA tools.

Protecting genomic data analytics in the cloud: state of the art and opportunities.

PubMed

Tang, Haixu; Jiang, Xiaoqian; Wang, Xiaofeng; Wang, Shuang; Sofia, Heidi; Fox, Dov; Lauter, Kristin; Malin, Bradley; Telenti, Amalio; Xiong, Li; Ohno-Machado, Lucila

2016-10-13

The outsourcing of genomic data into public cloud computing settings raises concerns over privacy and security. Significant advancements in secure computation methods have emerged over the past several years, but such techniques need to be rigorously evaluated for their ability to support the analysis of human genomic data in an efficient and cost-effective manner. With respect to public cloud environments, there are concerns about the inadvertent exposure of human genomic data to unauthorized users. In analyses involving multiple institutions, there is additional concern about data being used beyond agreed research scope and being prcoessed in untrused computational environments, which may not satisfy institutional policies. To systematically investigate these issues, the NIH-funded National Center for Biomedical Computing iDASH (integrating Data for Analysis, 'anonymization' and SHaring) hosted the second Critical Assessment of Data Privacy and Protection competition to assess the capacity of cryptographic technologies for protecting computation over human genomes in the cloud and promoting cross-institutional collaboration. Data scientists were challenged to design and engineer practical algorithms for secure outsourcing of genome computation tasks in working software, whereby analyses are performed only on encrypted data. They were also challenged to develop approaches to enable secure collaboration on data from genomic studies generated by multiple organizations (e.g., medical centers) to jointly compute aggregate statistics without sharing individual-level records. The results of the competition indicated that secure computation techniques can enable comparative analysis of human genomes, but greater efficiency (in terms of compute time and memory utilization) are needed before they are sufficiently practical for real world environments.

Mobile cloud-computing-based healthcare service by noncontact ECG monitoring.

PubMed

Fong, Ee-May; Chung, Wan-Young

2013-12-02

Noncontact electrocardiogram (ECG) measurement technique has gained popularity these days owing to its noninvasive features and convenience in daily life use. This paper presents mobile cloud computing for a healthcare system where a noncontact ECG measurement method is employed to capture biomedical signals from users. Healthcare service is provided to continuously collect biomedical signals from multiple locations. To observe and analyze the ECG signals in real time, a mobile device is used as a mobile monitoring terminal. In addition, a personalized healthcare assistant is installed on the mobile device; several healthcare features such as health status summaries, medication QR code scanning, and reminders are integrated into the mobile application. Health data are being synchronized into the healthcare cloud computing service (Web server system and Web server dataset) to ensure a seamless healthcare monitoring system and anytime and anywhere coverage of network connection is available. Together with a Web page application, medical data are easily accessed by medical professionals or family members. Web page performance evaluation was conducted to ensure minimal Web server latency. The system demonstrates better availability of off-site and up-to-the-minute patient data, which can help detect health problems early and keep elderly patients out of the emergency room, thus providing a better and more comprehensive healthcare cloud computing service.

Mobile Cloud-Computing-Based Healthcare Service by Noncontact ECG Monitoring

PubMed Central

Fong, Ee-May; Chung, Wan-Young

2013-01-01

Noncontact electrocardiogram (ECG) measurement technique has gained popularity these days owing to its noninvasive features and convenience in daily life use. This paper presents mobile cloud computing for a healthcare system where a noncontact ECG measurement method is employed to capture biomedical signals from users. Healthcare service is provided to continuously collect biomedical signals from multiple locations. To observe and analyze the ECG signals in real time, a mobile device is used as a mobile monitoring terminal. In addition, a personalized healthcare assistant is installed on the mobile device; several healthcare features such as health status summaries, medication QR code scanning, and reminders are integrated into the mobile application. Health data are being synchronized into the healthcare cloud computing service (Web server system and Web server dataset) to ensure a seamless healthcare monitoring system and anytime and anywhere coverage of network connection is available. Together with a Web page application, medical data are easily accessed by medical professionals or family members. Web page performance evaluation was conducted to ensure minimal Web server latency. The system demonstrates better availability of off-site and up-to-the-minute patient data, which can help detect health problems early and keep elderly patients out of the emergency room, thus providing a better and more comprehensive healthcare cloud computing service. PMID:24316562

OpenTopography: Addressing Big Data Challenges Using Cloud Computing, HPC, and Data Analytics

NASA Astrophysics Data System (ADS)

Crosby, C. J.; Nandigam, V.; Phan, M.; Youn, C.; Baru, C.; Arrowsmith, R.

2014-12-01

OpenTopography (OT) is a geoinformatics-based data facility initiated in 2009 for democratizing access to high-resolution topographic data, derived products, and tools. Hosted at the San Diego Supercomputer Center (SDSC), OT utilizes cyberinfrastructure, including large-scale data management, high-performance computing, and service-oriented architectures to provide efficient Web based access to large, high-resolution topographic datasets. OT collocates data with processing tools to enable users to quickly access custom data and derived products for their application. OT's ongoing R&D efforts aim to solve emerging technical challenges associated with exponential growth in data, higher order data products, as well as user base. Optimization of data management strategies can be informed by a comprehensive set of OT user access metrics that allows us to better understand usage patterns with respect to the data. By analyzing the spatiotemporal access patterns within the datasets, we can map areas of the data archive that are highly active (hot) versus the ones that are rarely accessed (cold). This enables us to architect a tiered storage environment consisting of high performance disk storage (SSD) for the hot areas and less expensive slower disk for the cold ones, thereby optimizing price to performance. From a compute perspective, OT is looking at cloud based solutions such as the Microsoft Azure platform to handle sudden increases in load. An OT virtual machine image in Microsoft's VM Depot can be invoked and deployed quickly in response to increased system demand. OT has also integrated SDSC HPC systems like the Gordon supercomputer into our infrastructure tier to enable compute intensive workloads like parallel computation of hydrologic routing on high resolution topography. This capability also allows OT to scale to HPC resources during high loads to meet user demand and provide more efficient processing. With a growing user base and maturing scientific user community comes new requests for algorithms and processing capabilities. To address this demand, OT is developing an extensible service based architecture for integrating community-developed software. This "plugable" approach to Web service deployment will enable new processing and analysis tools to run collocated with OT hosted data.

VDJServer: A Cloud-Based Analysis Portal and Data Commons for Immune Repertoire Sequences and Rearrangements.

PubMed

Christley, Scott; Scarborough, Walter; Salinas, Eddie; Rounds, William H; Toby, Inimary T; Fonner, John M; Levin, Mikhail K; Kim, Min; Mock, Stephen A; Jordan, Christopher; Ostmeyer, Jared; Buntzman, Adam; Rubelt, Florian; Davila, Marco L; Monson, Nancy L; Scheuermann, Richard H; Cowell, Lindsay G

2018-01-01

Recent technological advances in immune repertoire sequencing have created tremendous potential for advancing our understanding of adaptive immune response dynamics in various states of health and disease. Immune repertoire sequencing produces large, highly complex data sets, however, which require specialized methods and software tools for their effective analysis and interpretation. VDJServer is a cloud-based analysis portal for immune repertoire sequence data that provide access to a suite of tools for a complete analysis workflow, including modules for preprocessing and quality control of sequence reads, V(D)J gene segment assignment, repertoire characterization, and repertoire comparison. VDJServer also provides sophisticated visualizations for exploratory analysis. It is accessible through a standard web browser via a graphical user interface designed for use by immunologists, clinicians, and bioinformatics researchers. VDJServer provides a data commons for public sharing of repertoire sequencing data, as well as private sharing of data between users. We describe the main functionality and architecture of VDJServer and demonstrate its capabilities with use cases from cancer immunology and autoimmunity. VDJServer provides a complete analysis suite for human and mouse T-cell and B-cell receptor repertoire sequencing data. The combination of its user-friendly interface and high-performance computing allows large immune repertoire sequencing projects to be analyzed with no programming or software installation required. VDJServer is a web-accessible cloud platform that provides access through a graphical user interface to a data management infrastructure, a collection of analysis tools covering all steps in an analysis, and an infrastructure for sharing data along with workflows, results, and computational provenance. VDJServer is a free, publicly available, and open-source licensed resource.

Using the cloud to speed-up calibration of watershed-scale hydrologic models (Invited)

NASA Astrophysics Data System (ADS)

Goodall, J. L.; Ercan, M. B.; Castronova, A. M.; Humphrey, M.; Beekwilder, N.; Steele, J.; Kim, I.

2013-12-01

This research focuses on using the cloud to address computational challenges associated with hydrologic modeling. One example is calibration of a watershed-scale hydrologic model, which can take days of execution time on typical computers. While parallel algorithms for model calibration exist and some researchers have used multi-core computers or clusters to run these algorithms, these solutions do not fully address the challenge because (i) calibration can still be too time consuming even on multicore personal computers and (ii) few in the community have the time and expertise needed to manage a compute cluster. Given this, another option for addressing this challenge that we are exploring through this work is the use of the cloud for speeding-up calibration of watershed-scale hydrologic models. The cloud used in this capacity provides a means for renting a specific number and type of machines for only the time needed to perform a calibration model run. The cloud allows one to precisely balance the duration of the calibration with the financial costs so that, if the budget allows, the calibration can be performed more quickly by renting more machines. Focusing specifically on the SWAT hydrologic model and a parallel version of the DDS calibration algorithm, we show significant speed-up time across a range of watershed sizes using up to 256 cores to perform a model calibration. The tool provides a simple web-based user interface and the ability to monitor the calibration job submission process during the calibration process. Finally this talk concludes with initial work to leverage the cloud for other tasks associated with hydrologic modeling including tasks related to preparing inputs for constructing place-based hydrologic models.

Healthcare professionals' use of health clouds: Integrating technology acceptance and status quo bias perspectives.

PubMed

Hsieh, Pi-Jung

2015-07-01

Cloud computing technology has recently been seen as an important milestone in medical informatics development. Despite its great potential, there are gaps in our understanding of how users evaluate change in relation to the health cloud and how they decide to resist it. Integrating technology acceptance and status quo bias perspectives, this study develops an integrated model to explain healthcare professionals' intention to use the health cloud service and their intention to resist it. A field survey was conducted in Taiwan to collect data from healthcare professionals; a structural equation model was used to examine the data. A valid sample of 209 healthcare professionals was collected for data analysis. The results show that healthcare professionals' resistance to the use of the health cloud is the result of regret avoidance, inertia, perceived value, switching costs, and perceived threat. Attitude, subjective norm, and perceived behavior control are shown to have positive and direct effects on healthcare professionals' intention to use the health cloud. The results also indicate a significant negative effect in the relationship between healthcare professionals' intention and resistance to using the health cloud. Our study illustrates the importance of incorporating user resistance in technology acceptance studies in general and in health technology usage studies in particular. This study also identifies key factors for practitioners and hospitals to make adoption decisions in relation to the health cloud. Further, the study provides a useful reference for future studies in this subject field. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

User friendly IT Services for Monitoring and Prevention during Pregnancy.

PubMed

Crişan-Vida, Mihaela; Serban, Alexandru; Ghihor-Izdrăilă, Ioana; Mirea, Adrian; Stoicu-Tivadar, Lacramioara

2014-01-01

A healthy lifestyle for a mother and monitoring both mother and fetus activities are crucial factors for a normal pregnancy without hazardous conditions. This paper proposes a cloud computing solution and a mobile application which collect data from the sensors to be used in Obstetrics-Gynecology Department. This application monitors the dietary plan of the pregnant and gives her the possibility to socialize and share pregnancy experience with the rest of women from the social network from the hospital. The physicians can access the information's of the patient in real time and they can alert mothers in some situations. Using this cloud computing device, the health condition of the pregnant women may be improved.

Planning and management of cloud computing networks

NASA Astrophysics Data System (ADS)

Larumbe, Federico

The evolution of the Internet has a great impact on a big part of the population. People use it to communicate, query information, receive news, work, and as entertainment. Its extraordinary usefulness as a communication media made the number of applications and technological resources explode. However, that network expansion comes at the cost of an important power consumption. If the power consumption of telecommunication networks and data centers is considered as the power consumption of a country, it would rank at the 5 th place in the world. Furthermore, the number of servers in the world is expected to grow by a factor of 10 between 2013 and 2020. This context motivates us to study techniques and methods to allocate cloud computing resources in an optimal way with respect to cost, quality of service (QoS), power consumption, and environmental impact. The results we obtained from our test cases show that besides minimizing capital expenditures (CAPEX) and operational expenditures (OPEX), the response time can be reduced up to 6 times, power consumption by 30%, and CO2 emissions by a factor of 60. Cloud computing provides dynamic access to IT resources as a service. In this paradigm, programs are executed in servers connected to the Internet that users access from their computers and mobile devices. The first advantage of this architecture is to reduce the time of application deployment and interoperability, because a new user only needs a web browser and does not need to install software on local computers with specific operating systems. Second, applications and information are available from everywhere and with any device with an Internet access. Also, servers and IT resources can be dynamically allocated depending on the number of users and workload, a feature called elasticity. This thesis studies the resource management of cloud computing networks and is divided in three main stages. We start by analyzing the planning of cloud computing networks to get a comprehensive vision. The first question to be solved is what are the optimal data center locations. We found that the location of each data center has a big impact on cost, QoS, power consumption, and greenhouse gas emissions. An optimization problem with a multi-criteria objective function is proposed to decide jointly the optimal location of data centers and software components, link capacities, and information routing. Once the network planning has been analyzed, the problem of dynamic resource provisioning in real time is addressed. In this context, virtualization is a key technique in cloud computing because each server can be shared by multiple Virtual Machines (VMs) and the total power consumption can be reduced. In the same line of location problems, we propose a Green Cloud Broker that optimizes VM placement across multiple data centers. In fact, when multiple data centers are considered, response time can be reduced by placing VMs close to users, cost can be minimized, power consumption can be optimized by using energy efficient data centers, and CO2 emissions can be decreased by choosing data centers provided with renewable energy sources. The third stage of the analysis is the short-term management of a cloud data center. In particular, a method is proposed to assign VMs to servers by considering communication traffic among VMs. Cloud data centers receive new applications over time and these applications need on-demand resource provisioning. Each application is composed of multiple types of VMs that interact among themselves. A program called scheduler must place each new VM in a server and that impacts the QoS and power consumption. Our method places VMs that communicate among themselves in servers that are close to each other in the network topology, thus reducing communication delay and increasing the throughput available among VMs. Furthermore, the power consumption of each type of server is considered and the most efficient ones are chosen to place the VMs. The number of VMs of each application can be dynamically changed to match the workload and servers not needed in a particular period can be suspended to save energy. The methodology developed is based on Mixed Integer Programming (MIP) models to formalize the problems and use state of the art optimization solvers. Then, heuristics are developed to solve cases with more than 1,000 potential data center locations for the planning problem, 1,000 nodes for the cloud broker, and 128,000 servers for the VM placement problem. Solutions with very short optimality gaps, between 0% and 1.95%, are obtained, and execution time in the order of minutes for the planning problem and less than a second for real time cases. We consider that this thesis on resource provisioning of cloud computing networks includes important contributions on this research area, and innovative commercial applications based on the proposed methods have promising future.

Unidata Cyberinfrastructure in the Cloud

NASA Astrophysics Data System (ADS)

Ramamurthy, M. K.; Young, J. W.

2016-12-01

Data services, software, and user support are critical components of geosciences cyber-infrastructure to help researchers to advance science. With the maturity of and significant advances in cloud computing, it has recently emerged as an alternative new paradigm for developing and delivering a broad array of services over the Internet. Cloud computing is now mature enough in usability in many areas of science and education, bringing the benefits of virtualized and elastic remote services to infrastructure, software, computation, and data. Cloud environments reduce the amount of time and money spent to procure, install, and maintain new hardware and software, and reduce costs through resource pooling and shared infrastructure. Given the enormous potential of cloud-based services, Unidata has been moving to augment its software, services, data delivery mechanisms to align with the cloud-computing paradigm. To realize the above vision, Unidata has worked toward: * Providing access to many types of data from a cloud (e.g., via the THREDDS Data Server, RAMADDA and EDEX servers); * Deploying data-proximate tools to easily process, analyze, and visualize those data in a cloud environment cloud for consumption by any one, by any device, from anywhere, at any time; * Developing and providing a range of pre-configured and well-integrated tools and services that can be deployed by any university in their own private or public cloud settings. Specifically, Unidata has developed Docker for "containerized applications", making them easy to deploy. Docker helps to create "disposable" installs and eliminates many configuration challenges. Containerized applications include tools for data transport, access, analysis, and visualization: THREDDS Data Server, Integrated Data Viewer, GEMPAK, Local Data Manager, RAMADDA Data Server, and Python tools; * Leveraging Jupyter as a central platform and hub with its powerful set of interlinking tools to connect interactively data servers, Python scientific libraries, scripts, and workflows; * Exploring end-to-end modeling and prediction capabilities in the cloud; * Partnering with NOAA and public cloud vendors (e.g., Amazon and OCC) on the NOAA Big Data Project to harness their capabilities and resources for the benefit of the academic community.

EGI-EUDAT integration activity - Pair data and high-throughput computing resources together

NASA Astrophysics Data System (ADS)

Scardaci, Diego; Viljoen, Matthew; Vitlacil, Dejan; Fiameni, Giuseppe; Chen, Yin; sipos, Gergely; Ferrari, Tiziana

2016-04-01

EGI (www.egi.eu) is a publicly funded e-infrastructure put together to give scientists access to more than 530,000 logical CPUs, 200 PB of disk capacity and 300 PB of tape storage to drive research and innovation in Europe. The infrastructure provides both high throughput computing and cloud compute/storage capabilities. Resources are provided by about 350 resource centres which are distributed across 56 countries in Europe, the Asia-Pacific region, Canada and Latin America. EUDAT (www.eudat.eu) is a collaborative Pan-European infrastructure providing research data services, training and consultancy for researchers, research communities, research infrastructures and data centres. EUDAT's vision is to enable European researchers and practitioners from any research discipline to preserve, find, access, and process data in a trusted environment, as part of a Collaborative Data Infrastructure (CDI) conceived as a network of collaborating, cooperating centres, combining the richness of numerous community-specific data repositories with the permanence and persistence of some of Europe's largest scientific data centres. EGI and EUDAT, in the context of their flagship projects, EGI-Engage and EUDAT2020, started in March 2015 a collaboration to harmonise the two infrastructures, including technical interoperability, authentication, authorisation and identity management, policy and operations. The main objective of this work is to provide end-users with a seamless access to an integrated infrastructure offering both EGI and EUDAT services and, then, pairing data and high-throughput computing resources together. To define the roadmap of this collaboration, EGI and EUDAT selected a set of relevant user communities, already collaborating with both infrastructures, which could bring requirements and help to assign the right priorities to each of them. In this way, from the beginning, this activity has been really driven by the end users. The identified user communities are relevant European Research infrastructure in the field of Earth Science (EPOS and ICOS), Bioinformatics (BBMRI and ELIXIR) and Space Physics (EISCAT-3D). The first outcome of this activity has been the definition of a generic use case that captures the typical user scenario with respect the integrated use of the EGI and EUDAT infrastructures. This generic use case allows a user to instantiate a set of Virtual Machine images on the EGI Federated Cloud to perform computational jobs that analyse data previously stored on EUDAT long-term storage systems. The results of such analysis can be staged back to EUDAT storages, and if needed, allocated with Permanent identifyers (PIDs) for future use. The implementation of this generic use case requires the following integration activities between EGI and EUDAT: (1) harmonisation of the user authentication and authorisation models, (2) implementing interface connectors between the relevant EGI and EUDAT services, particularly EGI Cloud compute facilities and EUDAT long-term storage and PID systems. In the presentation, the collected user requirements and the implementation status of the universal use case will be showed. Furthermore, how the universal use case is currently applied to satisfy EPOS and ICOS needs will be described.

On localization attacks against cloud infrastructure

NASA Astrophysics Data System (ADS)

Ge, Linqiang; Yu, Wei; Sistani, Mohammad Ali

2013-05-01

One of the key characteristics of cloud computing is the device and location independence that enables the user to access systems regardless of their location. Because cloud computing is heavily based on sharing resource, it is vulnerable to cyber attacks. In this paper, we investigate a localization attack that enables the adversary to leverage central processing unit (CPU) resources to localize the physical location of server used by victims. By increasing and reducing CPU usage through the malicious virtual machine (VM), the response time from the victim VM will increase and decrease correspondingly. In this way, by embedding the probing signal into the CPU usage and correlating the same pattern in the response time from the victim VM, the adversary can find the location of victim VM. To determine attack accuracy, we investigate features in both the time and frequency domains. We conduct both theoretical and experimental study to demonstrate the effectiveness of such an attack.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lingerfelt, Eric J; Endeve, Eirik; Hui, Yawei

Improvements in scientific instrumentation allow imaging at mesoscopic to atomic length scales, many spectroscopic modes, and now--with the rise of multimodal acquisition systems and the associated processing capability--the era of multidimensional, informationally dense data sets has arrived. Technical issues in these combinatorial scientific fields are exacerbated by computational challenges best summarized as a necessity for drastic improvement in the capability to transfer, store, and analyze large volumes of data. The Bellerophon Environment for Analysis of Materials (BEAM) platform provides material scientists the capability to directly leverage the integrated computational and analytical power of High Performance Computing (HPC) to perform scalablemore » data analysis and simulation and manage uploaded data files via an intuitive, cross-platform client user interface. This framework delivers authenticated, "push-button" execution of complex user workflows that deploy data analysis algorithms and computational simulations utilizing compute-and-data cloud infrastructures and HPC environments like Titan at the Oak Ridge Leadershp Computing Facility (OLCF).« less

A Development of Lightweight Grid Interface

NASA Astrophysics Data System (ADS)

Iwai, G.; Kawai, Y.; Sasaki, T.; Watase, Y.

2011-12-01

In order to help a rapid development of Grid/Cloud aware applications, we have developed API to abstract the distributed computing infrastructures based on SAGA (A Simple API for Grid Applications). SAGA, which is standardized in the OGF (Open Grid Forum), defines API specifications to access distributed computing infrastructures, such as Grid, Cloud and local computing resources. The Universal Grid API (UGAPI), which is a set of command line interfaces (CLI) and APIs, aims to offer simpler API to combine several SAGA interfaces with richer functionalities. These CLIs of the UGAPI offer typical functionalities required by end users for job management and file access to the different distributed computing infrastructures as well as local computing resources. We have also built a web interface for the particle therapy simulation and demonstrated the large scale calculation using the different infrastructures at the same time. In this paper, we would like to present how the web interface based on UGAPI and SAGA achieve more efficient utilization of computing resources over the different infrastructures with technical details and practical experiences.

A Comparative Study of Point Cloud Data Collection and Processing

NASA Astrophysics Data System (ADS)

Pippin, J. E.; Matheney, M.; Gentle, J. N., Jr.; Pierce, S. A.; Fuentes-Pineda, G.

2016-12-01

Over the past decade, there has been dramatic growth in the acquisition of publicly funded high-resolution topographic data for scientific, environmental, engineering and planning purposes. These data sets are valuable for applications of interest across a large and varied user community. However, because of the large volumes of data produced by high-resolution mapping technologies and expense of aerial data collection, it is often difficult to collect and distribute these datasets. Furthermore, the data can be technically challenging to process, requiring software and computing resources not readily available to many users. This study presents a comparison of advanced computing hardware and software that is used to collect and process point cloud datasets, such as LIDAR scans. Activities included implementation and testing of open source libraries and applications for point cloud data processing such as, Meshlab, Blender, PDAL, and PCL. Additionally, a suite of commercial scale applications, Skanect and Cloudcompare, were applied to raw datasets. Handheld hardware solutions, a Structure Scanner and Xbox 360 Kinect V1, were tested for their ability to scan at three field locations. The resultant data projects successfully scanned and processed subsurface karst features ranging from small stalactites to large rooms, as well as a surface waterfall feature. Outcomes support the feasibility of rapid sensing in 3D at field scales.

Open NASA Earth Exchange (OpenNEX): Strategies for enabling cross organization collaboration in the earth sciences

NASA Astrophysics Data System (ADS)

Michaelis, A.; Ganguly, S.; Nemani, R. R.; Votava, P.; Wang, W.; Lee, T. J.; Dungan, J. L.

2014-12-01

Sharing community-valued codes, intermediary datasets and results from individual efforts with others that are not in a direct funded collaboration can be a challenge. Cross organization collaboration is often impeded due to infrastructure security constraints, rigid financial controls, bureaucracy, and workforce nationalities, etc., which can force groups to work in a segmented fashion and/or through awkward and suboptimal web services. We show how a focused community may come together, share modeling and analysis codes, computing configurations, scientific results, knowledge and expertise on a public cloud platform; diverse groups of researchers working together at "arms length". Through the OpenNEX experimental workshop, users can view short technical "how-to" videos and explore encapsulated working environment. Workshop participants can easily instantiate Amazon Machine Images (AMI) or launch full cluster and data processing configurations within minutes. Enabling users to instantiate computing environments from configuration templates on large public cloud infrastructures, such as Amazon Web Services, may provide a mechanism for groups to easily use each others work and collaborate indirectly. Moreover, using the public cloud for this workshop allowed a single group to host a large read only data archive, making datasets of interest to the community widely available on the public cloud, enabling other groups to directly connect to the data and reduce the costs of the collaborative work by freeing other individual groups from redundantly retrieving, integrating or financing the storage of the datasets of interest.

Calibration of radio-astronomical data on the cloud. LOFAR, the pathway to SKA

NASA Astrophysics Data System (ADS)

Sabater, J.; Sánchez-Expósito, S.; Garrido, J.; Ruiz, J. E.; Best, P. N.; Verdes-Montenegro, L.

2015-05-01

The radio interferometer LOFAR (LOw Frequency ARray) is fully operational now. This Square Kilometre Array (SKA) pathfinder allows the observation of the sky at frequencies between 10 and 240 MHz, a relatively unexplored region of the spectrum. LOFAR is a software defined telescope: the data is mainly processed using specialized software running in common computing facilities. That means that the capabilities of the telescope are virtually defined by software and mainly limited by the available computing power. However, the quantity of data produced can quickly reach huge volumes (several Petabytes per day). After the correlation and pre-processing of the data in a dedicated cluster, the final dataset is handled to the user (typically several Terabytes). The calibration of these data requires a powerful computing facility in which the specific state of the art software under heavy continuous development can be easily installed and updated. That makes this case a perfect candidate for a cloud infrastructure which adds the advantages of an on demand, flexible solution. We present our approach to the calibration of LOFAR data using Ibercloud, the cloud infrastructure provided by Ibergrid. With the calibration work-flow adapted to the cloud, we can explore calibration strategies for the SKA and show how private or commercial cloud infrastructures (Ibercloud, Amazon EC2, Google Compute Engine, etc.) can help to solve the problems with big datasets that will be prevalent in the future of astronomy.

Basic Techniques in Environmental Simulation.

DTIC Science & Technology

1982-07-01

the devel- ’I or oper is liable for all necessary changes in the model or its supporting computer software . After the 90-day warranty expires, the user...processing unit, that part of a computer which accom- plishes arithmetic and logical operations DCFLOS Dynamic cloud -free line-of-sight, a simulation... Software Development ......... 12 1.7.7 Operational Environment, Interfaces, and Constraints. . 12 1.7.8 Effectiveness Evaluation, Value Analysis, and

Dynamic Collaboration Infrastructure for Hydrologic Science

NASA Astrophysics Data System (ADS)

Tarboton, D. G.; Idaszak, R.; Castillo, C.; Yi, H.; Jiang, F.; Jones, N.; Goodall, J. L.

2016-12-01

Data and modeling infrastructure is becoming increasingly accessible to water scientists. HydroShare is a collaborative environment that currently offers water scientists the ability to access modeling and data infrastructure in support of data intensive modeling and analysis. It supports the sharing of and collaboration around "resources" which are social objects defined to include both data and models in a structured standardized format. Users collaborate around these objects via comments, ratings, and groups. HydroShare also supports web services and cloud based computation for the execution of hydrologic models and analysis and visualization of hydrologic data. However, the quantity and variety of data and modeling infrastructure available that can be accessed from environments like HydroShare is increasing. Storage infrastructure can range from one's local PC to campus or organizational storage to storage in the cloud. Modeling or computing infrastructure can range from one's desktop to departmental clusters to national HPC resources to grid and cloud computing resources. How does one orchestrate this vast number of data and computing infrastructure without needing to correspondingly learn each new system? A common limitation across these systems is the lack of efficient integration between data transport mechanisms and the corresponding high-level services to support large distributed data and compute operations. A scientist running a hydrology model from their desktop may require processing a large collection of files across the aforementioned storage and compute resources and various national databases. To address these community challenges a proof-of-concept prototype was created integrating HydroShare with RADII (Resource Aware Data-centric collaboration Infrastructure) to provide software infrastructure to enable the comprehensive and rapid dynamic deployment of what we refer to as "collaborative infrastructure." In this presentation we discuss the results of this proof-of-concept prototype which enabled HydroShare users to readily instantiate virtual infrastructure marshaling arbitrary combinations, varieties, and quantities of distributed data and computing infrastructure in addressing big problems in hydrology.

Virtual Geophysics Laboratory: Exploiting the Cloud and Empowering Geophysicsts

NASA Astrophysics Data System (ADS)

Fraser, Ryan; Vote, Josh; Goh, Richard; Cox, Simon

2013-04-01

Over the last five decades geoscientists from Australian state and federal agencies have collected and assembled around 3 Petabytes of geoscience data sets under public funding. As a consequence of technological progress, data is now being acquired at exponential rates and in higher resolution than ever before. Effective use of these big data sets challenges the storage and computational infrastructure of most organizations. The Virtual Geophysics Laboratory (VGL) is a scientific workflow portal addresses some of the resulting issues by providing Australian geophysicists with access to a Web 2.0 or Rich Internet Application (RIA) based integrated environment that exploits eResearch tools and Cloud computing technology, and promotes collaboration between the user community. VGL simplifies and automates large portions of what were previously manually intensive scientific workflow processes, allowing scientists to focus on the natural science problems, rather than computer science and IT. A number of geophysical processing codes are incorporated to support multiple workflows. For example a gravity inversion can be performed by combining the Escript/Finley codes (from the University of Queensland) with the gravity data registered in VGL. Likewise, tectonic processes can also be modeled by combining the Underworld code (from Monash University) with one of the various 3D models available to VGL. Cloud services provide scalable and cost effective compute resources. VGL is built on top of mature standards-compliant information services, many deployed using the Spatial Information Services Stack (SISS), which provides direct access to geophysical data. A large number of data sets from Geoscience Australia assist users in data discovery. GeoNetwork provides a metadata catalog to store workflow results for future use, discovery and provenance tracking. VGL has been developed in collaboration with the research community using incremental software development practices and open source tools. While developed to provide the geophysics research community with a sustainable platform and scalable infrastructure; VGL has also developed a number of concepts, patterns and generic components of which have been reused for cases beyond geophysics, including natural hazards, satellite processing and other areas requiring spatial data discovery and processing. Future plans for VGL include a number of improvements in both functional and non-functional areas in response to its user community needs and advancement in information technologies. In particular, research is underway in the following areas (a) distributed and parallel workflow processing in the cloud, (b) seamless integration with various cloud providers, and (c) integration with virtual laboratories representing other science domains. Acknowledgements: VGL was developed by CSIRO in collaboration with Geoscience Australia, National Computational Infrastructure, Australia National University, Monash University and University of Queensland, and has been supported by the Australian Government's Education Investment Funds through NeCTAR.

Performance evaluation of multi-stratum resources optimization with network functions virtualization for cloud-based radio over optical fiber networks.

PubMed

Yang, Hui; He, Yongqi; Zhang, Jie; Ji, Yuefeng; Bai, Wei; Lee, Young

2016-04-18

Cloud radio access network (C-RAN) has become a promising scenario to accommodate high-performance services with ubiquitous user coverage and real-time cloud computing using cloud BBUs. In our previous work, we implemented cross stratum optimization of optical network and application stratums resources that allows to accommodate the services in optical networks. In view of this, this study extends to consider the multiple dimensional resources optimization of radio, optical and BBU processing in 5G age. We propose a novel multi-stratum resources optimization (MSRO) architecture with network functions virtualization for cloud-based radio over optical fiber networks (C-RoFN) using software defined control. A global evaluation scheme (GES) for MSRO in C-RoFN is introduced based on the proposed architecture. The MSRO can enhance the responsiveness to dynamic end-to-end user demands and globally optimize radio frequency, optical and BBU resources effectively to maximize radio coverage. The efficiency and feasibility of the proposed architecture are experimentally demonstrated on OpenFlow-based enhanced SDN testbed. The performance of GES under heavy traffic load scenario is also quantitatively evaluated based on MSRO architecture in terms of resource occupation rate and path provisioning latency, compared with other provisioning scheme.

The Globus Galaxies Platform. Delivering Science Gateways as a Service

DOE Office of Scientific and Technical Information (OSTI.GOV)

Madduri, Ravi; Chard, Kyle; Chard, Ryan

We use public cloud computers to host sophisticated scientific data; software is then used to transform scientific practice by enabling broad access to capabilities previously available only to the few. The primary obstacle to more widespread use of public clouds to host scientific software (‘cloud-based science gateways’) has thus far been the considerable gap between the specialized needs of science applications and the capabilities provided by cloud infrastructures. We describe here a domain-independent, cloud-based science gateway platform, the Globus Galaxies platform, which overcomes this gap by providing a set of hosted services that directly address the needs of science gatewaymore » developers. The design and implementation of this platform leverages our several years of experience with Globus Genomics, a cloud-based science gateway that has served more than 200 genomics researchers across 30 institutions. Building on that foundation, we have also implemented a platform that leverages the popular Galaxy system for application hosting and workflow execution; Globus services for data transfer, user and group management, and authentication; and a cost-aware elastic provisioning model specialized for public cloud resources. We describe here the capabilities and architecture of this platform, present six scientific domains in which we have successfully applied it, report on user experiences, and analyze the economics of our deployments. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.« less

Analyzing the requirements for a robust security criteria and management of multi-level security in the clouds

NASA Astrophysics Data System (ADS)

Farroha, Bassam S.; Farroha, Deborah L.

2011-06-01

The new corporate approach to efficient processing and storage is migrating from in-house service-center services to the newly coined approach of Cloud Computing. This approach advocates thin clients and providing services by the service provider over time-shared resources. The concept is not new, however the implementation approach presents a strategic shift in the way organizations provision and manage their IT resources. The requirements on some of the data sets targeted to be run on the cloud vary depending on the data type, originator, user, and confidentiality level. Additionally, the systems that fuse such data would have to deal with the classifying the product and clearing the computing resources prior to allowing new application to be executed. This indicates that we could end up with a multi-level security system that needs to follow specific rules and can send the output to a protected network and systems in order not to have data spill or contaminated resources. The paper discusses these requirements and potential impact on the cloud architecture. Additionally, the paper discusses the unexpected advantages of the cloud framework providing a sophisticated environment for information sharing and data mining.

Secure Skyline Queries on Cloud Platform

PubMed Central

Liu, Jinfei; Yang, Juncheng; Xiong, Li; Pei, Jian

2017-01-01

Outsourcing data and computation to cloud server provides a cost-effective way to support large scale data storage and query processing. However, due to security and privacy concerns, sensitive data (e.g., medical records) need to be protected from the cloud server and other unauthorized users. One approach is to outsource encrypted data to the cloud server and have the cloud server perform query processing on the encrypted data only. It remains a challenging task to support various queries over encrypted data in a secure and efficient way such that the cloud server does not gain any knowledge about the data, query, and query result. In this paper, we study the problem of secure skyline queries over encrypted data. The skyline query is particularly important for multi-criteria decision making but also presents significant challenges due to its complex computations. We propose a fully secure skyline query protocol on data encrypted using semantically-secure encryption. As a key subroutine, we present a new secure dominance protocol, which can be also used as a building block for other queries. Finally, we provide both serial and parallelized implementations and empirically study the protocols in terms of efficiency and scalability under different parameter settings, verifying the feasibility of our proposed solutions. PMID:28883710

Context-aware distributed cloud computing using CloudScheduler

NASA Astrophysics Data System (ADS)

Seuster, R.; Leavett-Brown, CR; Casteels, K.; Driemel, C.; Paterson, M.; Ring, D.; Sobie, RJ; Taylor, RP; Weldon, J.

2017-10-01

The distributed cloud using the CloudScheduler VM provisioning service is one of the longest running systems for HEP workloads. It has run millions of jobs for ATLAS and Belle II over the past few years using private and commercial clouds around the world. Our goal is to scale the distributed cloud to the 10,000-core level, with the ability to run any type of application (low I/O, high I/O and high memory) on any cloud. To achieve this goal, we have been implementing changes that utilize context-aware computing designs that are currently employed in the mobile communication industry. Context-awareness makes use of real-time and archived data to respond to user or system requirements. In our distributed cloud, we have many opportunistic clouds with no local HEP services, software or storage repositories. A context-aware design significantly improves the reliability and performance of our system by locating the nearest location of the required services. We describe how we are collecting and managing contextual information from our workload management systems, the clouds, the virtual machines and our services. This information is used not only to monitor the system but also to carry out automated corrective actions. We are incrementally adding new alerting and response services to our distributed cloud. This will enable us to scale the number of clouds and virtual machines. Further, a context-aware design will enable us to run analysis or high I/O application on opportunistic clouds. We envisage an open-source HTTP data federation (for example, the DynaFed system at CERN) as a service that would provide us access to existing storage elements used by the HEP experiments.

SU-D-BRD-02: A Web-Based Image Processing and Plan Evaluation Platform (WIPPEP) for Future Cloud-Based Radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chai, X; Liu, L; Xing, L

Purpose: Visualization and processing of medical images and radiation treatment plan evaluation have traditionally been constrained to local workstations with limited computation power and ability of data sharing and software update. We present a web-based image processing and planning evaluation platform (WIPPEP) for radiotherapy applications with high efficiency, ubiquitous web access, and real-time data sharing. Methods: This software platform consists of three parts: web server, image server and computation server. Each independent server communicates with each other through HTTP requests. The web server is the key component that provides visualizations and user interface through front-end web browsers and relay informationmore » to the backend to process user requests. The image server serves as a PACS system. The computation server performs the actual image processing and dose calculation. The web server backend is developed using Java Servlets and the frontend is developed using HTML5, Javascript, and jQuery. The image server is based on open source DCME4CHEE PACS system. The computation server can be written in any programming language as long as it can send/receive HTTP requests. Our computation server was implemented in Delphi, Python and PHP, which can process data directly or via a C++ program DLL. Results: This software platform is running on a 32-core CPU server virtually hosting the web server, image server, and computation servers separately. Users can visit our internal website with Chrome browser, select a specific patient, visualize image and RT structures belonging to this patient and perform image segmentation running Delphi computation server and Monte Carlo dose calculation on Python or PHP computation server. Conclusion: We have developed a webbased image processing and plan evaluation platform prototype for radiotherapy. This system has clearly demonstrated the feasibility of performing image processing and plan evaluation platform through a web browser and exhibited potential for future cloud based radiotherapy.« less

BisQue: cloud-based system for management, annotation, visualization, analysis and data mining of underwater and remote sensing imagery

NASA Astrophysics Data System (ADS)

Fedorov, D.; Miller, R. J.; Kvilekval, K. G.; Doheny, B.; Sampson, S.; Manjunath, B. S.

2016-02-01

Logistical and financial limitations of underwater operations are inherent in marine science, including biodiversity observation. Imagery is a promising way to address these challenges, but the diversity of organisms thwarts simple automated analysis. Recent developments in computer vision methods, such as convolutional neural networks (CNN), are promising for automated classification and detection tasks but are typically very computationally expensive and require extensive training on large datasets. Therefore, managing and connecting distributed computation, large storage and human annotations of diverse marine datasets is crucial for effective application of these methods. BisQue is a cloud-based system for management, annotation, visualization, analysis and data mining of underwater and remote sensing imagery and associated data. Designed to hide the complexity of distributed storage, large computational clusters, diversity of data formats and inhomogeneous computational environments behind a user friendly web-based interface, BisQue is built around an idea of flexible and hierarchical annotations defined by the user. Such textual and graphical annotations can describe captured attributes and the relationships between data elements. Annotations are powerful enough to describe cells in fluorescent 4D images, fish species in underwater videos and kelp beds in aerial imagery. Presently we are developing BisQue-based analysis modules for automated identification of benthic marine organisms. Recent experiments with drop-out and CNN based classification of several thousand annotated underwater images demonstrated an overall accuracy above 70% for the 15 best performing species and above 85% for the top 5 species. Based on these promising results, we have extended bisque with a CNN-based classification system allowing continuous training on user-provided data.

Framework Development Supporting the Safety Portal

DOE Office of Scientific and Technical Information (OSTI.GOV)

Prescott, Steven Ralph; Kvarfordt, Kellie Jean; Vang, Leng

2015-07-01

In a collaborating scientific research arena it is important to have an environment where analysts have access to a shared repository of information, documents, and software tools, and be able to accurately maintain and track historical changes in models. The new Safety Portal cloud-based environment will be accessible remotely from anywhere regardless of computing platforms given that the platform has available Internet access and proper browser capabilities. Information stored at this environment would be restricted based on user assigned credentials. This report discusses current development of a cloud-based web portal for PRA tools.

Scientific Data Storage for Cloud Computing

NASA Astrophysics Data System (ADS)

Readey, J.

2014-12-01

Traditionally data storage used for geophysical software systems has centered on file-based systems and libraries such as NetCDF and HDF5. In contrast cloud based infrastructure providers such as Amazon AWS, Microsoft Azure, and the Google Cloud Platform generally provide storage technologies based on an object based storage service (for large binary objects) complemented by a database service (for small objects that can be represented as key-value pairs). These systems have been shown to be highly scalable, reliable, and cost effective. We will discuss a proposed system that leverages these cloud-based storage technologies to provide an API-compatible library for traditional NetCDF and HDF5 applications. This system will enable cloud storage suitable for geophysical applications that can scale up to petabytes of data and thousands of users. We'll also cover other advantages of this system such as enhanced metadata search.

Impact of different cloud deployments on real-time video applications for mobile video cloud users

NASA Astrophysics Data System (ADS)

Khan, Kashif A.; Wang, Qi; Luo, Chunbo; Wang, Xinheng; Grecos, Christos

2015-02-01

The latest trend to access mobile cloud services through wireless network connectivity has amplified globally among both entrepreneurs and home end users. Although existing public cloud service vendors such as Google, Microsoft Azure etc. are providing on-demand cloud services with affordable cost for mobile users, there are still a number of challenges to achieve high-quality mobile cloud based video applications, especially due to the bandwidth-constrained and errorprone mobile network connectivity, which is the communication bottleneck for end-to-end video delivery. In addition, existing accessible clouds networking architectures are different in term of their implementation, services, resources, storage, pricing, support and so on, and these differences have varied impact on the performance of cloud-based real-time video applications. Nevertheless, these challenges and impacts have not been thoroughly investigated in the literature. In our previous work, we have implemented a mobile cloud network model that integrates localized and decentralized cloudlets (mini-clouds) and wireless mesh networks. In this paper, we deploy a real-time framework consisting of various existing Internet cloud networking architectures (Google Cloud, Microsoft Azure and Eucalyptus Cloud) and a cloudlet based on Ubuntu Enterprise Cloud over wireless mesh networking technology for mobile cloud end users. It is noted that the increasing trend to access real-time video streaming over HTTP/HTTPS is gaining popularity among both research and industrial communities to leverage the existing web services and HTTP infrastructure in the Internet. To study the performance under different deployments using different public and private cloud service providers, we employ real-time video streaming over the HTTP/HTTPS standard, and conduct experimental evaluation and in-depth comparative analysis of the impact of different deployments on the quality of service for mobile video cloud users. Empirical results are presented and discussed to quantify and explain the different impacts resulted from various cloud deployments, video application and wireless/mobile network setting, and user mobility. Additionally, this paper analyses the advantages, disadvantages, limitations and optimization techniques in various cloud networking deployments, in particular the cloudlet approach compared with the Internet cloud approach, with recommendations of optimized deployments highlighted. Finally, federated clouds and inter-cloud collaboration challenges and opportunities are discussed in the context of supporting real-time video applications for mobile users.

Infrastructure Systems for Advanced Computing in E-science applications

NASA Astrophysics Data System (ADS)

Terzo, Olivier

2013-04-01

In the e-science field are growing needs for having computing infrastructure more dynamic and customizable with a model of use "on demand" that follow the exact request in term of resources and storage capacities. The integration of grid and cloud infrastructure solutions allows us to offer services that can adapt the availability in terms of up scaling and downscaling resources. The main challenges for e-sciences domains will on implement infrastructure solutions for scientific computing that allow to adapt dynamically the demands of computing resources with a strong emphasis on optimizing the use of computing resources for reducing costs of investments. Instrumentation, data volumes, algorithms, analysis contribute to increase the complexity for applications who require high processing power and storage for a limited time and often exceeds the computational resources that equip the majority of laboratories, research Unit in an organization. Very often it is necessary to adapt or even tweak rethink tools, algorithms, and consolidate existing applications through a phase of reverse engineering in order to adapt them to a deployment on Cloud infrastructure. For example, in areas such as rainfall monitoring, meteorological analysis, Hydrometeorology, Climatology Bioinformatics Next Generation Sequencing, Computational Electromagnetic, Radio occultation, the complexity of the analysis raises several issues such as the processing time, the scheduling of tasks of processing, storage of results, a multi users environment. For these reasons, it is necessary to rethink the writing model of E-Science applications in order to be already adapted to exploit the potentiality of cloud computing services through the uses of IaaS, PaaS and SaaS layer. An other important focus is on create/use hybrid infrastructure typically a federation between Private and public cloud, in fact in this way when all resources owned by the organization are all used it will be easy with a federate cloud infrastructure to add some additional resources form the Public cloud for following the needs in term of computational and storage resources and release them where process are finished. Following the hybrid model, the scheduling approach is important for managing both cloud models. Thanks to this model infrastructure every time resources are available for additional request in term of IT capacities that can used "on demand" for a limited time without having to proceed to purchase additional servers.

Load balancing prediction method of cloud storage based on analytic hierarchy process and hybrid hierarchical genetic algorithm.

PubMed

Zhou, Xiuze; Lin, Fan; Yang, Lvqing; Nie, Jing; Tan, Qian; Zeng, Wenhua; Zhang, Nian

2016-01-01

With the continuous expansion of the cloud computing platform scale and rapid growth of users and applications, how to efficiently use system resources to improve the overall performance of cloud computing has become a crucial issue. To address this issue, this paper proposes a method that uses an analytic hierarchy process group decision (AHPGD) to evaluate the load state of server nodes. Training was carried out by using a hybrid hierarchical genetic algorithm (HHGA) for optimizing a radial basis function neural network (RBFNN). The AHPGD makes the aggregative indicator of virtual machines in cloud, and become input parameters of predicted RBFNN. Also, this paper proposes a new dynamic load balancing scheduling algorithm combined with a weighted round-robin algorithm, which uses the predictive periodical load value of nodes based on AHPPGD and RBFNN optimized by HHGA, then calculates the corresponding weight values of nodes and makes constant updates. Meanwhile, it keeps the advantages and avoids the shortcomings of static weighted round-robin algorithm.

An imperialist competitive algorithm for virtual machine placement in cloud computing

NASA Astrophysics Data System (ADS)

Jamali, Shahram; Malektaji, Sepideh; Analoui, Morteza

2017-05-01

Cloud computing, the recently emerged revolution in IT industry, is empowered by virtualisation technology. In this paradigm, the user's applications run over some virtual machines (VMs). The process of selecting proper physical machines to host these virtual machines is called virtual machine placement. It plays an important role on resource utilisation and power efficiency of cloud computing environment. In this paper, we propose an imperialist competitive-based algorithm for the virtual machine placement problem called ICA-VMPLC. The base optimisation algorithm is chosen to be ICA because of its ease in neighbourhood movement, good convergence rate and suitable terminology. The proposed algorithm investigates search space in a unique manner to efficiently obtain optimal placement solution that simultaneously minimises power consumption and total resource wastage. Its final solution performance is compared with several existing methods such as grouping genetic and ant colony-based algorithms as well as bin packing heuristic. The simulation results show that the proposed method is superior to other tested algorithms in terms of power consumption, resource wastage, CPU usage efficiency and memory usage efficiency.

Effective user management with high strength crypto -key in dynamic group environment in cloud

NASA Astrophysics Data System (ADS)

Kumar, P. J.; Suganya, P.; Karthik, G.

2017-11-01

Cloud Clusters consists of various collections of files which are being accessed by multiple users of Cloud. The users are managed as a group and the association of the user to a particular group is dynamic in nature. Every group has a manager who handles the membership of a user to a particular group by issuing keys for encryption and decryption. Due to the dynamic nature of a user he/she may leave the group very frequently. But an attempt can be made by the user who has recently left the group to access a file maintained by that group. Key distribution becomes a critical issue while the behavior of the user is dynamic. Existing techniques to manage the users of group in terms of security and key distribution has been investigated so that to arrive at an objective to identify the scopes to increase security and key management scheme in cloud. The usage of various key combinations to measure the strength of security and efficiency of user management in dynamic cloud environment has been investigated.

A Hybrid Scheme for Fine-Grained Search and Access Authorization in Fog Computing Environment

PubMed Central

Xiao, Min; Zhou, Jing; Liu, Xuejiao; Jiang, Mingda

2017-01-01

In the fog computing environment, the encrypted sensitive data may be transferred to multiple fog nodes on the edge of a network for low latency; thus, fog nodes need to implement a search over encrypted data as a cloud server. Since the fog nodes tend to provide service for IoT applications often running on resource-constrained end devices, it is necessary to design lightweight solutions. At present, there is little research on this issue. In this paper, we propose a fine-grained owner-forced data search and access authorization scheme spanning user-fog-cloud for resource constrained end users. Compared to existing schemes only supporting either index encryption with search ability or data encryption with fine-grained access control ability, the proposed hybrid scheme supports both abilities simultaneously, and index ciphertext and data ciphertext are constructed based on a single ciphertext-policy attribute based encryption (CP-ABE) primitive and share the same key pair, thus the data access efficiency is significantly improved and the cost of key management is greatly reduced. Moreover, in the proposed scheme, the resource constrained end devices are allowed to rapidly assemble ciphertexts online and securely outsource most of decryption task to fog nodes, and mediated encryption mechanism is also adopted to achieve instantaneous user revocation instead of re-encrypting ciphertexts with many copies in many fog nodes. The security and the performance analysis show that our scheme is suitable for a fog computing environment. PMID:28629131

A Hybrid Scheme for Fine-Grained Search and Access Authorization in Fog Computing Environment.

PubMed

Xiao, Min; Zhou, Jing; Liu, Xuejiao; Jiang, Mingda

2017-06-17

In the fog computing environment, the encrypted sensitive data may be transferred to multiple fog nodes on the edge of a network for low latency; thus, fog nodes need to implement a search over encrypted data as a cloud server. Since the fog nodes tend to provide service for IoT applications often running on resource-constrained end devices, it is necessary to design lightweight solutions. At present, there is little research on this issue. In this paper, we propose a fine-grained owner-forced data search and access authorization scheme spanning user-fog-cloud for resource constrained end users. Compared to existing schemes only supporting either index encryption with search ability or data encryption with fine-grained access control ability, the proposed hybrid scheme supports both abilities simultaneously, and index ciphertext and data ciphertext are constructed based on a single ciphertext-policy attribute based encryption (CP-ABE) primitive and share the same key pair, thus the data access efficiency is significantly improved and the cost of key management is greatly reduced. Moreover, in the proposed scheme, the resource constrained end devices are allowed to rapidly assemble ciphertexts online and securely outsource most of decryption task to fog nodes, and mediated encryption mechanism is also adopted to achieve instantaneous user revocation instead of re-encrypting ciphertexts with many copies in many fog nodes. The security and the performance analysis show that our scheme is suitable for a fog computing environment.

Pervasive Knowledge, Social Networks, and Cloud Computing: E-Learning 2.0

ERIC Educational Resources Information Center

Anshari, Muhammad; Alas, Yabit; Guan, Lim Sei

2015-01-01

Embedding Web 2.0 in learning processes has extended learning from traditional based learning-centred to a collaborative based learning-centred institution that emphasises learning anywhere and anytime. While deploying Semantic Web into e-learning offers a broader spectrum of pervasive knowledge acquisition to enrich users' experience in learning.…

Exploring Students' Acceptance of Team Messaging Services: The Roles of Social Presence and Motivation

ERIC Educational Resources Information Center

Huang, Yong-Ming

2017-01-01

Team messaging services represent a type of cloud computing applications that support not only the messaging among users but also the collaboration in a team. Accordingly, team messaging services have great potential to facilitate students' collaboration. However, only few studies utilized such services to support students' collaboration and…

Google Wave: Collaboration Reworked

ERIC Educational Resources Information Center

Rethlefsen, Melissa L.

2010-01-01

Over the past several years, Internet users have become accustomed to Web 2.0 and cloud computing-style applications. It's commonplace and even intuitive to drag and drop gadgets on personalized start pages, to comment on a Facebook post without reloading the page, and to compose and save documents through a web browser. The web paradigm has…

Going behind IT's Back

ERIC Educational Resources Information Center

Schaffhauser, Dian

2013-01-01

The pendulum of technology control in higher education has swung away from central IT toward the users. It has become easier for individuals and departments to find their own computing solutions via mobile apps, the cloud, BYOD, web services, and other means. As a result, IT can often find itself out of the loop in certain technology decisions.…

Novel Techniques for Secure Use of Public Cloud Computing Resources

DTIC Science & Technology

2015-09-17

applications such as Twitter and Instagram . It allows users to post messages to their public feed, where any followers of that feed will receive them...86]) and topic based systems, this work focuses on a topic based systems. Due to the recent popularity of social networks such as Twitter and Instagram

A Mobility Management Using Follow-Me Cloud-Cloudlet in Fog-Computing-Based RANs for Smart Cities.

PubMed

Chen, Yuh-Shyan; Tsai, Yi-Ting

2018-02-06

Mobility management for supporting the location tracking and location-based service (LBS) is an important issue of smart city by providing the means for the smooth transportation of people and goods. The mobility is useful to contribute the innovation in both public and private transportation infrastructures for smart cities. With the assistance of edge/fog computing, this paper presents a fully new mobility management using the proposed follow-me cloud-cloudlet (FMCL) approach in fog-computing-based radio access networks (Fog-RANs) for smart cities. The proposed follow-me cloud-cloudlet approach is an integration strategy of follow-me cloud (FMC) and follow-me edge (FME) (or called cloudlet). A user equipment (UE) receives the data, transmitted from original cloud, into the original edge cloud before the handover operation. After the handover operation, an UE searches for a new cloud, called as a migrated cloud, and a new edge cloud, called as a migrated edge cloud near to UE, where the remaining data is migrated from the original cloud to the migrated cloud and all the remaining data are received in the new edge cloud. Existing FMC results do not have the property of the VM migration between cloudlets for the purpose of reducing the transmission latency, and existing FME results do not keep the property of the service migration between data centers for reducing the transmission latency. Our proposed FMCL approach can simultaneously keep the VM migration between cloudlets and service migration between data centers to significantly reduce the transmission latency. The new proposed mobility management using FMCL approach aims to reduce the total transmission time if some data packets are pre-scheduled and pre-stored into the cache of cloudlet if UE is switching from the previous Fog-RAN to the serving Fog-RAN. To illustrate the performance achievement, the mathematical analysis and simulation results are examined in terms of the total transmission time, the throughput, the probability of packet loss, and the number of control messages.

A Mobility Management Using Follow-Me Cloud-Cloudlet in Fog-Computing-Based RANs for Smart Cities

PubMed Central

Tsai, Yi-Ting

2018-01-01

Mobility management for supporting the location tracking and location-based service (LBS) is an important issue of smart city by providing the means for the smooth transportation of people and goods. The mobility is useful to contribute the innovation in both public and private transportation infrastructures for smart cities. With the assistance of edge/fog computing, this paper presents a fully new mobility management using the proposed follow-me cloud-cloudlet (FMCL) approach in fog-computing-based radio access networks (Fog-RANs) for smart cities. The proposed follow-me cloud-cloudlet approach is an integration strategy of follow-me cloud (FMC) and follow-me edge (FME) (or called cloudlet). A user equipment (UE) receives the data, transmitted from original cloud, into the original edge cloud before the handover operation. After the handover operation, an UE searches for a new cloud, called as a migrated cloud, and a new edge cloud, called as a migrated edge cloud near to UE, where the remaining data is migrated from the original cloud to the migrated cloud and all the remaining data are received in the new edge cloud. Existing FMC results do not have the property of the VM migration between cloudlets for the purpose of reducing the transmission latency, and existing FME results do not keep the property of the service migration between data centers for reducing the transmission latency. Our proposed FMCL approach can simultaneously keep the VM migration between cloudlets and service migration between data centers to significantly reduce the transmission latency. The new proposed mobility management using FMCL approach aims to reduce the total transmission time if some data packets are pre-scheduled and pre-stored into the cache of cloudlet if UE is switching from the previous Fog-RAN to the serving Fog-RAN. To illustrate the performance achievement, the mathematical analysis and simulation results are examined in terms of the total transmission time, the throughput, the probability of packet loss, and the number of control messages. PMID:29415510

A case study for cloud based high throughput analysis of NGS data using the globus genomics system

DOE PAGES

Bhuvaneshwar, Krithika; Sulakhe, Dinanath; Gauba, Robinder; ...

2015-01-01

Next generation sequencing (NGS) technologies produce massive amounts of data requiring a powerful computational infrastructure, high quality bioinformatics software, and skilled personnel to operate the tools. We present a case study of a practical solution to this data management and analysis challenge that simplifies terabyte scale data handling and provides advanced tools for NGS data analysis. These capabilities are implemented using the “Globus Genomics” system, which is an enhanced Galaxy workflow system made available as a service that offers users the capability to process and transfer data easily, reliably and quickly to address end-to-end NGS analysis requirements. The Globus Genomicsmore » system is built on Amazon's cloud computing infrastructure. The system takes advantage of elastic scaling of compute resources to run multiple workflows in parallel and it also helps meet the scale-out analysis needs of modern translational genomics research.« less

A case study for cloud based high throughput analysis of NGS data using the globus genomics system

PubMed Central

Bhuvaneshwar, Krithika; Sulakhe, Dinanath; Gauba, Robinder; Rodriguez, Alex; Madduri, Ravi; Dave, Utpal; Lacinski, Lukasz; Foster, Ian; Gusev, Yuriy; Madhavan, Subha

2014-01-01

Next generation sequencing (NGS) technologies produce massive amounts of data requiring a powerful computational infrastructure, high quality bioinformatics software, and skilled personnel to operate the tools. We present a case study of a practical solution to this data management and analysis challenge that simplifies terabyte scale data handling and provides advanced tools for NGS data analysis. These capabilities are implemented using the “Globus Genomics” system, which is an enhanced Galaxy workflow system made available as a service that offers users the capability to process and transfer data easily, reliably and quickly to address end-to-endNGS analysis requirements. The Globus Genomics system is built on Amazon 's cloud computing infrastructure. The system takes advantage of elastic scaling of compute resources to run multiple workflows in parallel and it also helps meet the scale-out analysis needs of modern translational genomics research. PMID:26925205

Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology

PubMed Central

Zao, John K.; Gan, Tchin-Tze; You, Chun-Kai; Chung, Cheng-En; Wang, Yu-Te; Rodríguez Méndez, Sergio José; Mullen, Tim; Yu, Chieh; Kothe, Christian; Hsiao, Ching-Teng; Chu, San-Liang; Shieh, Ce-Kuen; Jung, Tzyy-Ping

2014-01-01

EEG-based Brain-computer interfaces (BCI) are facing basic challenges in real-world applications. The technical difficulties in developing truly wearable BCI systems that are capable of making reliable real-time prediction of users' cognitive states in dynamic real-life situations may seem almost insurmountable at times. Fortunately, recent advances in miniature sensors, wireless communication and distributed computing technologies offered promising ways to bridge these chasms. In this paper, we report an attempt to develop a pervasive on-line EEG-BCI system using state-of-art technologies including multi-tier Fog and Cloud Computing, semantic Linked Data search, and adaptive prediction/classification models. To verify our approach, we implement a pilot system by employing wireless dry-electrode EEG headsets and MEMS motion sensors as the front-end devices, Android mobile phones as the personal user interfaces, compact personal computers as the near-end Fog Servers and the computer clusters hosted by the Taiwan National Center for High-performance Computing (NCHC) as the far-end Cloud Servers. We succeeded in conducting synchronous multi-modal global data streaming in March and then running a multi-player on-line EEG-BCI game in September, 2013. We are currently working with the ARL Translational Neuroscience Branch to use our system in real-life personal stress monitoring and the UCSD Movement Disorder Center to conduct in-home Parkinson's disease patient monitoring experiments. We shall proceed to develop the necessary BCI ontology and introduce automatic semantic annotation and progressive model refinement capability to our system. PMID:24917804

Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology.

PubMed

Zao, John K; Gan, Tchin-Tze; You, Chun-Kai; Chung, Cheng-En; Wang, Yu-Te; Rodríguez Méndez, Sergio José; Mullen, Tim; Yu, Chieh; Kothe, Christian; Hsiao, Ching-Teng; Chu, San-Liang; Shieh, Ce-Kuen; Jung, Tzyy-Ping

2014-01-01

EEG-based Brain-computer interfaces (BCI) are facing basic challenges in real-world applications. The technical difficulties in developing truly wearable BCI systems that are capable of making reliable real-time prediction of users' cognitive states in dynamic real-life situations may seem almost insurmountable at times. Fortunately, recent advances in miniature sensors, wireless communication and distributed computing technologies offered promising ways to bridge these chasms. In this paper, we report an attempt to develop a pervasive on-line EEG-BCI system using state-of-art technologies including multi-tier Fog and Cloud Computing, semantic Linked Data search, and adaptive prediction/classification models. To verify our approach, we implement a pilot system by employing wireless dry-electrode EEG headsets and MEMS motion sensors as the front-end devices, Android mobile phones as the personal user interfaces, compact personal computers as the near-end Fog Servers and the computer clusters hosted by the Taiwan National Center for High-performance Computing (NCHC) as the far-end Cloud Servers. We succeeded in conducting synchronous multi-modal global data streaming in March and then running a multi-player on-line EEG-BCI game in September, 2013. We are currently working with the ARL Translational Neuroscience Branch to use our system in real-life personal stress monitoring and the UCSD Movement Disorder Center to conduct in-home Parkinson's disease patient monitoring experiments. We shall proceed to develop the necessary BCI ontology and introduce automatic semantic annotation and progressive model refinement capability to our system.

Future Approach to tier-0 extension

NASA Astrophysics Data System (ADS)

Jones, B.; McCance, G.; Cordeiro, C.; Giordano, D.; Traylen, S.; Moreno García, D.

2017-10-01

The current tier-0 processing at CERN is done on two managed sites, the CERN computer centre and the Wigner computer centre. With the proliferation of public cloud resources at increasingly competitive prices, we have been investigating how to transparently increase our compute capacity to include these providers. The approach taken has been to integrate these resources using our existing deployment and computer management tools and to provide them in a way that exposes them to users as part of the same site. The paper will describe the architecture, the toolset and the current production experiences of this model.

Cloud Computing Fundamentals

NASA Astrophysics Data System (ADS)

Furht, Borko

In the introductory chapter we define the concept of cloud computing and cloud services, and we introduce layers and types of cloud computing. We discuss the differences between cloud computing and cloud services. New technologies that enabled cloud computing are presented next. We also discuss cloud computing features, standards, and security issues. We introduce the key cloud computing platforms, their vendors, and their offerings. We discuss cloud computing challenges and the future of cloud computing.

CloVR-Comparative: automated, cloud-enabled comparative microbial genome sequence analysis pipeline.

PubMed

Agrawal, Sonia; Arze, Cesar; Adkins, Ricky S; Crabtree, Jonathan; Riley, David; Vangala, Mahesh; Galens, Kevin; Fraser, Claire M; Tettelin, Hervé; White, Owen; Angiuoli, Samuel V; Mahurkar, Anup; Fricke, W Florian

2017-04-27

The benefit of increasing genomic sequence data to the scientific community depends on easy-to-use, scalable bioinformatics support. CloVR-Comparative combines commonly used bioinformatics tools into an intuitive, automated, and cloud-enabled analysis pipeline for comparative microbial genomics. CloVR-Comparative runs on annotated complete or draft genome sequences that are uploaded by the user or selected via a taxonomic tree-based user interface and downloaded from NCBI. CloVR-Comparative runs reference-free multiple whole-genome alignments to determine unique, shared and core coding sequences (CDSs) and single nucleotide polymorphisms (SNPs). Output includes short summary reports and detailed text-based results files, graphical visualizations (phylogenetic trees, circular figures), and a database file linked to the Sybil comparative genome browser. Data up- and download, pipeline configuration and monitoring, and access to Sybil are managed through CloVR-Comparative web interface. CloVR-Comparative and Sybil are distributed as part of the CloVR virtual appliance, which runs on local computers or the Amazon EC2 cloud. Representative datasets (e.g. 40 draft and complete Escherichia coli genomes) are processed in <36 h on a local desktop or at a cost of <$20 on EC2. CloVR-Comparative allows anybody with Internet access to run comparative genomics projects, while eliminating the need for on-site computational resources and expertise.

Attribute based encryption for secure sharing of E-health data

NASA Astrophysics Data System (ADS)

Charanya, R.; Nithya, S.; Manikandan, N.

2017-11-01

Distributed computing is one of the developing innovations in IT part and information security assumes a real part. It includes sending gathering of remote server and programming that permit the unified information and online access to PC administrations. Distributed computing depends on offering of asset among different clients are additionally progressively reallocated on interest. Cloud computing is a revolutionary computing paradigm which enables flexible, on-demand and low-cost usage of computing resources. The reasons for security and protection issues, which rise on the grounds that the health information possessed by distinctive clients are put away in some cloud servers rather than under their own particular control”z. To deal with security problems, various schemes based on the Attribute-Based Encryption have been proposed. In this paper, in order to make ehealth data’s more secure we use multi party in cloud computing system. Where the health data is encrypted using attributes and key policy. And the user with a particular attribute and key policy alone will be able to decrypt the health data after it is verified by “key distribution centre” and the “secure data distributor”. This technique can be used in medical field for secure storage of patient details and limiting to particular doctor access. To make data’s scalable secure we need to encrypt the health data before outsourcing.

e-Collaboration for Earth observation (E-CEO): the Cloud4SAR interferometry data challenge

NASA Astrophysics Data System (ADS)

Casu, Francesco; Manunta, Michele; Boissier, Enguerran; Brito, Fabrice; Aas, Christina; Lavender, Samantha; Ribeiro, Rita; Farres, Jordi

2014-05-01

The e-Collaboration for Earth Observation (E-CEO) project addresses the technologies and architectures needed to provide a collaborative research Platform for automating data mining and processing, and information extraction experiments. The Platform serves for the implementation of Data Challenge Contests focusing on Information Extraction for Earth Observations (EO) applications. The possibility to implement multiple processors within a Common Software Environment facilitates the validation, evaluation and transparent peer comparison among different methodologies, which is one of the main requirements rose by scientists who develop algorithms in the EO field. In this scenario, we set up a Data Challenge, referred to as Cloud4SAR (http://wiki.services.eoportal.org/tiki-index.php?page=ECEO), to foster the deployment of Interferometric SAR (InSAR) processing chains within a Cloud Computing platform. While a large variety of InSAR processing software tools are available, they require a high level of expertise and a complex user interaction to be effectively run. Computing a co-seismic interferogram or a 20-years deformation time series on a volcanic area are not easy tasks to be performed in a fully unsupervised way and/or in very short time (hours or less). Benefiting from ESA's E-CEO platform, participants can optimise algorithms on a Virtual Sandbox environment without being expert programmers, and compute results on high performing Cloud platforms. Cloud4SAR requires solving a relatively easy InSAR problem by trying to maximize the exploitation of the processing capabilities provided by a Cloud Computing infrastructure. The proposed challenge offers two different frameworks, each dedicated to participants with different skills, identified as Beginners and Experts. For both of them, the contest mainly resides in the degree of automation of the deployed algorithms, no matter which one is used, as well as in the capability of taking effective benefit from a parallel computing environment.

A Cloud Robotics Based Service for Managing RPAS in Emergency, Rescue and Hazardous Scenarios

NASA Astrophysics Data System (ADS)

Silvagni, Mario; Chiaberge, Marcello; Sanguedolce, Claudio; Dara, Gianluca

2016-04-01

Cloud robotics and cloud services are revolutionizing not only the ICT world but also the robotics industry, giving robots more computing capabilities, storage and connection bandwidth while opening new scenarios that blend the physical to the digital world. In this vision, new IT architectures are required to manage robots, retrieve data from them and create services to interact with users. Among all the robots this work is mainly focused on flying robots, better known as drones, UAV (Unmanned Aerial Vehicle) or RPAS (Remotely Piloted Aircraft Systems). The cloud robotics approach shifts the concept of having a single local "intelligence" for every single UAV, as a unique device that carries out onboard all the computation and storage processes, to a more powerful "centralized brain" located in the cloud. This breakthrough opens new scenarios where UAVs are agents, relying on remote servers for most of their computational load and data storage, creating a network of devices where they can share knowledge and information. Many applications, using UAVs, are growing as interesting and suitable devices for environment monitoring. Many services can be build fetching data from UAVs, such as telemetry, video streaming, pictures or sensors data; once. These services, part of the IT architecture, can be accessed via web by other devices or shared with other UAVs. As test cases of the proposed architecture, two examples are reported. In the first one a search and rescue or emergency management, where UAVs are required for monitoring intervention, is shown. In case of emergency or aggression, the user requests the emergency service from the IT architecture, providing GPS coordinates and an identification number. The IT architecture uses a UAV (choosing among the available one according to distance, service status, etc.) to reach him/her for monitoring and support operations. In the meantime, an officer will use the service to see the current position of the UAV, its telemetry and video streaming from its camera. Data are stored for further use and documentation and can be shared to all the involved personal or services. The second case refer to imaging survey. An investigation area is selected using a map or a set of coordinates by a user that can be on the field on in a management facility. The cloud system elaborate this data and automatically compute a flight plan that consider the survey data requirements (i.e: picture ground resolution, overlapping) but also several environment constraints (i.e: no fly zones, possible hazardous areas, known obstacles, etc). Once the flight plan is loaded in the selected UAV the mission starts. During the mission, if a suitable data network coverage is available, the UAV transmit acquired images (typically low quality image to limit bandwidth) and shooting pose in order to perform a preliminary check during the mission and minimize failing in survey; if not, all data are uploaded asynchronously after the mission. The cloud servers perform all the tasks related to image processing (mosaic, ortho-photo, geo-referencing, 3D models) and data management.

Stochastic Simulation Service: Bridging the Gap between the Computational Expert and the Biologist

PubMed Central

Banerjee, Debjani; Bellesia, Giovanni; Daigle, Bernie J.; Douglas, Geoffrey; Gu, Mengyuan; Gupta, Anand; Hellander, Stefan; Horuk, Chris; Nath, Dibyendu; Takkar, Aviral; Lötstedt, Per; Petzold, Linda R.

2016-01-01

We present StochSS: Stochastic Simulation as a Service, an integrated development environment for modeling and simulation of both deterministic and discrete stochastic biochemical systems in up to three dimensions. An easy to use graphical user interface enables researchers to quickly develop and simulate a biological model on a desktop or laptop, which can then be expanded to incorporate increasing levels of complexity. StochSS features state-of-the-art simulation engines. As the demand for computational power increases, StochSS can seamlessly scale computing resources in the cloud. In addition, StochSS can be deployed as a multi-user software environment where collaborators share computational resources and exchange models via a public model repository. We demonstrate the capabilities and ease of use of StochSS with an example of model development and simulation at increasing levels of complexity. PMID:27930676

Stochastic Simulation Service: Bridging the Gap between the Computational Expert and the Biologist

DOE PAGES

Drawert, Brian; Hellander, Andreas; Bales, Ben; ...

2016-12-08

We present StochSS: Stochastic Simulation as a Service, an integrated development environment for modeling and simulation of both deterministic and discrete stochastic biochemical systems in up to three dimensions. An easy to use graphical user interface enables researchers to quickly develop and simulate a biological model on a desktop or laptop, which can then be expanded to incorporate increasing levels of complexity. StochSS features state-of-the-art simulation engines. As the demand for computational power increases, StochSS can seamlessly scale computing resources in the cloud. In addition, StochSS can be deployed as a multi-user software environment where collaborators share computational resources andmore » exchange models via a public model repository. We also demonstrate the capabilities and ease of use of StochSS with an example of model development and simulation at increasing levels of complexity.« less

m2-ABKS: Attribute-Based Multi-Keyword Search over Encrypted Personal Health Records in Multi-Owner Setting.

PubMed

Miao, Yinbin; Ma, Jianfeng; Liu, Ximeng; Wei, Fushan; Liu, Zhiquan; Wang, Xu An

2016-11-01

Online personal health record (PHR) is more inclined to shift data storage and search operations to cloud server so as to enjoy the elastic resources and lessen computational burden in cloud storage. As multiple patients' data is always stored in the cloud server simultaneously, it is a challenge to guarantee the confidentiality of PHR data and allow data users to search encrypted data in an efficient and privacy-preserving way. To this end, we design a secure cryptographic primitive called as attribute-based multi-keyword search over encrypted personal health records in multi-owner setting to support both fine-grained access control and multi-keyword search via Ciphertext-Policy Attribute-Based Encryption. Formal security analysis proves our scheme is selectively secure against chosen-keyword attack. As a further contribution, we conduct empirical experiments over real-world dataset to show its feasibility and practicality in a broad range of actual scenarios without incurring additional computational burden.

Cloud Computing Services for Seismic Networks

NASA Astrophysics Data System (ADS)

Olson, Michael

This thesis describes a compositional framework for developing situation awareness applications: applications that provide ongoing information about a user's changing environment. The thesis describes how the framework is used to develop a situation awareness application for earthquakes. The applications are implemented as Cloud computing services connected to sensors and actuators. The architecture and design of the Cloud services are described and measurements of performance metrics are provided. The thesis includes results of experiments on earthquake monitoring conducted over a year. The applications developed by the framework are (1) the CSN---the Community Seismic Network---which uses relatively low-cost sensors deployed by members of the community, and (2) SAF---the Situation Awareness Framework---which integrates data from multiple sources, including the CSN, CISN---the California Integrated Seismic Network, a network consisting of high-quality seismometers deployed carefully by professionals in the CISN organization and spread across Southern California---and prototypes of multi-sensor platforms that include carbon monoxide, methane, dust and radiation sensors.

Geo-spatial Service and Application based on National E-government Network Platform and Cloud

NASA Astrophysics Data System (ADS)

Meng, X.; Deng, Y.; Li, H.; Yao, L.; Shi, J.

2014-04-01

With the acceleration of China's informatization process, our party and government take a substantive stride in advancing development and application of digital technology, which promotes the evolution of e-government and its informatization. Meanwhile, as a service mode based on innovative resources, cloud computing may connect huge pools together to provide a variety of IT services, and has become one relatively mature technical pattern with further studies and massive practical applications. Based on cloud computing technology and national e-government network platform, "National Natural Resources and Geospatial Database (NRGD)" project integrated and transformed natural resources and geospatial information dispersed in various sectors and regions, established logically unified and physically dispersed fundamental database and developed national integrated information database system supporting main e-government applications. Cross-sector e-government applications and services are realized to provide long-term, stable and standardized natural resources and geospatial fundamental information products and services for national egovernment and public users.

Cloud-based opportunities in scientific computing: insights from processing Suomi National Polar-Orbiting Partnership (S-NPP) Direct Broadcast data

NASA Astrophysics Data System (ADS)

Evans, J. D.; Hao, W.; Chettri, S.

2013-12-01

The cloud is proving to be a uniquely promising platform for scientific computing. Our experience with processing satellite data using Amazon Web Services highlights several opportunities for enhanced performance, flexibility, and cost effectiveness in the cloud relative to traditional computing -- for example: - Direct readout from a polar-orbiting satellite such as the Suomi National Polar-Orbiting Partnership (S-NPP) requires bursts of processing a few times a day, separated by quiet periods when the satellite is out of receiving range. In the cloud, by starting and stopping virtual machines in minutes, we can marshal significant computing resources quickly when needed, but not pay for them when not needed. To take advantage of this capability, we are automating a data-driven approach to the management of cloud computing resources, in which new data availability triggers the creation of new virtual machines (of variable size and processing power) which last only until the processing workflow is complete. - 'Spot instances' are virtual machines that run as long as one's asking price is higher than the provider's variable spot price. Spot instances can greatly reduce the cost of computing -- for software systems that are engineered to withstand unpredictable interruptions in service (as occurs when a spot price exceeds the asking price). We are implementing an approach to workflow management that allows data processing workflows to resume with minimal delays after temporary spot price spikes. This will allow systems to take full advantage of variably-priced 'utility computing.' - Thanks to virtual machine images, we can easily launch multiple, identical machines differentiated only by 'user data' containing individualized instructions (e.g., to fetch particular datasets or to perform certain workflows or algorithms) This is particularly useful when (as is the case with S-NPP data) we need to launch many very similar machines to process an unpredictable number of data files concurrently. Our experience shows the viability and flexibility of this approach to workflow management for scientific data processing. - Finally, cloud computing is a promising platform for distributed volunteer ('interstitial') computing, via mechanisms such as the Berkeley Open Infrastructure for Network Computing (BOINC) popularized with the SETI@Home project and others such as ClimatePrediction.net and NASA's Climate@Home. Interstitial computing faces significant challenges as commodity computing shifts from (always on) desktop computers towards smartphones and tablets (untethered and running on scarce battery power); but cloud computing offers significant slack capacity. This capacity includes virtual machines with unused RAM or underused CPUs; virtual storage volumes allocated (& paid for) but not full; and virtual machines that are paid up for the current hour but whose work is complete. We are devising ways to facilitate the reuse of these resources (i.e., cloud-based interstitial computing) for satellite data processing and related analyses. We will present our findings and research directions on these and related topics.

Working with Planetary-Scale Data in the Cloud

NASA Astrophysics Data System (ADS)

Flasher, J.

2017-12-01

When data is shared on AWS, it can be analyzed using AWS on-demand computing resources quickly and efficiently. Users can work with any amount of data without needing to download it or store their own copies. When heavy data like imagery, genomics data, or volumes of sensor data are available in AWS's cloud, the time required to copy the data to a virtual server for analysis is virtually eliminated. AWS's global infrastructure allows data providers to make their data available worldwide and ensure quick access to critical data from anywhere. In this session, we will share lessons learned from our experience supporting a global community of entrepreneurs, students and researchers by making petabytes of data freely available for anyone to use in the cloud.

DDBJ read annotation pipeline: a cloud computing-based pipeline for high-throughput analysis of next-generation sequencing data.

PubMed

Nagasaki, Hideki; Mochizuki, Takako; Kodama, Yuichi; Saruhashi, Satoshi; Morizaki, Shota; Sugawara, Hideaki; Ohyanagi, Hajime; Kurata, Nori; Okubo, Kousaku; Takagi, Toshihisa; Kaminuma, Eli; Nakamura, Yasukazu

2013-08-01

High-performance next-generation sequencing (NGS) technologies are advancing genomics and molecular biological research. However, the immense amount of sequence data requires computational skills and suitable hardware resources that are a challenge to molecular biologists. The DNA Data Bank of Japan (DDBJ) of the National Institute of Genetics (NIG) has initiated a cloud computing-based analytical pipeline, the DDBJ Read Annotation Pipeline (DDBJ Pipeline), for a high-throughput annotation of NGS reads. The DDBJ Pipeline offers a user-friendly graphical web interface and processes massive NGS datasets using decentralized processing by NIG supercomputers currently free of charge. The proposed pipeline consists of two analysis components: basic analysis for reference genome mapping and de novo assembly and subsequent high-level analysis of structural and functional annotations. Users may smoothly switch between the two components in the pipeline, facilitating web-based operations on a supercomputer for high-throughput data analysis. Moreover, public NGS reads of the DDBJ Sequence Read Archive located on the same supercomputer can be imported into the pipeline through the input of only an accession number. This proposed pipeline will facilitate research by utilizing unified analytical workflows applied to the NGS data. The DDBJ Pipeline is accessible at http://p.ddbj.nig.ac.jp/.

Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications.

PubMed

Pérez-Torres, Rafael; Torres-Huitzil, César; Galeana-Zapién, Hiram

2016-10-13

The tracking of frequently visited places, also known as stay points, is a critical feature in location-aware mobile applications as a way to adapt the information and services provided to smartphones users according to their moving patterns. Location based applications usually employ the GPS receiver along with Wi-Fi hot-spots and cellular cell tower mechanisms for estimating user location. Typically, fine-grained GPS location data are collected by the smartphone and transferred to dedicated servers for trajectory analysis and stay points detection. Such Mobile Cloud Computing approach has been successfully employed for extending smartphone's battery lifetime by exchanging computation costs, assuming that on-device stay points detection is prohibitive. In this article, we propose and validate the feasibility of having an alternative event-driven mechanism for stay points detection that is executed fully on-device, and that provides higher energy savings by avoiding communication costs. Our solution is encapsulated in a sensing middleware for Android smartphones, where a stream of GPS location updates is collected in the background, supporting duty cycling schemes, and incrementally analyzed following an event-driven paradigm for stay points detection. To evaluate the performance of the proposed middleware, real world experiments were conducted under different stress levels, validating its power efficiency when compared against a Mobile Cloud Computing oriented solution.

DDBJ Read Annotation Pipeline: A Cloud Computing-Based Pipeline for High-Throughput Analysis of Next-Generation Sequencing Data

PubMed Central

Nagasaki, Hideki; Mochizuki, Takako; Kodama, Yuichi; Saruhashi, Satoshi; Morizaki, Shota; Sugawara, Hideaki; Ohyanagi, Hajime; Kurata, Nori; Okubo, Kousaku; Takagi, Toshihisa; Kaminuma, Eli; Nakamura, Yasukazu

2013-01-01

High-performance next-generation sequencing (NGS) technologies are advancing genomics and molecular biological research. However, the immense amount of sequence data requires computational skills and suitable hardware resources that are a challenge to molecular biologists. The DNA Data Bank of Japan (DDBJ) of the National Institute of Genetics (NIG) has initiated a cloud computing-based analytical pipeline, the DDBJ Read Annotation Pipeline (DDBJ Pipeline), for a high-throughput annotation of NGS reads. The DDBJ Pipeline offers a user-friendly graphical web interface and processes massive NGS datasets using decentralized processing by NIG supercomputers currently free of charge. The proposed pipeline consists of two analysis components: basic analysis for reference genome mapping and de novo assembly and subsequent high-level analysis of structural and functional annotations. Users may smoothly switch between the two components in the pipeline, facilitating web-based operations on a supercomputer for high-throughput data analysis. Moreover, public NGS reads of the DDBJ Sequence Read Archive located on the same supercomputer can be imported into the pipeline through the input of only an accession number. This proposed pipeline will facilitate research by utilizing unified analytical workflows applied to the NGS data. The DDBJ Pipeline is accessible at http://p.ddbj.nig.ac.jp/. PMID:23657089

Lost in Virtual Reality: Pathfinding Algorithms Detect Rock Fractures and Contacts in Point Clouds

NASA Astrophysics Data System (ADS)

Thiele, S.; Grose, L.; Micklethwaite, S.

2016-12-01

UAV-based photogrammetric and LiDAR techniques provide high resolution 3D point clouds and ortho-rectified photomontages that can capture surface geology in outstanding detail over wide areas. Automated and semi-automated methods are vital to extract full value from these data in practical time periods, though the nuances of geological structures and materials (natural variability in colour and geometry, soft and hard linkage, shadows and multiscale properties) make this a challenging task. We present a novel method for computer assisted trace detection in dense point clouds, using a lowest cost path solver to "follow" fracture traces and lithological contacts between user defined end points. This is achieved by defining a local neighbourhood network where each point in the cloud is linked to its neighbours, and then using a least-cost path algorithm to search this network and estimate the trace of the fracture or contact. A variety of different algorithms can then be applied to calculate the best fit plane, produce a fracture network, or map properties such as roughness, curvature and fracture intensity. Our prototype of this method (Fig. 1) suggests the technique is feasible and remarkably good at following traces under non-optimal conditions such as variable-shadow, partial occlusion and complex fracturing. Furthermore, if a fracture is initially mapped incorrectly, the user can easily provide further guidance by defining intermediate waypoints. Future development will include optimization of the algorithm to perform well on large point clouds and modifications that permit the detection of features such as step-overs. We also plan on implementing this approach in an interactive graphical user environment.

Aether: leveraging linear programming for optimal cloud computing in genomics.

PubMed

Luber, Jacob M; Tierney, Braden T; Cofer, Evan M; Patel, Chirag J; Kostic, Aleksandar D

2018-05-01

Across biology, we are seeing rapid developments in scale of data production without a corresponding increase in data analysis capabilities. Here, we present Aether (http://aether.kosticlab.org), an intuitive, easy-to-use, cost-effective and scalable framework that uses linear programming to optimally bid on and deploy combinations of underutilized cloud computing resources. Our approach simultaneously minimizes the cost of data analysis and provides an easy transition from users' existing HPC pipelines. Data utilized are available at https://pubs.broadinstitute.org/diabimmune and with EBI SRA accession ERP005989. Source code is available at (https://github.com/kosticlab/aether). Examples, documentation and a tutorial are available at http://aether.kosticlab.org. chirag_patel@hms.harvard.edu or aleksandar.kostic@joslin.harvard.edu. Supplementary data are available at Bioinformatics online.

iSPHERE - A New Approach to Collaborative Research and Cloud Computing

NASA Astrophysics Data System (ADS)

Al-Ubaidi, T.; Khodachenko, M. L.; Kallio, E. J.; Harry, A.; Alexeev, I. I.; Vázquez-Poletti, J. L.; Enke, H.; Magin, T.; Mair, M.; Scherf, M.; Poedts, S.; De Causmaecker, P.; Heynderickx, D.; Congedo, P.; Manolescu, I.; Esser, B.; Webb, S.; Ruja, C.

2015-10-01

The project iSPHERE (integrated Scientific Platform for HEterogeneous Research and Engineering) that has been proposed for Horizon 2020 (EINFRA-9- 2015, [1]) aims at creating a next generation Virtual Research Environment (VRE) that embraces existing and emerging technologies and standards in order to provide a versatile platform for scientific investigations and collaboration. The presentation will introduce the large project consortium, provide a comprehensive overview of iSPHERE's basic concepts and approaches and outline general user requirements that the VRE will strive to satisfy. An overview of the envisioned architecture will be given, focusing on the adapted Service Bus concept, i.e. the "Scientific Service Bus" as it is called in iSPHERE. The bus will act as a central hub for all communication and user access, and will be implemented in the course of the project. The agile approach [2] that has been chosen for detailed elaboration and documentation of user requirements, as well as for the actual implementation of the system, will be outlined and its motivation and basic structure will be discussed. The presentation will show which user communities will benefit and which concrete problems, scientific investigations are facing today, will be tackled by the system. Another focus of the presentation is iSPHERE's seamless integration of cloud computing resources and how these will benefit scientific modeling teams by providing a reliable and web based environment for cloud based model execution, storage of results, and comparison with measurements, including fully web based tools for data mining, analysis and visualization. Also the envisioned creation of a dedicated data model for experimental plasma physics will be discussed. It will be shown why the Scientific Service Bus provides an ideal basis to integrate a number of data models and communication protocols and to provide mechanisms for data exchange across multiple and even multidisciplinary platforms.

An Efficient Quantum Somewhat Homomorphic Symmetric Searchable Encryption

NASA Astrophysics Data System (ADS)

Sun, Xiaoqiang; Wang, Ting; Sun, Zhiwei; Wang, Ping; Yu, Jianping; Xie, Weixin

2017-04-01

In 2009, Gentry first introduced an ideal lattices fully homomorphic encryption (FHE) scheme. Later, based on the approximate greatest common divisor problem, learning with errors problem or learning with errors over rings problem, FHE has developed rapidly, along with the low efficiency and computational security. Combined with quantum mechanics, Liang proposed a symmetric quantum somewhat homomorphic encryption (QSHE) scheme based on quantum one-time pad, which is unconditional security. And it was converted to a quantum fully homomorphic encryption scheme, whose evaluation algorithm is based on the secret key. Compared with Liang's QSHE scheme, we propose a more efficient QSHE scheme for classical input states with perfect security, which is used to encrypt the classical message, and the secret key is not required in the evaluation algorithm. Furthermore, an efficient symmetric searchable encryption (SSE) scheme is constructed based on our QSHE scheme. SSE is important in the cloud storage, which allows users to offload search queries to the untrusted cloud. Then the cloud is responsible for returning encrypted files that match search queries (also encrypted), which protects users' privacy.

Data and knowledge in medical distributed applications.

PubMed

Serban, Alexandru; Crişan-Vida, Mihaela; Stoicu-Tivadar, Lăcrămioara

2014-01-01

Building a clinical decision support system (CDSS) capable to collect process and diagnose data from the patients automatically, based on information, symptoms and investigations is one of the current challenges for researchers and medical science. The purpose of the current study is to design a cloud-based CDSS to improve patient safety, quality of care and organizational efficiency. It presents the design of a cloud-based application system using a medical based approach, which covers different diseases to diagnosis, differentiated on most important pathologies. Using online questionnaires, traditional and new data will be collected from patients. After data input, the application will formulate a presumptive diagnosis and will direct patients to the correspondent department. A questionnaire will dynamically ask questions about the interface, and functionality improvements. Based on the answers, the functionality of the system and the user interface will be improved considering the real needs expressed by the end-users. The cloud-based CDSS, as a useful tool for patients, physicians and healthcare providers involves the computer support in the diagnosis of different pathologies and an accurate automatic differential diagnostic system.

mPano: cloud-based mobile panorama view from single picture

NASA Astrophysics Data System (ADS)

Li, Hongzhi; Zhu, Wenwu

2013-09-01

Panorama view provides people an informative and natural user experience to represent the whole scene. The advances on mobile augmented reality, mobile-cloud computing, and mobile internet can enable panorama view on mobile phone with new functionalities, such as anytime anywhere query where a landmark picture is and what the whole scene looks like. To generate and explore panorama view on mobile devices faces significant challenges due to the limitations of computing capacity, battery life, and memory size of mobile phones, as well as the bandwidth of mobile Internet connection. To address the challenges, this paper presents a novel cloud-based mobile panorama view system that can generate and view panorama-view on mobile devices from a single picture, namely "Pano". In our system, first, we propose a novel iterative multi-modal image retrieval (IMIR) approach to get spatially adjacent images using both tag and content information from the single picture. Second, we propose a cloud-based parallel server synthing approach to generate panorama view in cloud, against today's local-client synthing approach that is almost impossible for mobile phones. Third, we propose predictive-cache solution to reduce latency of image delivery from cloud server to the mobile client. We have built a real mobile panorama view system and perform experiments. The experimental results demonstrated the effectiveness of our system and the proposed key component technologies, especially for landmark images.

The EPOS Vision for the Open Science Cloud

NASA Astrophysics Data System (ADS)

Jeffery, Keith; Harrison, Matt; Cocco, Massimo

2016-04-01

Cloud computing offers dynamic elastic scalability for data processing on demand. For much research activity, demand for computing is uneven over time and so CLOUD computing offers both cost-effectiveness and capacity advantages. However, as reported repeatedly by the EC Cloud Expert Group, there are barriers to the uptake of Cloud Computing: (1) security and privacy; (2) interoperability (avoidance of lock-in); (3) lack of appropriate systems development environments for application programmers to characterise their applications to allow CLOUD middleware to optimize their deployment and execution. From CERN, the Helix-Nebula group has proposed the architecture for the European Open Science Cloud. They are discussing with other e-Infrastructure groups such as EGI (GRIDs), EUDAT (data curation), AARC (network authentication and authorisation) and also with the EIROFORUM group of 'international treaty' RIs (Research Infrastructures) and the ESFRI (European Strategic Forum for Research Infrastructures) RIs including EPOS. Many of these RIs are either e-RIs (electronic-RIs) or have an e-RI interface for access and use. The EPOS architecture is centred on a portal: ICS (Integrated Core Services). The architectural design already allows for access to e-RIs (which may include any or all of data, software, users and resources such as computers or instruments). Those within any one domain (subject area) of EPOS are considered within the TCS (Thematic Core Services). Those outside, or available across multiple domains of EPOS, are ICS-d (Integrated Core Services-Distributed) since the intention is that they will be used by any or all of the TCS via the ICS. Another such service type is CES (Computational Earth Science); effectively an ICS-d specializing in high performance computation, analytics, simulation or visualization offered by a TCS for others to use. Already discussions are underway between EPOS and EGI, EUDAT, AARC and Helix-Nebula for those offerings to be considered as ICS-ds by EPOS.. Provision of access to ICS-Ds from ICS-C concerns several aspects: (a) Technical : it may be more or less difficult to connect and pass from ICS-C to the ICS-d/ CES the 'package' (probably a virtual machine) of data and software; (b) Security/privacy : including passing personal information e.g. related to AAAI (Authentication, authorization, accounting Infrastructure); (c) financial and legal : such as payment, licence conditions; Appropriate interfaces from ICS-C to ICS-d are being designed to accommodate these aspects. The Open Science Cloud is timely because it provides a framework to discuss governance and sustainability for computational resource provision as well as an effective interpretation of federated approach to HPC(High Performance Computing) -HTC (High Throughput Computing). It will be a unique opportunity to share and adopt procurement policies to provide access to computational resources for RIs. The current state of discussions and expected roadmap for the EPOS-Open Science Cloud relationship are presented.

SatCam: A mobile application for coordinated ground/satellite observation of clouds and validation of satellite-derived cloud mask products.

NASA Astrophysics Data System (ADS)

Gumley, L.; Parker, D.; Flynn, B.; Holz, R.; Marais, W.

2011-12-01

SatCam is an application for iOS devices that allows users to collect observations of local cloud and surface conditions in coordination with an overpass of the Terra, Aqua, or NPP satellites. SatCam allows users to acquire images of sky conditions and ground conditions at their location anywhere in the world using the built-in iPhone or iPod Touch camera at the same time that the satellite is passing overhead and viewing their location. Immediately after the sky and ground observations are acquired, the application asks the user to rate the level of cloudiness in the sky (Completely Clear, Mostly Clear, Partly Cloudy, Overcast). For the ground observation, the user selects their assessment of the surface conditions (Urban, Green Vegetation, Brown Vegetation, Desert, Snow, Water). The sky condition and surface condition selections are stored along with the date, time, and geographic location for the images, and the images are uploaded to a central server. When the MODIS (Terra and Aqua) or VIIRS (NPP) imagery acquired over the user location becomes available, a MODIS or VIIRS true color image centered at the user's location is delivered back to the SatCam application on the user's iOS device. SSEC also proposes to develop a community driven SatCam website where users can share their observations and assessments of satellite cloud products in a collaborative environment. SSEC is developing a server side data analysis system to ingest the SatCam user observations, apply quality control, analyze the sky images for cloud cover, and collocate the observations with MODIS and VIIRS satellite products (e.g., cloud mask). For each observation that is collocated with a satellite observation, the server will determine whether the user scored a "hit", meaning their sky observation and sky assessment matched the automated cloud mask obtained from the satellite observation. The hit rate will be an objective assessment of the accuracy of the user's sky observations. Users with high hit rates will be identified automatically and their observations will be used globally to evaluate the performance of the MODIS cloud mask algorithm for Terra and Aqua and the VIIRS cloud mask algorithm for NPP. The user's assessment of the ground conditions will also be used to evaluate the cloud mask accuracy in selecting the correct surface type at the user's location, which is an important element in the decision path used internally by the cloud mask algorithm. This presentation will describe the SatCam application, how it is used, and show examples of SatCam observations.

Residential Consumer-Centric Demand-Side Management Based on Energy Disaggregation-Piloting Constrained Swarm Intelligence: Towards Edge Computing.

PubMed

Lin, Yu-Hsiu; Hu, Yu-Chen

2018-04-27

The emergence of smart Internet of Things (IoT) devices has highly favored the realization of smart homes in a down-stream sector of a smart grid. The underlying objective of Demand Response (DR) schemes is to actively engage customers to modify their energy consumption on domestic appliances in response to pricing signals. Domestic appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption intelligently. Besides, to residential customers for DR implementation, maintaining a balance between energy consumption cost and users’ comfort satisfaction is a challenge. Hence, in this paper, a constrained Particle Swarm Optimization (PSO)-based residential consumer-centric load-scheduling method is proposed. The method can be further featured with edge computing. In contrast with cloud computing, edge computing—a method of optimizing cloud computing technologies by driving computing capabilities at the IoT edge of the Internet as one of the emerging trends in engineering technology—addresses bandwidth-intensive contents and latency-sensitive applications required among sensors and central data centers through data analytics at or near the source of data. A non-intrusive load-monitoring technique proposed previously is utilized to automatic determination of physical characteristics of power-intensive home appliances from users’ life patterns. The swarm intelligence, constrained PSO, is used to minimize the energy consumption cost while considering users’ comfort satisfaction for DR implementation. The residential consumer-centric load-scheduling method proposed in this paper is evaluated under real-time pricing with inclining block rates and is demonstrated in a case study. The experimentation reported in this paper shows the proposed residential consumer-centric load-scheduling method can re-shape loads by home appliances in response to DR signals. Moreover, a phenomenal reduction in peak power consumption is achieved by 13.97%.

Clearing your Desk! Software and Data Services for Collaborative Web Based GIS Analysis

NASA Astrophysics Data System (ADS)

Tarboton, D. G.; Idaszak, R.; Horsburgh, J. S.; Ames, D. P.; Goodall, J. L.; Band, L. E.; Merwade, V.; Couch, A.; Hooper, R. P.; Maidment, D. R.; Dash, P. K.; Stealey, M.; Yi, H.; Gan, T.; Gichamo, T.; Yildirim, A. A.; Liu, Y.

2015-12-01

Can your desktop computer crunch the large GIS datasets that are becoming increasingly common across the geosciences? Do you have access to or the know-how to take advantage of advanced high performance computing (HPC) capability? Web based cyberinfrastructure takes work off your desk or laptop computer and onto infrastructure or "cloud" based data and processing servers. This talk will describe the HydroShare collaborative environment and web based services being developed to support the sharing and processing of hydrologic data and models. HydroShare supports the upload, storage, and sharing of a broad class of hydrologic data including time series, geographic features and raster datasets, multidimensional space-time data, and other structured collections of data. Web service tools and a Python client library provide researchers with access to HPC resources without requiring them to become HPC experts. This reduces the time and effort spent in finding and organizing the data required to prepare the inputs for hydrologic models and facilitates the management of online data and execution of models on HPC systems. This presentation will illustrate the use of web based data and computation services from both the browser and desktop client software. These web-based services implement the Terrain Analysis Using Digital Elevation Model (TauDEM) tools for watershed delineation, generation of hydrology-based terrain information, and preparation of hydrologic model inputs. They allow users to develop scripts on their desktop computer that call analytical functions that are executed completely in the cloud, on HPC resources using input datasets stored in the cloud, without installing specialized software, learning how to use HPC, or transferring large datasets back to the user's desktop. These cases serve as examples for how this approach can be extended to other models to enhance the use of web and data services in the geosciences.

Using Cloud Computing Services in e-Learning Process: Benefits and Challenges

ERIC Educational Resources Information Center

El Mhouti, Abderrahim; Erradi, Mohamed; Nasseh, Azeddine

2018-01-01

During the recent years, Information and Communication Technologies (ICT) play a significant role in the field of education and e-learning has become a very popular trend of the education technology. However, with the huge growth of the number of users, data and educational resources generated, e-learning systems have become more and more…

Open Courseware in Design and Planning Education and Utilization of Distance Education Opportunity: Anadolu University Experience

ERIC Educational Resources Information Center

Halac, Hicran Hanim; Cabuk, Alper

2013-01-01

Depending on the evolving technological possibilities, distance and online education applications have gradually gained more significance in the education system. Regarding the issues, such as advancements in the server services, disc capacity, cloud computing opportunities resulting from the increase in the number of the broadband internet users,…

SeaDataCloud - further developing the pan-European SeaDataNet infrastructure for marine and ocean data management

NASA Astrophysics Data System (ADS)

Schaap, Dick M. A.; Fichaut, Michele

2017-04-01

SeaDataCloud marks the third phase of developing the pan-European SeaDataNet infrastructure for marine and ocean data management. The SeaDataCloud project is funded by EU and runs for 4 years from 1st November 2016. It succeeds the successful SeaDataNet II (2011 - 2015) and SeaDataNet (2006 - 2011) projects. SeaDataNet has set up and operates a pan-European infrastructure for managing marine and ocean data and is undertaken by National Oceanographic Data Centres (NODC's) and oceanographic data focal points from 34 coastal states in Europe. The infrastructure comprises a network of interconnected data centres and central SeaDataNet portal. The portal provides users a harmonised set of metadata directories and controlled access to the large collections of datasets, managed by the interconnected data centres. The population of directories has increased considerably in cooperation with and involvement in many associated EU projects and initiatives such as EMODnet. SeaDataNet at present gives overview and access to more than 1.9 million data sets for physical oceanography, chemistry, geology, geophysics, bathymetry and biology from more than 100 connected data centres from 34 countries riparian to European seas. SeaDataNet is also active in setting and governing marine data standards, and exploring and establishing interoperability solutions to connect to other e-infrastructures on the basis of standards of ISO (19115, 19139), and OGC (WMS, WFS, CS-W and SWE). Standards and associated SeaDataNet tools are made available at the SeaDataNet portal for wide uptake by data handling and managing organisations. SeaDataCloud aims at further developing standards, innovating services & products, adopting new technologies, and giving more attention to users. Moreover, it is about implementing a cooperation between the SeaDataNet consortium of marine data centres and the EUDAT consortium of e-infrastructure service providers. SeaDataCloud aims at considerably advancing services and increasing their usage by adopting cloud and High Performance Computing technology. SeaDataCloud will empower researchers with a packaged collection of services and tools, tailored to their specific needs, supporting research and enabling generation of added-value products from marine and ocean data. Substantial activities will be focused on developing added-value services, such as data subsetting, analysis, visualisation, and publishing workflows for users, both regular and advanced users, as part of a Virtual Research Environment (VRE). SeaDataCloud aims at a number of leading user communities that have new challenges for upgrading and expanding the SeaDataNet standards and services: Science, EMODnet, Copernicus Marine Environmental Monitoring Service (CMEMS) and EuroGOOS, and International scientific programmes. The presentation will give information on present services of the SeaDataNet infrastructure and services, and the new challenges in SeaDataCloud, and will highlight a number of key achievements in SeaDataCloud so far.

Exploiting parallel R in the cloud with SPRINT.

PubMed

Piotrowski, M; McGilvary, G A; Sloan, T M; Mewissen, M; Lloyd, A D; Forster, T; Mitchell, L; Ghazal, P; Hill, J

2013-01-01

Advances in DNA Microarray devices and next-generation massively parallel DNA sequencing platforms have led to an exponential growth in data availability but the arising opportunities require adequate computing resources. High Performance Computing (HPC) in the Cloud offers an affordable way of meeting this need. Bioconductor, a popular tool for high-throughput genomic data analysis, is distributed as add-on modules for the R statistical programming language but R has no native capabilities for exploiting multi-processor architectures. SPRINT is an R package that enables easy access to HPC for genomics researchers. This paper investigates: setting up and running SPRINT-enabled genomic analyses on Amazon's Elastic Compute Cloud (EC2), the advantages of submitting applications to EC2 from different parts of the world and, if resource underutilization can improve application performance. The SPRINT parallel implementations of correlation, permutation testing, partitioning around medoids and the multi-purpose papply have been benchmarked on data sets of various size on Amazon EC2. Jobs have been submitted from both the UK and Thailand to investigate monetary differences. It is possible to obtain good, scalable performance but the level of improvement is dependent upon the nature of the algorithm. Resource underutilization can further improve the time to result. End-user's location impacts on costs due to factors such as local taxation. Although not designed to satisfy HPC requirements, Amazon EC2 and cloud computing in general provides an interesting alternative and provides new possibilities for smaller organisations with limited funds.

Unidata cyberinfrastructure in the cloud: A progress report

NASA Astrophysics Data System (ADS)

Ramamurthy, Mohan

2016-04-01

Data services, software, and committed support are critical components of geosciences cyber-infrastructure that can help scientists address problems of unprecedented complexity, scale, and scope. Unidata is currently working on innovative ideas, new paradigms, and novel techniques to complement and extend its offerings. Our goal is to empower users so that they can tackle major, heretofore difficult problems. Unidata recognizes that its products and services must evolve to support new approaches to research and education. After years of hype and ambiguity, cloud computing is maturing in usability in many areas of science and education, bringing the benefits of virtualized and elastic remote services to infrastructure, software, computation, and data. Cloud environments reduce the amount of time and money spent to procure, install, and maintain new hardware and software, and reduce costs through resource pooling and shared infrastructure. Cloud services aimed at providing any resource, at any time, from any place, using any device are increasingly being embraced by all types of organizations. Given this trend and the enormous potential of cloud-based services, Unidata is moving to augment its products, services, data delivery mechanisms and applications to align with the cloud-computing paradigm. To realize the above vision, Unidata is working toward: * Providing access to many types of data from a cloud (e.g., TDS, RAMADDA and EDEX); * Deploying data-proximate tools to easily process, analyze and visualize those data in a cloud environment cloud for consumption by any one, by any device, from anywhere, at any time; * Developing and providing a range of pre-configured and well-integrated tools and services that can be deployed by any university in their own private or public cloud settings. Specifically, Unidata has developed Docker for "containerized applications", making them easy to deploy. Docker helps to create "disposable" installs and eliminates many configuration challenges. Containerized applications include tools for data transport, access, analysis, and visualization: THREDDS Data Server, Integrated Data Viewer, GEMPAK, Local Data Manager, RAMADDA Data Server, and Python tools; * Fostering partnerships with NOAA and public cloud vendors (e.g., Amazon) to harness their capabilities and resources for the benefit of the academic community.

The Iqmulus Urban Showcase: Automatic Tree Classification and Identification in Huge Mobile Mapping Point Clouds

NASA Astrophysics Data System (ADS)

Böhm, J.; Bredif, M.; Gierlinger, T.; Krämer, M.; Lindenberg, R.; Liu, K.; Michel, F.; Sirmacek, B.

2016-06-01

Current 3D data capturing as implemented on for example airborne or mobile laser scanning systems is able to efficiently sample the surface of a city by billions of unselective points during one working day. What is still difficult is to extract and visualize meaningful information hidden in these point clouds with the same efficiency. This is where the FP7 IQmulus project enters the scene. IQmulus is an interactive facility for processing and visualizing big spatial data. In this study the potential of IQmulus is demonstrated on a laser mobile mapping point cloud of 1 billion points sampling ~ 10 km of street environment in Toulouse, France. After the data is uploaded to the IQmulus Hadoop Distributed File System, a workflow is defined by the user consisting of retiling the data followed by a PCA driven local dimensionality analysis, which runs efficiently on the IQmulus cloud facility using a Spark implementation. Points scattering in 3 directions are clustered in the tree class, and are separated next into individual trees. Five hours of processing at the 12 node computing cluster results in the automatic identification of 4000+ urban trees. Visualization of the results in the IQmulus fat client helps users to appreciate the results, and developers to identify remaining flaws in the processing workflow.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martin, Shawn

This code consists of Matlab routines which enable the user to perform non-manifold surface reconstruction via triangulation from high dimensional point cloud data. The code was based on an algorithm originally developed in [Freedman (2007), An Incremental Algorithm for Reconstruction of Surfaces of Arbitrary Codimension Computational Geometry: Theory and Applications, 36(2):106-116]. This algorithm has been modified to accommodate non-manifold surface according to the work described in [S. Martin and J.-P. Watson (2009), Non-Manifold Surface Reconstruction from High Dimensional Point Cloud DataSAND #5272610].The motivation for developing the code was a point cloud describing the molecular conformation space of cyclooctane (C8H16). Cyclooctanemore » conformation space was represented using points in 72 dimensions (3 coordinates for each molecule). The code was used to triangulate the point cloud and thereby study the geometry and topology of cyclooctane. Futures applications are envisioned for peptides and proteins.« less

Performance testing of 3D point cloud software

NASA Astrophysics Data System (ADS)

Varela-González, M.; González-Jorge, H.; Riveiro, B.; Arias, P.

2013-10-01

LiDAR systems are being used widely in recent years for many applications in the engineering field: civil engineering, cultural heritage, mining, industry and environmental engineering. One of the most important limitations of this technology is the large computational requirements involved in data processing, especially for large mobile LiDAR datasets. Several software solutions for data managing are available in the market, including open source suites, however, users often unknown methodologies to verify their performance properly. In this work a methodology for LiDAR software performance testing is presented and four different suites are studied: QT Modeler, VR Mesh, AutoCAD 3D Civil and the Point Cloud Library running in software developed at the University of Vigo (SITEGI). The software based on the Point Cloud Library shows better results in the loading time of the point clouds and CPU usage. However, it is not as strong as commercial suites in working set and commit size tests.

Exploring Architectural Details Through a Wearable Egocentric Vision Device

PubMed Central

Alletto, Stefano; Abati, Davide; Serra, Giuseppe; Cucchiara, Rita

2016-01-01

Augmented user experiences in the cultural heritage domain are in increasing demand by the new digital native tourists of 21st century. In this paper, we propose a novel solution that aims at assisting the visitor during an outdoor tour of a cultural site using the unique first person perspective of wearable cameras. In particular, the approach exploits computer vision techniques to retrieve the details by proposing a robust descriptor based on the covariance of local features. Using a lightweight wearable board, the solution can localize the user with respect to the 3D point cloud of the historical landmark and provide him with information about the details at which he is currently looking. Experimental results validate the method both in terms of accuracy and computational effort. Furthermore, user evaluation based on real-world experiments shows that the proposal is deemed effective in enriching a cultural experience. PMID:26901197

Exploring Architectural Details Through a Wearable Egocentric Vision Device.

PubMed

Alletto, Stefano; Abati, Davide; Serra, Giuseppe; Cucchiara, Rita

2016-02-17

Augmented user experiences in the cultural heritage domain are in increasing demand by the new digital native tourists of 21st century. In this paper, we propose a novel solution that aims at assisting the visitor during an outdoor tour of a cultural site using the unique first person perspective of wearable cameras. In particular, the approach exploits computer vision techniques to retrieve the details by proposing a robust descriptor based on the covariance of local features. Using a lightweight wearable board, the solution can localize the user with respect to the 3D point cloud of the historical landmark and provide him with information about the details at which he is currently looking. Experimental results validate the method both in terms of accuracy and computational effort. Furthermore, user evaluation based on real-world experiments shows that the proposal is deemed effective in enriching a cultural experience.

Heart beats in the cloud: distributed analysis of electrophysiological ‘Big Data’ using cloud computing for epilepsy clinical research

PubMed Central

Sahoo, Satya S; Jayapandian, Catherine; Garg, Gaurav; Kaffashi, Farhad; Chung, Stephanie; Bozorgi, Alireza; Chen, Chien-Hun; Loparo, Kenneth; Lhatoo, Samden D; Zhang, Guo-Qiang

2014-01-01

Objective The rapidly growing volume of multimodal electrophysiological signal data is playing a critical role in patient care and clinical research across multiple disease domains, such as epilepsy and sleep medicine. To facilitate secondary use of these data, there is an urgent need to develop novel algorithms and informatics approaches using new cloud computing technologies as well as ontologies for collaborative multicenter studies. Materials and methods We present the Cloudwave platform, which (a) defines parallelized algorithms for computing cardiac measures using the MapReduce parallel programming framework, (b) supports real-time interaction with large volumes of electrophysiological signals, and (c) features signal visualization and querying functionalities using an ontology-driven web-based interface. Cloudwave is currently used in the multicenter National Institute of Neurological Diseases and Stroke (NINDS)-funded Prevention and Risk Identification of SUDEP (sudden unexplained death in epilepsy) Mortality (PRISM) project to identify risk factors for sudden death in epilepsy. Results Comparative evaluations of Cloudwave with traditional desktop approaches to compute cardiac measures (eg, QRS complexes, RR intervals, and instantaneous heart rate) on epilepsy patient data show one order of magnitude improvement for single-channel ECG data and 20 times improvement for four-channel ECG data. This enables Cloudwave to support real-time user interaction with signal data, which is semantically annotated with a novel epilepsy and seizure ontology. Discussion Data privacy is a critical issue in using cloud infrastructure, and cloud platforms, such as Amazon Web Services, offer features to support Health Insurance Portability and Accountability Act standards. Conclusion The Cloudwave platform is a new approach to leverage of large-scale electrophysiological data for advancing multicenter clinical research. PMID:24326538

Heart beats in the cloud: distributed analysis of electrophysiological 'Big Data' using cloud computing for epilepsy clinical research.

PubMed

Sahoo, Satya S; Jayapandian, Catherine; Garg, Gaurav; Kaffashi, Farhad; Chung, Stephanie; Bozorgi, Alireza; Chen, Chien-Hun; Loparo, Kenneth; Lhatoo, Samden D; Zhang, Guo-Qiang

2014-01-01

The rapidly growing volume of multimodal electrophysiological signal data is playing a critical role in patient care and clinical research across multiple disease domains, such as epilepsy and sleep medicine. To facilitate secondary use of these data, there is an urgent need to develop novel algorithms and informatics approaches using new cloud computing technologies as well as ontologies for collaborative multicenter studies. We present the Cloudwave platform, which (a) defines parallelized algorithms for computing cardiac measures using the MapReduce parallel programming framework, (b) supports real-time interaction with large volumes of electrophysiological signals, and (c) features signal visualization and querying functionalities using an ontology-driven web-based interface. Cloudwave is currently used in the multicenter National Institute of Neurological Diseases and Stroke (NINDS)-funded Prevention and Risk Identification of SUDEP (sudden unexplained death in epilepsy) Mortality (PRISM) project to identify risk factors for sudden death in epilepsy. Comparative evaluations of Cloudwave with traditional desktop approaches to compute cardiac measures (eg, QRS complexes, RR intervals, and instantaneous heart rate) on epilepsy patient data show one order of magnitude improvement for single-channel ECG data and 20 times improvement for four-channel ECG data. This enables Cloudwave to support real-time user interaction with signal data, which is semantically annotated with a novel epilepsy and seizure ontology. Data privacy is a critical issue in using cloud infrastructure, and cloud platforms, such as Amazon Web Services, offer features to support Health Insurance Portability and Accountability Act standards. The Cloudwave platform is a new approach to leverage of large-scale electrophysiological data for advancing multicenter clinical research.

Development of a High Resolution Weather Forecast Model for Mesoamerica Using the NASA Nebula Cloud Computing Environment

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Case, Jonathan L.; Venner, Jason; Moreno-Madrinan, Max. J.; Delgado, Francisco

2012-01-01

Over the past two years, scientists in the Earth Science Office at NASA fs Marshall Space Flight Center (MSFC) have explored opportunities to apply cloud computing concepts to support near real ]time weather forecast modeling via the Weather Research and Forecasting (WRF) model. Collaborators at NASA fs Short ]term Prediction Research and Transition (SPoRT) Center and the SERVIR project at Marshall Space Flight Center have established a framework that provides high resolution, daily weather forecasts over Mesoamerica through use of the NASA Nebula Cloud Computing Platform at Ames Research Center. Supported by experts at Ames, staff at SPoRT and SERVIR have established daily forecasts complete with web graphics and a user interface that allows SERVIR partners access to high resolution depictions of weather in the next 48 hours, useful for monitoring and mitigating meteorological hazards such as thunderstorms, heavy precipitation, and tropical weather that can lead to other disasters such as flooding and landslides. This presentation will describe the framework for establishing and providing WRF forecasts, example applications of output provided via the SERVIR web portal, and early results of forecast model verification against available surface ] and satellite ]based observations.

Machine learning patterns for neuroimaging-genetic studies in the cloud.

PubMed

Da Mota, Benoit; Tudoran, Radu; Costan, Alexandru; Varoquaux, Gaël; Brasche, Goetz; Conrod, Patricia; Lemaitre, Herve; Paus, Tomas; Rietschel, Marcella; Frouin, Vincent; Poline, Jean-Baptiste; Antoniu, Gabriel; Thirion, Bertrand

2014-01-01

Brain imaging is a natural intermediate phenotype to understand the link between genetic information and behavior or brain pathologies risk factors. Massive efforts have been made in the last few years to acquire high-dimensional neuroimaging and genetic data on large cohorts of subjects. The statistical analysis of such data is carried out with increasingly sophisticated techniques and represents a great computational challenge. Fortunately, increasing computational power in distributed architectures can be harnessed, if new neuroinformatics infrastructures are designed and training to use these new tools is provided. Combining a MapReduce framework (TomusBLOB) with machine learning algorithms (Scikit-learn library), we design a scalable analysis tool that can deal with non-parametric statistics on high-dimensional data. End-users describe the statistical procedure to perform and can then test the model on their own computers before running the very same code in the cloud at a larger scale. We illustrate the potential of our approach on real data with an experiment showing how the functional signal in subcortical brain regions can be significantly fit with genome-wide genotypes. This experiment demonstrates the scalability and the reliability of our framework in the cloud with a 2 weeks deployment on hundreds of virtual machines.

Performance implications from sizing a VM on multi-core systems: A Data analytic application s view

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lim, Seung-Hwan; Horey, James L; Begoli, Edmon

In this paper, we present a quantitative performance analysis of data analytics applications running on multi-core virtual machines. Such environments form the core of cloud computing. In addition, data analytics applications, such as Cassandra and Hadoop, are becoming increasingly popular on cloud computing platforms. This convergence necessitates a better understanding of the performance and cost implications of such hybrid systems. For example, the very rst step in hosting applications in virtualized environments, requires the user to con gure the number of virtual processors and the size of memory. To understand performance implications of this step, we benchmarked three Yahoo Cloudmore » Serving Benchmark (YCSB) workloads in a virtualized multi-core environment. Our measurements indicate that the performance of Cassandra for YCSB workloads does not heavily depend on the processing capacity of a system, while the size of the data set is critical to performance relative to allocated memory. We also identi ed a strong relationship between the running time of workloads and various hardware events (last level cache loads, misses, and CPU migrations). From this analysis, we provide several suggestions to improve the performance of data analytics applications running on cloud computing environments.« less

Development of a High Resolution Weather Forecast Model for Mesoamerica Using the NASA Nebula Cloud Computing Environment

NASA Astrophysics Data System (ADS)

Molthan, A.; Case, J.; Venner, J.; Moreno-Madriñán, M. J.; Delgado, F.

2012-12-01

Over the past two years, scientists in the Earth Science Office at NASA's Marshall Space Flight Center (MSFC) have explored opportunities to apply cloud computing concepts to support near real-time weather forecast modeling via the Weather Research and Forecasting (WRF) model. Collaborators at NASA's Short-term Prediction Research and Transition (SPoRT) Center and the SERVIR project at Marshall Space Flight Center have established a framework that provides high resolution, daily weather forecasts over Mesoamerica through use of the NASA Nebula Cloud Computing Platform at Ames Research Center. Supported by experts at Ames, staff at SPoRT and SERVIR have established daily forecasts complete with web graphics and a user interface that allows SERVIR partners access to high resolution depictions of weather in the next 48 hours, useful for monitoring and mitigating meteorological hazards such as thunderstorms, heavy precipitation, and tropical weather that can lead to other disasters such as flooding and landslides. This presentation will describe the framework for establishing and providing WRF forecasts, example applications of output provided via the SERVIR web portal, and early results of forecast model verification against available surface- and satellite-based observations.

Distributed storage and cloud computing: a test case

NASA Astrophysics Data System (ADS)

Piano, S.; Delia Ricca, G.

2014-06-01

Since 2003 the computing farm hosted by the INFN Tier3 facility in Trieste supports the activities of many scientific communities. Hundreds of jobs from 45 different VOs, including those of the LHC experiments, are processed simultaneously. Given that normally the requirements of the different computational communities are not synchronized, the probability that at any given time the resources owned by one of the participants are not fully utilized is quite high. A balanced compensation should in principle allocate the free resources to other users, but there are limits to this mechanism. In fact, the Trieste site may not hold the amount of data needed to attract enough analysis jobs, and even in that case there could be a lack of bandwidth for their access. The Trieste ALICE and CMS computing groups, in collaboration with other Italian groups, aim to overcome the limitations of existing solutions using two approaches: sharing the data among all the participants taking full advantage of GARR-X wide area networks (10 GB/s) and integrating the resources dedicated to batch analysis with the ones reserved for dynamic interactive analysis, through modern solutions as cloud computing.

Unified Geophysical Cloud Platform (UGCP) for Seismic Monitoring and other Geophysical Applications.

NASA Astrophysics Data System (ADS)

Synytsky, R.; Starovoit, Y. O.; Henadiy, S.; Lobzakov, V.; Kolesnikov, L.

2016-12-01

We present Unified Geophysical Cloud Platform (UGCP) or UniGeoCloud as an innovative approach for geophysical data processing in the Cloud environment with the ability to run any type of data processing software in isolated environment within the single Cloud platform. We've developed a simple and quick method of several open-source widely known software seismic packages (SeisComp3, Earthworm, Geotool, MSNoise) installation which does not require knowledge of system administration, configuration, OS compatibility issues etc. and other often annoying details preventing time wasting for system configuration work. Installation process is simplified as "mouse click" on selected software package from the Cloud market place. The main objective of the developed capability was the software tools conception with which users are able to design and install quickly their own highly reliable and highly available virtual IT-infrastructure for the organization of seismic (and in future other geophysical) data processing for either research or monitoring purposes. These tools provide access to any seismic station data available in open IP configuration from the different networks affiliated with different Institutions and Organizations. It allows also setting up your own network as you desire by selecting either regionally deployed stations or the worldwide global network based on stations selection form the global map. The processing software and products and research results could be easily monitored from everywhere using variety of user's devices form desk top computers to IT gadgets. Currents efforts of the development team are directed to achieve Scalability, Reliability and Sustainability (SRS) of proposed solutions allowing any user to run their applications with the confidence of no data loss and no failure of the monitoring or research software components. The system is suitable for quick rollout of NDC-in-Box software package developed for State Signatories and aimed for promotion of data processing collected by the IMS Network.

Towards a Low-Cost Real-Time Photogrammetric Landslide Monitoring System Utilising Mobile and Cloud Computing Technology

NASA Astrophysics Data System (ADS)

Chidburee, P.; Mills, J. P.; Miller, P. E.; Fieber, K. D.

2016-06-01

Close-range photogrammetric techniques offer a potentially low-cost approach in terms of implementation and operation for initial assessment and monitoring of landslide processes over small areas. In particular, the Structure-from-Motion (SfM) pipeline is now extensively used to help overcome many constraints of traditional digital photogrammetry, offering increased user-friendliness to nonexperts, as well as lower costs. However, a landslide monitoring approach based on the SfM technique also presents some potential drawbacks due to the difficulty in managing and processing a large volume of data in real-time. This research addresses the aforementioned issues by attempting to combine a mobile device with cloud computing technology to develop a photogrammetric measurement solution as part of a monitoring system for landslide hazard analysis. The research presented here focusses on (i) the development of an Android mobile application; (ii) the implementation of SfM-based open-source software in the Amazon cloud computing web service, and (iii) performance assessment through a simulated environment using data collected at a recognized landslide test site in North Yorkshire, UK. Whilst the landslide monitoring mobile application is under development, this paper describes experiments carried out to ensure effective performance of the system in the future. Investigations presented here describe the initial assessment of a cloud-implemented approach, which is developed around the well-known VisualSFM algorithm. Results are compared to point clouds obtained from alternative SfM 3D reconstruction approaches considering a commercial software solution (Agisoft PhotoScan) and a web-based system (Autodesk 123D Catch). Investigations demonstrate that the cloud-based photogrammetric measurement system is capable of providing results of centimeter-level accuracy, evidencing its potential to provide an effective approach for quantifying and analyzing landslide hazard at a local-scale.

Secure Data Access Control for Fog Computing Based on Multi-Authority Attribute-Based Signcryption with Computation Outsourcing and Attribute Revocation.

PubMed

Xu, Qian; Tan, Chengxiang; Fan, Zhijie; Zhu, Wenye; Xiao, Ya; Cheng, Fujia

2018-05-17

Nowadays, fog computing provides computation, storage, and application services to end users in the Internet of Things. One of the major concerns in fog computing systems is how fine-grained access control can be imposed. As a logical combination of attribute-based encryption and attribute-based signature, Attribute-based Signcryption (ABSC) can provide confidentiality and anonymous authentication for sensitive data and is more efficient than traditional "encrypt-then-sign" or "sign-then-encrypt" strategy. Thus, ABSC is suitable for fine-grained access control in a semi-trusted cloud environment and is gaining more and more attention recently. However, in many existing ABSC systems, the computation cost required for the end users in signcryption and designcryption is linear with the complexity of signing and encryption access policy. Moreover, only a single authority that is responsible for attribute management and key generation exists in the previous proposed ABSC schemes, whereas in reality, mostly, different authorities monitor different attributes of the user. In this paper, we propose OMDAC-ABSC, a novel data access control scheme based on Ciphertext-Policy ABSC, to provide data confidentiality, fine-grained control, and anonymous authentication in a multi-authority fog computing system. The signcryption and designcryption overhead for the user is significantly reduced by outsourcing the undesirable computation operations to fog nodes. The proposed scheme is proven to be secure in the standard model and can provide attribute revocation and public verifiability. The security analysis, asymptotic complexity comparison, and implementation results indicate that our construction can balance the security goals with practical efficiency in computation.

Confidentiality Protection of Digital Health Records in Cloud Computing.

PubMed

Chen, Shyh-Wei; Chiang, Dai Lun; Liu, Chia-Hui; Chen, Tzer-Shyong; Lai, Feipei; Wang, Huihui; Wei, Wei

2016-05-01

Electronic medical records containing confidential information were uploaded to the cloud. The cloud allows medical crews to access and manage the data and integration of medical records easily. This data system provides relevant information to medical personnel and facilitates and improve electronic medical record management and data transmission. A structure of cloud-based and patient-centered personal health record (PHR) is proposed in this study. This technique helps patients to manage their health information, such as appointment date with doctor, health reports, and a completed understanding of their own health conditions. It will create patients a positive attitudes to maintain the health. The patients make decision on their own for those whom has access to their records over a specific span of time specified by the patients. Storing data in the cloud environment can reduce costs and enhance the share of information, but the potential threat of information security should be taken into consideration. This study is proposing the cloud-based secure transmission mechanism is suitable for multiple users (like nurse aides, patients, and family members).

An Effective Mechanism for Virtual Machine Placement using Aco in IAAS Cloud

NASA Astrophysics Data System (ADS)

Shenbaga Moorthy, Rajalakshmi; Fareentaj, U.; Divya, T. K.

2017-08-01

Cloud computing provides an effective way to dynamically provide numerous resources to meet customer demands. A major challenging problem for cloud providers is designing efficient mechanisms for optimal virtual machine Placement (OVMP). Such mechanisms enable the cloud providers to effectively utilize their available resources and obtain higher profits. In order to provide appropriate resources to the clients an optimal virtual machine placement algorithm is proposed. Virtual machine placement is NP-Hard problem. Such NP-Hard problem can be solved using heuristic algorithm. In this paper, Ant Colony Optimization based virtual machine placement is proposed. Our proposed system focuses on minimizing the cost spending in each plan for hosting virtual machines in a multiple cloud provider environment and the response time of each cloud provider is monitored periodically, in such a way to minimize delay in providing the resources to the users. The performance of the proposed algorithm is compared with greedy mechanism. The proposed algorithm is simulated in Eclipse IDE. The results clearly show that the proposed algorithm minimizes the cost, response time and also number of migrations.

Objective and subjective quality assessment of geometry compression of reconstructed 3D humans in a 3D virtual room

NASA Astrophysics Data System (ADS)

Mekuria, Rufael; Cesar, Pablo; Doumanis, Ioannis; Frisiello, Antonella

2015-09-01

Compression of 3D object based video is relevant for 3D Immersive applications. Nevertheless, the perceptual aspects of the degradation introduced by codecs for meshes and point clouds are not well understood. In this paper we evaluate the subjective and objective degradations introduced by such codecs in a state of art 3D immersive virtual room. In the 3D immersive virtual room, users are captured with multiple cameras, and their surfaces are reconstructed as photorealistic colored/textured 3D meshes or point clouds. To test the perceptual effect of compression and transmission, we render degraded versions with different frame rates in different contexts (near/far) in the scene. A quantitative subjective study with 16 users shows that negligible distortion of decoded surfaces compared to the original reconstructions can be achieved in the 3D virtual room. In addition, a qualitative task based analysis in a full prototype field trial shows increased presence, emotion, user and state recognition of the reconstructed 3D Human representation compared to animated computer avatars.

Users' instructions for the NASA/MSFC cloud-rise preprocessor program, version 6, and the NASA/MSFC multilayer diffusion program, version 6: Research version for Univac 1108 system

NASA Technical Reports Server (NTRS)

Bjorklund, J. R.

1978-01-01

The cloud-rise preprocessor and multilayer diffusion computer programs were used by NASA in predicting concentrations and dosages downwind from normal and abnormal launches of rocket vehicles. These programs incorporated: (1) the latest data for the heat content and chemistry of rocket exhaust clouds; (2) provision for the automated calculation of surface water pH due to deposition of HCl from precipitation scavenging; (3) provision for automated calculation of concentration and dosage parameters at any level within the vertical grounds for which meteorological inputs have been specified; and (4) provision for execution of multiple cases of meteorological data. Procedures used to automatically calculate wind direction shear in a layer were updated.

Researching, Evaluating, and Choosing a Backup Service in the Cloud

ERIC Educational Resources Information Center

Hastings, Robin

2012-01-01

Backups are a modern fact of life. Every organization that has any kind of computing technology (and that is all of them these days) needs to back up its data in case of technological or user errors. Traditionally, large-scale backups have been done via an internal or external tape drive that takes magnetic tapes (minicassettes, essentially) and…

Development of an Innovative Interactive Virtual Classroom System for K-12 Education Using Google App Engine

ERIC Educational Resources Information Center

Mumba, Frackson; Zhu, Mengxia

2013-01-01

This paper presents a Simulation-based interactive Virtual ClassRoom web system (SVCR: www.vclasie.com) powered by the state-of-the-art cloud computing technology from Google SVCR integrates popular free open-source math, science and engineering simulations and provides functions such as secure user access control and management of courses,…

A Toolkit for ARB to Integrate Custom Databases and Externally Built Phylogenies

DOE PAGES

Essinger, Steven D.; Reichenberger, Erin; Morrison, Calvin; ...

2015-01-21

Researchers are perpetually amassing biological sequence data. The computational approaches employed by ecologists for organizing this data (e.g. alignment, phylogeny, etc.) typically scale nonlinearly in execution time with the size of the dataset. This often serves as a bottleneck for processing experimental data since many molecular studies are characterized by massive datasets. To keep up with experimental data demands, ecologists are forced to choose between continually upgrading expensive in-house computer hardware or outsourcing the most demanding computations to the cloud. Outsourcing is attractive since it is the least expensive option, but does not necessarily allow direct user interaction with themore » data for exploratory analysis. Desktop analytical tools such as ARB are indispensable for this purpose, but they do not necessarily offer a convenient solution for the coordination and integration of datasets between local and outsourced destinations. Therefore, researchers are currently left with an undesirable tradeoff between computational throughput and analytical capability. To mitigate this tradeoff we introduce a software package to leverage the utility of the interactive exploratory tools offered by ARB with the computational throughput of cloud-based resources. Our pipeline serves as middleware between the desktop and the cloud allowing researchers to form local custom databases containing sequences and metadata from multiple resources and a method for linking data outsourced for computation back to the local database. Furthermore, a tutorial implementation of the toolkit is provided in the supporting information, S1 Tutorial.« less

A Toolkit for ARB to Integrate Custom Databases and Externally Built Phylogenies

PubMed Central

Essinger, Steven D.; Reichenberger, Erin; Morrison, Calvin; Blackwood, Christopher B.; Rosen, Gail L.

2015-01-01

Researchers are perpetually amassing biological sequence data. The computational approaches employed by ecologists for organizing this data (e.g. alignment, phylogeny, etc.) typically scale nonlinearly in execution time with the size of the dataset. This often serves as a bottleneck for processing experimental data since many molecular studies are characterized by massive datasets. To keep up with experimental data demands, ecologists are forced to choose between continually upgrading expensive in-house computer hardware or outsourcing the most demanding computations to the cloud. Outsourcing is attractive since it is the least expensive option, but does not necessarily allow direct user interaction with the data for exploratory analysis. Desktop analytical tools such as ARB are indispensable for this purpose, but they do not necessarily offer a convenient solution for the coordination and integration of datasets between local and outsourced destinations. Therefore, researchers are currently left with an undesirable tradeoff between computational throughput and analytical capability. To mitigate this tradeoff we introduce a software package to leverage the utility of the interactive exploratory tools offered by ARB with the computational throughput of cloud-based resources. Our pipeline serves as middleware between the desktop and the cloud allowing researchers to form local custom databases containing sequences and metadata from multiple resources and a method for linking data outsourced for computation back to the local database. A tutorial implementation of the toolkit is provided in the supporting information, S1 Tutorial. Availability: http://www.ece.drexel.edu/gailr/EESI/tutorial.php. PMID:25607539

Supporting the scientific lifecycle through cloud services

NASA Astrophysics Data System (ADS)

Gensch, S.; Klump, J. F.; Bertelmann, R.; Braune, C.

2014-12-01

Cloud computing has made resources and applications available for numerous use cases ranging from business processes in the private sector to scientific applications. Developers have created tools for data management, collaborative writing, social networking, data access and visualization, project management and many more; either for free or as paid premium services with additional or extended features. Scientists have begun to incorporate tools that fit their needs into their daily work. To satisfy specialized needs, some cloud applications specifically address the needs of scientists for sharing research data, literature search, laboratory documentation, or data visualization. Cloud services may vary in extent, user coverage, and inter-service integration and are also at risk of being abandonend or changed by the service providers making changes to their business model, or leaving the field entirely.Within the project Academic Enterprise Cloud we examine cloud based services that support the research lifecycle, using feature models to describe key properties in the areas of infrastructure and service provision, compliance to legal regulations, and data curation. Emphasis is put on the term Enterprise as to establish an academic cloud service provider infrastructure that satisfies demands of the research community through continious provision across the whole cloud stack. This could enable the research community to be independent from service providers regarding changes to terms of service and ensuring full control of its extent and usage. This shift towards a self-empowered scientific cloud provider infrastructure and its community raises implications about feasability of provision and overall costs. Legal aspects and licensing issues have to be considered, when moving data into cloud services, especially when personal data is involved.Educating researchers about cloud based tools is important to help in the transition towards effective and safe use. Scientists can benefit from the provision of standard services, like weblog and website creation, virtual machine deployments, and groupware provision using cloud based app store-like portals. And, other than in an industrial environment, researchers will want to keep their existing user profile when moving from one institution to another.

Cloud Computing

DTIC Science & Technology

2010-04-29

Cloud Computing   The answer, my friend, is blowing in the wind.   The answer is blowing in the wind. 1Bingue ‐ Cook  Cloud   Computing  STSC 2010... Cloud   Computing  STSC 2010 Objectives • Define the cloud    • Risks of  cloud   computing f l d i• Essence o  c ou  comput ng • Deployed clouds in DoD 3Bingue...Cook  Cloud   Computing  STSC 2010 Definitions of Cloud Computing       Cloud   computing  is a model for enabling  b d d ku

Feeding People's Curiosity: Leveraging the Cloud for Automatic Dissemination of Mars Images

NASA Technical Reports Server (NTRS)

Knight, David; Powell, Mark

2013-01-01

Smartphones and tablets have made wireless computing ubiquitous, and users expect instant, on-demand access to information. The Mars Science Laboratory (MSL) operations software suite, MSL InterfaCE (MSLICE), employs a different back-end image processing architecture compared to that of the Mars Exploration Rovers (MER) in order to better satisfy modern consumer-driven usage patterns and to offer greater server-side flexibility. Cloud services are a centerpiece of the server-side architecture that allows new image data to be delivered automatically to both scientists using MSLICE and the general public through the MSL website (http://mars.jpl.nasa.gov/msl/).

Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications

PubMed Central

Pérez-Torres, Rafael; Torres-Huitzil, César; Galeana-Zapién, Hiram

2016-01-01

The tracking of frequently visited places, also known as stay points, is a critical feature in location-aware mobile applications as a way to adapt the information and services provided to smartphones users according to their moving patterns. Location based applications usually employ the GPS receiver along with Wi-Fi hot-spots and cellular cell tower mechanisms for estimating user location. Typically, fine-grained GPS location data are collected by the smartphone and transferred to dedicated servers for trajectory analysis and stay points detection. Such Mobile Cloud Computing approach has been successfully employed for extending smartphone’s battery lifetime by exchanging computation costs, assuming that on-device stay points detection is prohibitive. In this article, we propose and validate the feasibility of having an alternative event-driven mechanism for stay points detection that is executed fully on-device, and that provides higher energy savings by avoiding communication costs. Our solution is encapsulated in a sensing middleware for Android smartphones, where a stream of GPS location updates is collected in the background, supporting duty cycling schemes, and incrementally analyzed following an event-driven paradigm for stay points detection. To evaluate the performance of the proposed middleware, real world experiments were conducted under different stress levels, validating its power efficiency when compared against a Mobile Cloud Computing oriented solution. PMID:27754388

SAR processing in the cloud for oil detection in the Arctic

NASA Astrophysics Data System (ADS)

Garron, J.; Stoner, C.; Meyer, F. J.

2016-12-01

A new world of opportunity is being thawed from the ice of the Arctic, driven by decreased persistent Arctic sea-ice cover, increases in shipping, tourism, natural resource development. Tools that can automatically monitor key sea ice characteristics and potential oil spills are essential for safe passage in these changing waters. Synthetic aperture radar (SAR) data can be used to discriminate sea ice types and oil on the ocean surface and also for feature tracking. Additionally, SAR can image the earth through the night and most weather conditions. SAR data is volumetrically large and requires significant computing power to manipulate. Algorithms designed to identify key environmental features, like oil spills, in SAR imagery require secondary processing, and are computationally intensive, which can functionally limit their application in a real-time setting. Cloud processing is designed to manage big data and big data processing jobs by means of small cycles of off-site computations, eliminating up-front hardware costs. Pairing SAR data with cloud processing has allowed us to create and solidify a processing pipeline for SAR data products in the cloud to compare operational algorithms efficiency and effectiveness when run using an Alaska Satellite Facility (ASF) defined Amazon Machine Image (AMI). The products created from this secondary processing, were compared to determine which algorithm was most accurate in Arctic feature identification, and what operational conditions were required to produce the results on the ASF defined AMI. Results will be used to inform a series of recommendations to oil-spill response data managers and SAR users interested in expanding their analytical computing power.

Wildlife in the cloud: a new approach for engaging stakeholders in wildlife management.

PubMed

Chapron, Guillaume

2015-11-01

Research in wildlife management increasingly relies on quantitative population models. However, a remaining challenge is to have end-users, who are often alienated by mathematics, benefiting from this research. I propose a new approach, 'wildlife in the cloud,' to enable active learning by practitioners from cloud-based ecological models whose complexity remains invisible to the user. I argue that this concept carries the potential to overcome limitations of desktop-based software and allows new understandings of human-wildlife systems. This concept is illustrated by presenting an online decision-support tool for moose management in areas with predators in Sweden. The tool takes the form of a user-friendly cloud-app through which users can compare the effects of alternative management decisions, and may feed into adjustment of their hunting strategy. I explain how the dynamic nature of cloud-apps opens the door to different ways of learning, informed by ecological models that can benefit both users and researchers.

The Dynamo package for tomography and subtomogram averaging: components for MATLAB, GPU computing and EC2 Amazon Web Services

PubMed Central

Castaño-Díez, Daniel

2017-01-01

Dynamo is a package for the processing of tomographic data. As a tool for subtomogram averaging, it includes different alignment and classification strategies. Furthermore, its data-management module allows experiments to be organized in groups of tomograms, while offering specialized three-dimensional tomographic browsers that facilitate visualization, location of regions of interest, modelling and particle extraction in complex geometries. Here, a technical description of the package is presented, focusing on its diverse strategies for optimizing computing performance. Dynamo is built upon mbtools (middle layer toolbox), a general-purpose MATLAB library for object-oriented scientific programming specifically developed to underpin Dynamo but usable as an independent tool. Its structure intertwines a flexible MATLAB codebase with precompiled C++ functions that carry the burden of numerically intensive operations. The package can be delivered as a precompiled standalone ready for execution without a MATLAB license. Multicore parallelization on a single node is directly inherited from the high-level parallelization engine provided for MATLAB, automatically imparting a balanced workload among the threads in computationally intense tasks such as alignment and classification, but also in logistic-oriented tasks such as tomogram binning and particle extraction. Dynamo supports the use of graphical processing units (GPUs), yielding considerable speedup factors both for native Dynamo procedures (such as the numerically intensive subtomogram alignment) and procedures defined by the user through its MATLAB-based GPU library for three-dimensional operations. Cloud-based virtual computing environments supplied with a pre-installed version of Dynamo can be publicly accessed through the Amazon Elastic Compute Cloud (EC2), enabling users to rent GPU computing time on a pay-as-you-go basis, thus avoiding upfront investments in hardware and longterm software maintenance. PMID:28580909

The Dynamo package for tomography and subtomogram averaging: components for MATLAB, GPU computing and EC2 Amazon Web Services.

PubMed

Castaño-Díez, Daniel

2017-06-01

Dynamo is a package for the processing of tomographic data. As a tool for subtomogram averaging, it includes different alignment and classification strategies. Furthermore, its data-management module allows experiments to be organized in groups of tomograms, while offering specialized three-dimensional tomographic browsers that facilitate visualization, location of regions of interest, modelling and particle extraction in complex geometries. Here, a technical description of the package is presented, focusing on its diverse strategies for optimizing computing performance. Dynamo is built upon mbtools (middle layer toolbox), a general-purpose MATLAB library for object-oriented scientific programming specifically developed to underpin Dynamo but usable as an independent tool. Its structure intertwines a flexible MATLAB codebase with precompiled C++ functions that carry the burden of numerically intensive operations. The package can be delivered as a precompiled standalone ready for execution without a MATLAB license. Multicore parallelization on a single node is directly inherited from the high-level parallelization engine provided for MATLAB, automatically imparting a balanced workload among the threads in computationally intense tasks such as alignment and classification, but also in logistic-oriented tasks such as tomogram binning and particle extraction. Dynamo supports the use of graphical processing units (GPUs), yielding considerable speedup factors both for native Dynamo procedures (such as the numerically intensive subtomogram alignment) and procedures defined by the user through its MATLAB-based GPU library for three-dimensional operations. Cloud-based virtual computing environments supplied with a pre-installed version of Dynamo can be publicly accessed through the Amazon Elastic Compute Cloud (EC2), enabling users to rent GPU computing time on a pay-as-you-go basis, thus avoiding upfront investments in hardware and longterm software maintenance.

High-performance integrated virtual environment (HIVE): a robust infrastructure for next-generation sequence data analysis

PubMed Central

Simonyan, Vahan; Chumakov, Konstantin; Dingerdissen, Hayley; Faison, William; Goldweber, Scott; Golikov, Anton; Gulzar, Naila; Karagiannis, Konstantinos; Vinh Nguyen Lam, Phuc; Maudru, Thomas; Muravitskaja, Olesja; Osipova, Ekaterina; Pan, Yang; Pschenichnov, Alexey; Rostovtsev, Alexandre; Santana-Quintero, Luis; Smith, Krista; Thompson, Elaine E.; Tkachenko, Valery; Torcivia-Rodriguez, John; Wan, Quan; Wang, Jing; Wu, Tsung-Jung; Wilson, Carolyn; Mazumder, Raja

2016-01-01

The High-performance Integrated Virtual Environment (HIVE) is a distributed storage and compute environment designed primarily to handle next-generation sequencing (NGS) data. This multicomponent cloud infrastructure provides secure web access for authorized users to deposit, retrieve, annotate and compute on NGS data, and to analyse the outcomes using web interface visual environments appropriately built in collaboration with research and regulatory scientists and other end users. Unlike many massively parallel computing environments, HIVE uses a cloud control server which virtualizes services, not processes. It is both very robust and flexible due to the abstraction layer introduced between computational requests and operating system processes. The novel paradigm of moving computations to the data, instead of moving data to computational nodes, has proven to be significantly less taxing for both hardware and network infrastructure. The honeycomb data model developed for HIVE integrates metadata into an object-oriented model. Its distinction from other object-oriented databases is in the additional implementation of a unified application program interface to search, view and manipulate data of all types. This model simplifies the introduction of new data types, thereby minimizing the need for database restructuring and streamlining the development of new integrated information systems. The honeycomb model employs a highly secure hierarchical access control and permission system, allowing determination of data access privileges in a finely granular manner without flooding the security subsystem with a multiplicity of rules. HIVE infrastructure will allow engineers and scientists to perform NGS analysis in a manner that is both efficient and secure. HIVE is actively supported in public and private domains, and project collaborations are welcomed. Database URL: https://hive.biochemistry.gwu.edu PMID:26989153

High-performance integrated virtual environment (HIVE): a robust infrastructure for next-generation sequence data analysis.

PubMed

Simonyan, Vahan; Chumakov, Konstantin; Dingerdissen, Hayley; Faison, William; Goldweber, Scott; Golikov, Anton; Gulzar, Naila; Karagiannis, Konstantinos; Vinh Nguyen Lam, Phuc; Maudru, Thomas; Muravitskaja, Olesja; Osipova, Ekaterina; Pan, Yang; Pschenichnov, Alexey; Rostovtsev, Alexandre; Santana-Quintero, Luis; Smith, Krista; Thompson, Elaine E; Tkachenko, Valery; Torcivia-Rodriguez, John; Voskanian, Alin; Wan, Quan; Wang, Jing; Wu, Tsung-Jung; Wilson, Carolyn; Mazumder, Raja

2016-01-01

The High-performance Integrated Virtual Environment (HIVE) is a distributed storage and compute environment designed primarily to handle next-generation sequencing (NGS) data. This multicomponent cloud infrastructure provides secure web access for authorized users to deposit, retrieve, annotate and compute on NGS data, and to analyse the outcomes using web interface visual environments appropriately built in collaboration with research and regulatory scientists and other end users. Unlike many massively parallel computing environments, HIVE uses a cloud control server which virtualizes services, not processes. It is both very robust and flexible due to the abstraction layer introduced between computational requests and operating system processes. The novel paradigm of moving computations to the data, instead of moving data to computational nodes, has proven to be significantly less taxing for both hardware and network infrastructure.The honeycomb data model developed for HIVE integrates metadata into an object-oriented model. Its distinction from other object-oriented databases is in the additional implementation of a unified application program interface to search, view and manipulate data of all types. This model simplifies the introduction of new data types, thereby minimizing the need for database restructuring and streamlining the development of new integrated information systems. The honeycomb model employs a highly secure hierarchical access control and permission system, allowing determination of data access privileges in a finely granular manner without flooding the security subsystem with a multiplicity of rules. HIVE infrastructure will allow engineers and scientists to perform NGS analysis in a manner that is both efficient and secure. HIVE is actively supported in public and private domains, and project collaborations are welcomed. Database URL: https://hive.biochemistry.gwu.edu. © The Author(s) 2016. Published by Oxford University Press.

Cloud-Based Numerical Weather Prediction for Near Real-Time Forecasting and Disaster Response

NASA Technical Reports Server (NTRS)

Molthan, Andrew; Case, Jonathan; Venners, Jason; Schroeder, Richard; Checchi, Milton; Zavodsky, Bradley; Limaye, Ashutosh; O'Brien, Raymond

2015-01-01

The use of cloud computing resources continues to grow within the public and private sector components of the weather enterprise as users become more familiar with cloud-computing concepts, and competition among service providers continues to reduce costs and other barriers to entry. Cloud resources can also provide capabilities similar to high-performance computing environments, supporting multi-node systems required for near real-time, regional weather predictions. Referred to as "Infrastructure as a Service", or IaaS, the use of cloud-based computing hardware in an on-demand payment system allows for rapid deployment of a modeling system in environments lacking access to a large, supercomputing infrastructure. Use of IaaS capabilities to support regional weather prediction may be of particular interest to developing countries that have not yet established large supercomputing resources, but would otherwise benefit from a regional weather forecasting capability. Recently, collaborators from NASA Marshall Space Flight Center and Ames Research Center have developed a scripted, on-demand capability for launching the NOAA/NWS Science and Training Resource Center (STRC) Environmental Modeling System (EMS), which includes pre-compiled binaries of the latest version of the Weather Research and Forecasting (WRF) model. The WRF-EMS provides scripting for downloading appropriate initial and boundary conditions from global models, along with higher-resolution vegetation, land surface, and sea surface temperature data sets provided by the NASA Short-term Prediction Research and Transition (SPoRT) Center. This presentation will provide an overview of the modeling system capabilities and benchmarks performed on the Amazon Elastic Compute Cloud (EC2) environment. In addition, the presentation will discuss future opportunities to deploy the system in support of weather prediction in developing countries supported by NASA's SERVIR Project, which provides capacity building activities in environmental monitoring and prediction across a growing number of regional hubs throughout the world. Capacity-building applications that extend numerical weather prediction to developing countries are intended to provide near real-time applications to benefit public health, safety, and economic interests, but may have a greater impact during disaster events by providing a source for local predictions of weather-related hazards, or impacts that local weather events may have during the recovery phase.

IoT gateways, cloud and the last mile for energy efficiency and sustainability in the era of CPS expansion: "A bot is irrigating my farm.. "

NASA Astrophysics Data System (ADS)

Papageorgas, Panagiotis G.; Agavanakis, Kyriakos; Dogas, Ioannis; Piromalis, Dimitrios D.

2018-05-01

A cloud-based architecture is presented for the internetworking of sensors and actuators through a universal gateway, network server and application user interface design. The proposed approach targets to Energy Efficiency and sustainability in a holistic way, by integrating an open-source test bed prototype based on long-range low-bandwidth wireless networking technology for sensing and actuation as the elementary block of a viable, cost-effective and reliable solution. The prototype presented is capable of supporting both sensors and actuators, processing data locally and transmitting the results of the imposed computations to a higher level node. Additionally, it is combined with a service-oriented architecture and involves publish/subscribe middleware protocols and cloud technology to confront with the system needs in terms of data volume and processing power. In this context, the integration of instant message (chat) services is demonstrated so that they can be part of an emerging global-scope eco-system of Cyber-Physical Systems to support a wide variety of IoT applications, with strong advantages such as usability, scalability and security, while adopting a unified gateway design and a simple - yet powerful - user interface.

An Overview of Cloud Computing in Distributed Systems

NASA Astrophysics Data System (ADS)

Divakarla, Usha; Kumari, Geetha

2010-11-01

Cloud computing is the emerging trend in the field of distributed computing. Cloud computing evolved from grid computing and distributed computing. Cloud plays an important role in huge organizations in maintaining huge data with limited resources. Cloud also helps in resource sharing through some specific virtual machines provided by the cloud service provider. This paper gives an overview of the cloud organization and some of the basic security issues pertaining to the cloud.

User-driven Cloud Implementation of environmental models and data for all

NASA Astrophysics Data System (ADS)

Gurney, R. J.; Percy, B. J.; Elkhatib, Y.; Blair, G. S.

2014-12-01

Environmental data and models come from disparate sources over a variety of geographical and temporal scales with different resolutions and data standards, often including terabytes of data and model simulations. Unfortunately, these data and models tend to remain solely within the custody of the private and public organisations which create the data, and the scientists who build models and generate results. Although many models and datasets are theoretically available to others, the lack of ease of access tends to keep them out of reach of many. We have developed an intuitive web-based tool that utilises environmental models and datasets located in a cloud to produce results that are appropriate to the user. Storyboards showing the interfaces and visualisations have been created for each of several exemplars. A library of virtual machine images has been prepared to serve these exemplars. Each virtual machine image has been tailored to run computer models appropriate to the end user. Two approaches have been used; first as RESTful web services conforming to the Open Geospatial Consortium (OGC) Web Processing Service (WPS) interface standard using the Python-based PyWPS; second, a MySQL database interrogated using PHP code. In all cases, the web client sends the server an HTTP GET request to execute the process with a number of parameter values and, once execution terminates, an XML or JSON response is sent back and parsed at the client side to extract the results. All web services are stateless, i.e. application state is not maintained by the server, reducing its operational overheads and simplifying infrastructure management tasks such as load balancing and failure recovery. A hybrid cloud solution has been used with models and data sited on both private and public clouds. The storyboards have been transformed into intuitive web interfaces at the client side using HTML, CSS and JavaScript, utilising plug-ins such as jQuery and Flot (for graphics), and Google Maps APIs. We have demonstrated that a cloud infrastructure can be used to assemble a virtual research environment that, coupled with a user-driven development approach, is able to cater to the needs of a wide range of user groups, from domain experts to concerned members of the general public.

Leveraging Cloud Technology to Provide a Responsive, Reliable and Scalable Backend for the Virtual Ice Sheet Laboratory Using the Ice Sheet System Model and Amazon's Elastic Compute Cloud

NASA Astrophysics Data System (ADS)

Perez, G. L.; Larour, E. Y.; Halkides, D. J.; Cheng, D. L. C.

2015-12-01

The Virtual Ice Sheet Laboratory(VISL) is a Cryosphere outreach effort byscientists at the Jet Propulsion Laboratory(JPL) in Pasadena, CA, Earth and SpaceResearch(ESR) in Seattle, WA, and the University of California at Irvine (UCI), with the goal of providing interactive lessons for K-12 and college level students,while conforming to STEM guidelines. At the core of VISL is the Ice Sheet System Model(ISSM), an open-source project developed jointlyat JPL and UCI whose main purpose is to model the evolution of the polar ice caps in Greenland and Antarctica. By using ISSM, VISL students have access tostate-of-the-art modeling software that is being used to conduct scientificresearch by users all over the world. However, providing this functionality isby no means simple. The modeling of ice sheets in response to sea and atmospheric temperatures, among many other possible parameters, requiressignificant computational resources. Furthermore, this service needs to beresponsive and capable of handling burst requests produced by classrooms ofstudents. Cloud computing providers represent a burgeoning industry. With majorinvestments by tech giants like Amazon, Google and Microsoft, it has never beeneasier or more affordable to deploy computational elements on-demand. This isexactly what VISL needs and ISSM is capable of. Moreover, this is a promisingalternative to investing in expensive and rapidly devaluing hardware.

Supporting reputation based trust management enhancing security layer for cloud service models

NASA Astrophysics Data System (ADS)

Karthiga, R.; Vanitha, M.; Sumaiya Thaseen, I.; Mangaiyarkarasi, R.

2017-11-01

In the existing system trust between cloud providers and consumers is inadequate to establish the service level agreement though the consumer’s response is good cause to assess the overall reliability of cloud services. Investigators recognized the significance of trust can be managed and security can be provided based on feedback collected from participant. In this work a face recognition system that helps to identify the user effectively. So we use an image comparison algorithm where the user face is captured during registration time and get stored in database. With that original image we compare it with the sample image that is already stored in database. If both the image get matched then the users are identified effectively. When the confidential data are subcontracted to the cloud, data holders will become worried about the confidentiality of their data in the cloud. Encrypting the data before subcontracting has been regarded as the important resources of keeping user data privacy beside the cloud server. So in order to keep the data secure we use an AES algorithm. Symmetric-key algorithms practice a shared key concept, keeping data secret requires keeping this key secret. So only the user with private key can decrypt data.

Closha: bioinformatics workflow system for the analysis of massive sequencing data.

PubMed

Ko, GunHwan; Kim, Pan-Gyu; Yoon, Jongcheol; Han, Gukhee; Park, Seong-Jin; Song, Wangho; Lee, Byungwook

2018-02-19

While next-generation sequencing (NGS) costs have fallen in recent years, the cost and complexity of computation remain substantial obstacles to the use of NGS in bio-medical care and genomic research. The rapidly increasing amounts of data available from the new high-throughput methods have made data processing infeasible without automated pipelines. The integration of data and analytic resources into workflow systems provides a solution to the problem by simplifying the task of data analysis. To address this challenge, we developed a cloud-based workflow management system, Closha, to provide fast and cost-effective analysis of massive genomic data. We implemented complex workflows making optimal use of high-performance computing clusters. Closha allows users to create multi-step analyses using drag and drop functionality and to modify the parameters of pipeline tools. Users can also import the Galaxy pipelines into Closha. Closha is a hybrid system that enables users to use both analysis programs providing traditional tools and MapReduce-based big data analysis programs simultaneously in a single pipeline. Thus, the execution of analytics algorithms can be parallelized, speeding up the whole process. We also developed a high-speed data transmission solution, KoDS, to transmit a large amount of data at a fast rate. KoDS has a file transfer speed of up to 10 times that of normal FTP and HTTP. The computer hardware for Closha is 660 CPU cores and 800 TB of disk storage, enabling 500 jobs to run at the same time. Closha is a scalable, cost-effective, and publicly available web service for large-scale genomic data analysis. Closha supports the reliable and highly scalable execution of sequencing analysis workflows in a fully automated manner. Closha provides a user-friendly interface to all genomic scientists to try to derive accurate results from NGS platform data. The Closha cloud server is freely available for use from http://closha.kobic.re.kr/ .

Software architecture and design of the web services facilitating climate model diagnostic analysis

NASA Astrophysics Data System (ADS)

Pan, L.; Lee, S.; Zhang, J.; Tang, B.; Zhai, C.; Jiang, J. H.; Wang, W.; Bao, Q.; Qi, M.; Kubar, T. L.; Teixeira, J.

2015-12-01

Climate model diagnostic analysis is a computationally- and data-intensive task because it involves multiple numerical model outputs and satellite observation data that can both be high resolution. We have built an online tool that facilitates this process. The tool is called Climate Model Diagnostic Analyzer (CMDA). It employs the web service technology and provides a web-based user interface. The benefits of these choices include: (1) No installation of any software other than a browser, hence it is platform compatable; (2) Co-location of computation and big data on the server side, and small results and plots to be downloaded on the client side, hence high data efficiency; (3) multi-threaded implementation to achieve parallel performance on multi-core servers; and (4) cloud deployment so each user has a dedicated virtual machine. In this presentation, we will focus on the computer science aspects of this tool, namely the architectural design, the infrastructure of the web services, the implementation of the web-based user interface, the mechanism of provenance collection, the approach to virtualization, and the Amazon Cloud deployment. As an example, We will describe our methodology to transform an existing science application code into a web service using a Python wrapper interface and Python web service frameworks (i.e., Flask, Gunicorn, and Tornado). Another example is the use of Docker, a light-weight virtualization container, to distribute and deploy CMDA onto an Amazon EC2 instance. Our tool of CMDA has been successfully used in the 2014 Summer School hosted by the JPL Center for Climate Science. Students had positive feedbacks in general and we will report their comments. An enhanced version of CMDA with several new features, some requested by the 2014 students, will be used in the 2015 Summer School soon.

Future of Department of Defense Cloud Computing Amid Cultural Confusion

DTIC Science & Technology

2013-03-01

enterprise cloud - computing environment and transition to a public cloud service provider. Services have started the development of individual cloud - computing environments...endorsing cloud computing . It addresses related issues in matters of service culture changes and how strategic leaders will dictate the future of cloud ...through data center consolidation and individual Service provided cloud computing .

Secure Data Access Control for Fog Computing Based on Multi-Authority Attribute-Based Signcryption with Computation Outsourcing and Attribute Revocation

PubMed Central

Xu, Qian; Tan, Chengxiang; Fan, Zhijie; Zhu, Wenye; Xiao, Ya; Cheng, Fujia

2018-01-01

Nowadays, fog computing provides computation, storage, and application services to end users in the Internet of Things. One of the major concerns in fog computing systems is how fine-grained access control can be imposed. As a logical combination of attribute-based encryption and attribute-based signature, Attribute-based Signcryption (ABSC) can provide confidentiality and anonymous authentication for sensitive data and is more efficient than traditional “encrypt-then-sign” or “sign-then-encrypt” strategy. Thus, ABSC is suitable for fine-grained access control in a semi-trusted cloud environment and is gaining more and more attention recently. However, in many existing ABSC systems, the computation cost required for the end users in signcryption and designcryption is linear with the complexity of signing and encryption access policy. Moreover, only a single authority that is responsible for attribute management and key generation exists in the previous proposed ABSC schemes, whereas in reality, mostly, different authorities monitor different attributes of the user. In this paper, we propose OMDAC-ABSC, a novel data access control scheme based on Ciphertext-Policy ABSC, to provide data confidentiality, fine-grained control, and anonymous authentication in a multi-authority fog computing system. The signcryption and designcryption overhead for the user is significantly reduced by outsourcing the undesirable computation operations to fog nodes. The proposed scheme is proven to be secure in the standard model and can provide attribute revocation and public verifiability. The security analysis, asymptotic complexity comparison, and implementation results indicate that our construction can balance the security goals with practical efficiency in computation. PMID:29772840

Progressive data transmission for anatomical landmark detection in a cloud.

PubMed

Sofka, M; Ralovich, K; Zhang, J; Zhou, S K; Comaniciu, D

2012-01-01

In the concept of cloud-computing-based systems, various authorized users have secure access to patient records from a number of care delivery organizations from any location. This creates a growing need for remote visualization, advanced image processing, state-of-the-art image analysis, and computer aided diagnosis. This paper proposes a system of algorithms for automatic detection of anatomical landmarks in 3D volumes in the cloud computing environment. The system addresses the inherent problem of limited bandwidth between a (thin) client, data center, and data analysis server. The problem of limited bandwidth is solved by a hierarchical sequential detection algorithm that obtains data by progressively transmitting only image regions required for processing. The client sends a request to detect a set of landmarks for region visualization or further analysis. The algorithm running on the data analysis server obtains a coarse level image from the data center and generates landmark location candidates. The candidates are then used to obtain image neighborhood regions at a finer resolution level for further detection. This way, the landmark locations are hierarchically and sequentially detected and refined. Only image regions surrounding landmark location candidates need to be trans- mitted during detection. Furthermore, the image regions are lossy compressed with JPEG 2000. Together, these properties amount to at least 30 times bandwidth reduction while achieving similar accuracy when compared to an algorithm using the original data. The hierarchical sequential algorithm with progressive data transmission considerably reduces bandwidth requirements in cloud-based detection systems.

Reducing Time to Science: Unidata and JupyterHub Technology Using the Jetstream Cloud

NASA Astrophysics Data System (ADS)

Chastang, J.; Signell, R. P.; Fischer, J. L.

2017-12-01

Cloud computing can accelerate scientific workflows, discovery, and collaborations by reducing research and data friction. We describe the deployment of Unidata and JupyterHub technologies on the NSF-funded XSEDE Jetstream cloud. With the aid of virtual machines and Docker technology, we deploy a Unidata JupyterHub server co-located with a Local Data Manager (LDM), THREDDS data server (TDS), and RAMADDA geoscience content management system. We provide Jupyter Notebooks and the pre-built Python environments needed to run them. The notebooks can be used for instruction and as templates for scientific experimentation and discovery. We also supply a large quantity of NCEP forecast model results to allow data-proximate analysis and visualization. In addition, users can transfer data using Globus command line tools, and perform their own data-proximate analysis and visualization with Notebook technology. These data can be shared with others via a dedicated TDS server for scientific distribution and collaboration. There are many benefits of this approach. Not only is the cloud computing environment fast, reliable and scalable, but scientists can analyze, visualize, and share data using only their web browser. No local specialized desktop software or a fast internet connection is required. This environment will enable scientists to spend less time managing their software and more time doing science.

SAME4HPC: A Promising Approach in Building a Scalable and Mobile Environment for High-Performance Computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Karthik, Rajasekar

2014-01-01

In this paper, an architecture for building Scalable And Mobile Environment For High-Performance Computing with spatial capabilities called SAME4HPC is described using cutting-edge technologies and standards such as Node.js, HTML5, ECMAScript 6, and PostgreSQL 9.4. Mobile devices are increasingly becoming powerful enough to run high-performance apps. At the same time, there exist a significant number of low-end and older devices that rely heavily on the server or the cloud infrastructure to do the heavy lifting. Our architecture aims to support both of these types of devices to provide high-performance and rich user experience. A cloud infrastructure consisting of OpenStack withmore » Ubuntu, GeoServer, and high-performance JavaScript frameworks are some of the key open-source and industry standard practices that has been adopted in this architecture.« less

The effective use of virtualization for selection of data centers in a cloud computing environment

NASA Astrophysics Data System (ADS)

Kumar, B. Santhosh; Parthiban, Latha

2018-04-01

Data centers are the places which consist of network of remote servers to store, access and process the data. Cloud computing is a technology where users worldwide will submit the tasks and the service providers will direct the requests to the data centers which are responsible for execution of tasks. The servers in the data centers need to employ the virtualization concept so that multiple tasks can be executed simultaneously. In this paper we proposed an algorithm for data center selection based on energy of virtual machines created in server. The virtualization energy in each of the server is calculated and total energy of the data center is obtained by the summation of individual server energy. The tasks submitted are routed to the data center with least energy consumption which will result in minimizing the operational expenses of a service provider.

A sustainability model based on cloud infrastructures for core and downstream Copernicus services

NASA Astrophysics Data System (ADS)

Manunta, Michele; Calò, Fabiana; De Luca, Claudio; Elefante, Stefano; Farres, Jordi; Guzzetti, Fausto; Imperatore, Pasquale; Lanari, Riccardo; Lengert, Wolfgang; Zinno, Ivana; Casu, Francesco

2014-05-01

The incoming Sentinel missions have been designed to be the first remote sensing satellite system devoted to operational services. In particular, the Synthetic Aperture Radar (SAR) Sentinel-1 sensor, dedicated to globally acquire over land in the interferometric mode, guarantees an unprecedented capability to investigate and monitor the Earth surface deformations related to natural and man-made hazards. Thanks to the global coverage strategy and 12-day revisit time, jointly with the free and open access data policy, such a system will allow an extensive application of Differential Interferometric SAR (DInSAR) techniques. In such a framework, European Commission has been funding several projects through the GMES and Copernicus programs, aimed at preparing the user community to the operational and extensive use of Sentinel-1 products for risk mitigation and management purposes. Among them, the FP7-DORIS, an advanced GMES downstream service coordinated by Italian National Council of Research (CNR), is based on the fully exploitation of advanced DInSAR products in landslides and subsidence contexts. In particular, the DORIS project (www.doris-project.eu) has developed innovative scientific techniques and methodologies to support Civil Protection Authorities (CPA) during the pre-event, event, and post-event phases of the risk management cycle. Nonetheless, the huge data stream expected from the Sentinel-1 satellite may jeopardize the effective use of such data in emergency response and security scenarios. This potential bottleneck can be properly overcome through the development of modern infrastructures, able to efficiently provide computing resources as well as advanced services for big data management, processing and dissemination. In this framework, CNR and ESA have tightened up a cooperation to foster the use of GRID and cloud computing platforms for remote sensing data processing, and to make available to a large audience advanced and innovative tools for DInSAR products generation and exploitation. In particular, CNR is porting the multi-temporal DInSAR technique referred to as Small Baseline Subset (SBAS) into the ESA G-POD (Grid Processing On Demand) and CIOP (Cloud Computing Operational Pilot) platforms (Elefante et al., 2013) within the SuperSites Exploitation Platform (SSEP) project, which aim is contributing to the development of an ecosystem for big geo-data processing and dissemination. This work focuses on presenting the main results that have been achieved by the DORIS project concerning the use of advanced DInSAR products for supporting CPA during the risk management cycle. Furthermore, based on the DORIS experience, a sustainability model for Core and Downstream Copernicus services based on the effective exploitation of cloud platforms is proposed. In this framework, remote sensing community, both service providers and users, can significantly benefit from the Helix Nebula-The Science Cloud initiative, created by European scientific institutions, agencies, SMEs and enterprises to pave the way for the development and exploitation of a cloud computing infrastructure for science. REFERENCES Elefante, S., Imperatore, P. , Zinno, I., M. Manunta, E. Mathot, F. Brito, J. Farres, W. Lengert, R. Lanari, F. Casu, 2013, "SBAS-DINSAR Time series generation on cloud computing platforms". IEEE IGARSS Conference, Melbourne (AU), July 2013.

MarDRe: efficient MapReduce-based removal of duplicate DNA reads in the cloud.

PubMed

Expósito, Roberto R; Veiga, Jorge; González-Domínguez, Jorge; Touriño, Juan

2017-09-01

This article presents MarDRe, a de novo cloud-ready duplicate and near-duplicate removal tool that can process single- and paired-end reads from FASTQ/FASTA datasets. MarDRe takes advantage of the widely adopted MapReduce programming model to fully exploit Big Data technologies on cloud-based infrastructures. Written in Java to maximize cross-platform compatibility, MarDRe is built upon the open-source Apache Hadoop project, the most popular distributed computing framework for scalable Big Data processing. On a 16-node cluster deployed on the Amazon EC2 cloud platform, MarDRe is up to 8.52 times faster than a representative state-of-the-art tool. Source code in Java and Hadoop as well as a user's guide are freely available under the GNU GPLv3 license at http://mardre.des.udc.es . rreye@udc.es. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Universal blind quantum computation for hybrid system

NASA Astrophysics Data System (ADS)

Huang, He-Liang; Bao, Wan-Su; Li, Tan; Li, Feng-Guang; Fu, Xiang-Qun; Zhang, Shuo; Zhang, Hai-Long; Wang, Xiang

2017-08-01

As progress on the development of building quantum computer continues to advance, first-generation practical quantum computers will be available for ordinary users in the cloud style similar to IBM's Quantum Experience nowadays. Clients can remotely access the quantum servers using some simple devices. In such a situation, it is of prime importance to keep the security of the client's information. Blind quantum computation protocols enable a client with limited quantum technology to delegate her quantum computation to a quantum server without leaking any privacy. To date, blind quantum computation has been considered only for an individual quantum system. However, practical universal quantum computer is likely to be a hybrid system. Here, we take the first step to construct a framework of blind quantum computation for the hybrid system, which provides a more feasible way for scalable blind quantum computation.

SciServer Compute brings Analysis to Big Data in the Cloud

NASA Astrophysics Data System (ADS)

Raddick, Jordan; Medvedev, Dmitry; Lemson, Gerard; Souter, Barbara

2016-06-01

SciServer Compute uses Jupyter Notebooks running within server-side Docker containers attached to big data collections to bring advanced analysis to big data "in the cloud." SciServer Compute is a component in the SciServer Big-Data ecosystem under development at JHU, which will provide a stable, reproducible, sharable virtual research environment.SciServer builds on the popular CasJobs and SkyServer systems that made the Sloan Digital Sky Survey (SDSS) archive one of the most-used astronomical instruments. SciServer extends those systems with server-side computational capabilities and very large scratch storage space, and further extends their functions to a range of other scientific disciplines.Although big datasets like SDSS have revolutionized astronomy research, for further analysis, users are still restricted to downloading the selected data sets locally - but increasing data sizes make this local approach impractical. Instead, researchers need online tools that are co-located with data in a virtual research environment, enabling them to bring their analysis to the data.SciServer supports this using the popular Jupyter notebooks, which allow users to write their own Python and R scripts and execute them on the server with the data (extensions to Matlab and other languages are planned). We have written special-purpose libraries that enable querying the databases and other persistent datasets. Intermediate results can be stored in large scratch space (hundreds of TBs) and analyzed directly from within Python or R with state-of-the-art visualization and machine learning libraries. Users can store science-ready results in their permanent allocation on SciDrive, a Dropbox-like system for sharing and publishing files. Communication between the various components of the SciServer system is managed through SciServer‘s new Single Sign-on Portal.We have created a number of demos to illustrate the capabilities of SciServer Compute, including Python and R scripts accessing a range of datasets and showing the data flow between storage and compute components.Demos, documentation, and more information can be found at www.sciserver.org.SciServer is funded by the National Science Foundation Award ACI-1261715.

Efficient Nash Equilibrium Resource Allocation Based on Game Theory Mechanism in Cloud Computing by Using Auction

PubMed Central

Nezarat, Amin; Dastghaibifard, GH

2015-01-01

One of the most complex issues in the cloud computing environment is the problem of resource allocation so that, on one hand, the cloud provider expects the most profitability and, on the other hand, users also expect to have the best resources at their disposal considering the budget constraints and time. In most previous work conducted, heuristic and evolutionary approaches have been used to solve this problem. Nevertheless, since the nature of this environment is based on economic methods, using such methods can decrease response time and reducing the complexity of the problem. In this paper, an auction-based method is proposed which determines the auction winner by applying game theory mechanism and holding a repetitive game with incomplete information in a non-cooperative environment. In this method, users calculate suitable price bid with their objective function during several round and repetitions and send it to the auctioneer; and the auctioneer chooses the winning player based the suggested utility function. In the proposed method, the end point of the game is the Nash equilibrium point where players are no longer inclined to alter their bid for that resource and the final bid also satisfies the auctioneer’s utility function. To prove the response space convexity, the Lagrange method is used and the proposed model is simulated in the cloudsim and the results are compared with previous work. At the end, it is concluded that this method converges to a response in a shorter time, provides the lowest service level agreement violations and the most utility to the provider. PMID:26431035

Efficient Nash Equilibrium Resource Allocation Based on Game Theory Mechanism in Cloud Computing by Using Auction.

PubMed

Nezarat, Amin; Dastghaibifard, G H

2015-01-01

One of the most complex issues in the cloud computing environment is the problem of resource allocation so that, on one hand, the cloud provider expects the most profitability and, on the other hand, users also expect to have the best resources at their disposal considering the budget constraints and time. In most previous work conducted, heuristic and evolutionary approaches have been used to solve this problem. Nevertheless, since the nature of this environment is based on economic methods, using such methods can decrease response time and reducing the complexity of the problem. In this paper, an auction-based method is proposed which determines the auction winner by applying game theory mechanism and holding a repetitive game with incomplete information in a non-cooperative environment. In this method, users calculate suitable price bid with their objective function during several round and repetitions and send it to the auctioneer; and the auctioneer chooses the winning player based the suggested utility function. In the proposed method, the end point of the game is the Nash equilibrium point where players are no longer inclined to alter their bid for that resource and the final bid also satisfies the auctioneer's utility function. To prove the response space convexity, the Lagrange method is used and the proposed model is simulated in the cloudsim and the results are compared with previous work. At the end, it is concluded that this method converges to a response in a shorter time, provides the lowest service level agreement violations and the most utility to the provider.

Self-service for software development projects and HPC activities

NASA Astrophysics Data System (ADS)

Husejko, M.; Høimyr, N.; Gonzalez, A.; Koloventzos, G.; Asbury, D.; Trzcinska, A.; Agtzidis, I.; Botrel, G.; Otto, J.

2014-05-01

This contribution describes how CERN has implemented several essential tools for agile software development processes, ranging from version control (Git) to issue tracking (Jira) and documentation (Wikis). Running such services in a large organisation like CERN requires many administrative actions both by users and service providers, such as creating software projects, managing access rights, users and groups, and performing tool-specific customisation. Dealing with these requests manually would be a time-consuming task. Another area of our CERN computing services that has required dedicated manual support has been clusters for specific user communities with special needs. Our aim is to move all our services to a layered approach, with server infrastructure running on the internal cloud computing infrastructure at CERN. This contribution illustrates how we plan to optimise the management of our of services by means of an end-user facing platform acting as a portal into all the related services for software projects, inspired by popular portals for open-source developments such as Sourceforge, GitHub and others. Furthermore, the contribution will discuss recent activities with tests and evaluations of High Performance Computing (HPC) applications on different hardware and software stacks, and plans to offer a dynamically scalable HPC service at CERN, based on affordable hardware.

Load Balancing in Multi Cloud Computing Environment with Genetic Algorithm

NASA Astrophysics Data System (ADS)

Vhansure, Fularani; Deshmukh, Apurva; Sumathy, S.

2017-11-01

Cloud is a pool of resources that is available on pay per use model. It provides services to the user which is increasing rapidly. Load balancing is an issue because it cannot handle so many requests at a time. It is also known as NP complete problem. In traditional system the functions consist of various parameter values to maximise it in order to achieve best optimal individualsolutions. Challenge is when there are many parameters of solutionsin the system space. Another challenge is to optimize the function which is much more complex. In this paper, various techniques to handle load balancing virtually (VM) as well as physically (nodes) using genetic algorithm is discussed.

Observer Interface Analysis for Standardization to a Cloud Based Real-Time Space Situational Awareness (SSA)

NASA Astrophysics Data System (ADS)

Eilers, J.

2013-09-01

The interface analysis from an observer of space objects makes a standard necessary. This standardized dataset serves as input for a cloud based service, which aimed for a near real-time Space Situational Awareness (SSA) system. The system contains all advantages of a cloud based solution, like redundancy, scalability and an easy way to distribute information. For the standard based on the interface analysis of the observer, the information can be separated in three parts. One part is the information about the observer e.g. a ground station. The next part is the information about the sensors that are used by the observer. And the last part is the data from the detected object. Backbone of the SSA System is the cloud based service which includes the consistency check for the observed objects, a database for the objects, the algorithms and analysis as well as the visualization of the results. This paper also provides an approximation of the needed computational power, data storage and a financial approach to deliver this service to a broad community. In this context cloud means, neither the user nor the observer has to think about the infrastructure of the calculation environment. The decision if the IT-infrastructure will be built by a conglomerate of different nations or rented on the marked should be based on an efficiency analysis. Also combinations are possible like starting on a rented cloud and then go to a private cloud owned by the government. One of the advantages of a cloud solution is the scalability. There are about 3000 satellites in space, 900 of them are active, and in total there are about ~17.000 detected space objects orbiting earth. But for the computation it is not a N(active) to N problem it is more N(active) to N(apo peri) quantity of N(all). Instead of 15.3 million possible collisions to calculate a computation of only approx. 2.3 million possible collisions must be done. In general, this Space Situational Awareness System can be used as a tool for satellite system owner for collision avoidance.

Delivering Unidata Technology via the Cloud

NASA Astrophysics Data System (ADS)

Fisher, Ward; Oxelson Ganter, Jennifer

2016-04-01

Over the last two years, Docker has emerged as the clear leader in open-source containerization. Containerization technology provides a means by which software can be pre-configured and packaged into a single unit, i.e. a container. This container can then be easily deployed either on local or remote systems. Containerization is particularly advantageous when moving software into the cloud, as it simplifies the process. Unidata is adopting containerization as part of our commitment to migrate our technologies to the cloud. We are using a two-pronged approach in this endeavor. In addition to migrating our data-portal services to a cloud environment, we are also exploring new and novel ways to use cloud-specific technology to serve our community. This effort has resulted in several new cloud/Docker-specific projects at Unidata: "CloudStream," "CloudIDV," and "CloudControl." CloudStream is a docker-based technology stack for bringing legacy desktop software to new computing environments, without the need to invest significant engineering/development resources. CloudStream helps make it easier to run existing software in a cloud environment via a technology called "Application Streaming." CloudIDV is a CloudStream-based implementation of the Unidata Integrated Data Viewer (IDV). CloudIDV serves as a practical example of application streaming, and demonstrates how traditional software can be easily accessed and controlled via a web browser. Finally, CloudControl is a web-based dashboard which provides administrative controls for running docker-based technologies in the cloud, as well as providing user management. In this work we will give an overview of these three open-source technologies and the value they offer to our community.

QwikMD — Integrative Molecular Dynamics Toolkit for Novices and Experts

PubMed Central

Ribeiro, João V.; Bernardi, Rafael C.; Rudack, Till; Stone, John E.; Phillips, James C.; Freddolino, Peter L.; Schulten, Klaus

2016-01-01

The proper functioning of biomolecules in living cells requires them to assume particular structures and to undergo conformational changes. Both biomolecular structure and motion can be studied using a wide variety of techniques, but none offers the level of detail as do molecular dynamics (MD) simulations. Integrating two widely used modeling programs, namely NAMD and VMD, we have created a robust, user-friendly software, QwikMD, which enables novices and experts alike to address biomedically relevant questions, where often only molecular dynamics simulations can provide answers. Performing both simple and advanced MD simulations interactively, QwikMD automates as many steps as necessary for preparing, carrying out, and analyzing simulations while checking for common errors and enabling reproducibility. QwikMD meets also the needs of experts in the field, increasing the efficiency and quality of their work by carrying out tedious or repetitive tasks while enabling easy control of every step. Whether carrying out simulations within the live view mode on a small laptop or performing complex and large simulations on supercomputers or Cloud computers, QwikMD uses the same steps and user interface. QwikMD is freely available by download on group and personal computers. It is also available on the cloud at Amazon Web Services. PMID:27216779

QwikMD — Integrative Molecular Dynamics Toolkit for Novices and Experts

NASA Astrophysics Data System (ADS)

Ribeiro, João V.; Bernardi, Rafael C.; Rudack, Till; Stone, John E.; Phillips, James C.; Freddolino, Peter L.; Schulten, Klaus

2016-05-01

The proper functioning of biomolecules in living cells requires them to assume particular structures and to undergo conformational changes. Both biomolecular structure and motion can be studied using a wide variety of techniques, but none offers the level of detail as do molecular dynamics (MD) simulations. Integrating two widely used modeling programs, namely NAMD and VMD, we have created a robust, user-friendly software, QwikMD, which enables novices and experts alike to address biomedically relevant questions, where often only molecular dynamics simulations can provide answers. Performing both simple and advanced MD simulations interactively, QwikMD automates as many steps as necessary for preparing, carrying out, and analyzing simulations while checking for common errors and enabling reproducibility. QwikMD meets also the needs of experts in the field, increasing the efficiency and quality of their work by carrying out tedious or repetitive tasks while enabling easy control of every step. Whether carrying out simulations within the live view mode on a small laptop or performing complex and large simulations on supercomputers or Cloud computers, QwikMD uses the same steps and user interface. QwikMD is freely available by download on group and personal computers. It is also available on the cloud at Amazon Web Services.

Cloudgene: A graphical execution platform for MapReduce programs on private and public clouds

PubMed Central

2012-01-01

Background The MapReduce framework enables a scalable processing and analyzing of large datasets by distributing the computational load on connected computer nodes, referred to as a cluster. In Bioinformatics, MapReduce has already been adopted to various case scenarios such as mapping next generation sequencing data to a reference genome, finding SNPs from short read data or matching strings in genotype files. Nevertheless, tasks like installing and maintaining MapReduce on a cluster system, importing data into its distributed file system or executing MapReduce programs require advanced knowledge in computer science and could thus prevent scientists from usage of currently available and useful software solutions. Results Here we present Cloudgene, a freely available platform to improve the usability of MapReduce programs in Bioinformatics by providing a graphical user interface for the execution, the import and export of data and the reproducibility of workflows on in-house (private clouds) and rented clusters (public clouds). The aim of Cloudgene is to build a standardized graphical execution environment for currently available and future MapReduce programs, which can all be integrated by using its plug-in interface. Since Cloudgene can be executed on private clusters, sensitive datasets can be kept in house at all time and data transfer times are therefore minimized. Conclusions Our results show that MapReduce programs can be integrated into Cloudgene with little effort and without adding any computational overhead to existing programs. This platform gives developers the opportunity to focus on the actual implementation task and provides scientists a platform with the aim to hide the complexity of MapReduce. In addition to MapReduce programs, Cloudgene can also be used to launch predefined systems (e.g. Cloud BioLinux, RStudio) in public clouds. Currently, five different bioinformatic programs using MapReduce and two systems are integrated and have been successfully deployed. Cloudgene is freely available at http://cloudgene.uibk.ac.at. PMID:22888776

Phenotype Instance Verification and Evaluation Tool (PIVET): A Scaled Phenotype Evidence Generation Framework Using Web-Based Medical Literature.

PubMed

Henderson, Jette; Ke, Junyuan; Ho, Joyce C; Ghosh, Joydeep; Wallace, Byron C

2018-05-04

Researchers are developing methods to automatically extract clinically relevant and useful patient characteristics from raw healthcare datasets. These characteristics, often capturing essential properties of patients with common medical conditions, are called computational phenotypes. Being generated by automated or semiautomated, data-driven methods, such potential phenotypes need to be validated as clinically meaningful (or not) before they are acceptable for use in decision making. The objective of this study was to present Phenotype Instance Verification and Evaluation Tool (PIVET), a framework that uses co-occurrence analysis on an online corpus of publically available medical journal articles to build clinical relevance evidence sets for user-supplied phenotypes. PIVET adopts a conceptual framework similar to the pioneering prototype tool PheKnow-Cloud that was developed for the phenotype validation task. PIVET completely refactors each part of the PheKnow-Cloud pipeline to deliver vast improvements in speed without sacrificing the quality of the insights PheKnow-Cloud achieved. PIVET leverages indexing in NoSQL databases to efficiently generate evidence sets. Specifically, PIVET uses a succinct representation of the phenotypes that corresponds to the index on the corpus database and an optimized co-occurrence algorithm inspired by the Aho-Corasick algorithm. We compare PIVET's phenotype representation with PheKnow-Cloud's by using PheKnow-Cloud's experimental setup. In PIVET's framework, we also introduce a statistical model trained on domain expert-verified phenotypes to automatically classify phenotypes as clinically relevant or not. Additionally, we show how the classification model can be used to examine user-supplied phenotypes in an online, rather than batch, manner. PIVET maintains the discriminative power of PheKnow-Cloud in terms of identifying clinically relevant phenotypes for the same corpus with which PheKnow-Cloud was originally developed, but PIVET's analysis is an order of magnitude faster than that of PheKnow-Cloud. Not only is PIVET much faster, it can be scaled to a larger corpus and still retain speed. We evaluated multiple classification models on top of the PIVET framework and found ridge regression to perform best, realizing an average F1 score of 0.91 when predicting clinically relevant phenotypes. Our study shows that PIVET improves on the most notable existing computational tool for phenotype validation in terms of speed and automation and is comparable in terms of accuracy. ©Jette Henderson, Junyuan Ke, Joyce C Ho, Joydeep Ghosh, Byron C Wallace. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 04.05.2018.

Building a cloud based distributed active archive data center

NASA Astrophysics Data System (ADS)

Ramachandran, Rahul; Baynes, Katie; Murphy, Kevin

2017-04-01

NASA's Earth Science Data System (ESDS) Program serves as a central cog in facilitating the implementation of NASA's Earth Science strategic plan. Since 1994, the ESDS Program has committed to the full and open sharing of Earth science data obtained from NASA instruments to all users. One of the key responsibilities of the ESDS Program is to continuously evolve the entire data and information system to maximize returns on the collected NASA data. An independent review was conducted in 2015 to holistically review the EOSDIS in order to identify gaps. The review recommendations were to investigate two areas: one, whether commercial cloud providers offer potential for storage, processing, and operational efficiencies, and two, the potential development of new data access and analysis paradigms. In response, ESDS has initiated several prototypes investigating the advantages and risks of leveraging cloud computing. This poster will provide an overview of one such prototyping activity, "Cumulus". Cumulus is being designed and developed as a "native" cloud-based data ingest, archive and management system that can be used for all future NASA Earth science data streams. The long term vision for Cumulus, its requirements, overall architecture, and implementation details, as well as lessons learned from the completion of the first phase of this prototype will be covered. We envision Cumulus will foster design of new analysis/visualization tools to leverage collocated data from all of the distributed DAACs as well as elastic cloud computing resources to open new research opportunities.

The EPOS ICT Architecture

NASA Astrophysics Data System (ADS)

Jeffery, Keith; Harrison, Matt; Bailo, Daniele

2016-04-01

The EPOS-PP Project 2010-2014 proposed an architecture and demonstrated feasibility with a prototype. Requirements based on use cases were collected and an inventory of assets (e.g. datasets, software, users, computing resources, equipment/detectors, laboratory services) (RIDE) was developed. The architecture evolved through three stages of refinement with much consultation both with the EPOS community representing EPOS users and participants in geoscience and with the overall ICT community especially those working on research such as the RDA (Research Data Alliance) community. The architecture consists of a central ICS (Integrated Core Services) consisting of a portal and catalog, the latter providing to end-users a 'map' of all EPOS resources (datasets, software, users, computing, equipment/detectors etc.). ICS is extended to ICS-d (distributed ICS) for certain services (such as visualisation software services or Cloud computing resources) and CES (Computational Earth Science) for specific simulation or analytical processing. ICS also communicates with TCS (Thematic Core Services) which represent European-wide portals to national and local assets, resources and services in the various specific domains (e.g. seismology, volcanology, geodesy) of EPOS. The EPOS-IP project 2015-2019 started October 2015. Two work-packages cover the ICT aspects; WP6 involves interaction with the TCS while WP7 concentrates on ICS including interoperation with ICS-d and CES offerings: in short the ICT architecture. Based on the experience and results of EPOS-PP the ICT team held a pre-meeting in July 2015 and set out a project plan. The first major activity involved requirements (re-)collection with use cases and also updating the inventory of assets held by the various TCS in EPOS. The RIDE database of assets is currently being converted to CERIF (Common European Research Information Format - an EU Recommendation to Member States) to provide the basis for the EPOS-IP ICS Catalog. In parallel the ICT team is tracking developments in ICT for relevance to EPOS-IP. In particular, the potential utilisation of e-Is (e-Infrastructures) such as GEANT(network), AARC (security), EGI (GRID computing), EUDAT (data curation), PRACE (High Performance Computing), HELIX-Nebula / Open Science Cloud (Cloud computing) are being assessed. Similarly relationships to other e-RIs (e-Research Infrastructures) such as ENVRI+, EXCELERATE and other ESFRI (European Strategic Forum for Research Infrastructures) projects are developed to share experience and technology and to promote interoperability. EPOS ICT team members are also involved in VRE4EIC, a project developing a reference architecture and component software services for a Virtual Research Environment to be superimposed on EPOS-ICS. The challenge which is being tackled now is therefore to keep consistency and interoperability among the different modules, initiatives and actors which participate to the process of running the EPOS platform. It implies both a continuous update about IT aspects of mentioned initiatives and a refinement of the e-architecture designed so far. One major aspect of EPOS-IP is the ICT support for legalistic, financial and governance aspects of the EPOS ERIC to be initiated during EPOS-IP. This implies a sophisticated AAAI (Authentication, authorization, accounting infrastructure) with consistency throughout the software, communications and data stack.

Cloud Computing for DoD

DTIC Science & Technology

2012-05-01

cloud computing 17 NASA Nebula Platform •  Cloud computing pilot program at NASA Ames •  Integrates open-source components into seamless, self...Mission support •  Education and public outreach (NASA Nebula , 2010) 18 NSF Supported Cloud Research •  Support for Cloud Computing in...Mell, P. & Grance, T. (2011). The NIST Definition of Cloud Computing. NIST Special Publication 800-145 •  NASA Nebula (2010). Retrieved from

Optimally analyzing and implementing of bolt fittings in steel structure based on ANSYS

NASA Astrophysics Data System (ADS)

Han, Na; Song, Shuangyang; Cui, Yan; Wu, Yongchun

2018-03-01

ANSYS simulation software for its excellent performance become outstanding one in Computer-aided Engineering (CAE) family, it is committed to the innovation of engineering simulation to help users to shorten the design process. First, a typical procedure to implement CAE was design. The framework of structural numerical analysis on ANSYS Technology was proposed. Then, A optimally analyzing and implementing of bolt fittings in beam-column join of steel structure was implemented by ANSYS, which was display the cloud chart of XY-shear stress, the cloud chart of YZ-shear stress and the cloud chart of Y component of stress. Finally, ANSYS software simulating results was compared with the measured results by the experiment. The result of ANSYS simulating and analyzing is reliable, efficient and optical. In above process, a structural performance's numerical simulating and analyzing model were explored for engineering enterprises' practice.

A Hybrid Cloud Computing Service for Earth Sciences

NASA Astrophysics Data System (ADS)

Yang, C. P.

2016-12-01

Cloud Computing is becoming a norm for providing computing capabilities for advancing Earth sciences including big Earth data management, processing, analytics, model simulations, and many other aspects. A hybrid spatiotemporal cloud computing service is bulit at George Mason NSF spatiotemporal innovation center to meet this demands. This paper will report the service including several aspects: 1) the hardware includes 500 computing services and close to 2PB storage as well as connection to XSEDE Jetstream and Caltech experimental cloud computing environment for sharing the resource; 2) the cloud service is geographically distributed at east coast, west coast, and central region; 3) the cloud includes private clouds managed using open stack and eucalyptus, DC2 is used to bridge these and the public AWS cloud for interoperability and sharing computing resources when high demands surfing; 4) the cloud service is used to support NSF EarthCube program through the ECITE project, ESIP through the ESIP cloud computing cluster, semantics testbed cluster, and other clusters; 5) the cloud service is also available for the earth science communities to conduct geoscience. A brief introduction about how to use the cloud service will be included.

A cloud-based system for measuring radiation treatment plan similarity

NASA Astrophysics Data System (ADS)

Andrea, Jennifer

PURPOSE: Radiation therapy is used to treat cancer using carefully designed plans that maximize the radiation dose delivered to the target and minimize damage to healthy tissue, with the dose administered over multiple occasions. Creating treatment plans is a laborious process and presents an obstacle to more frequent replanning, which remains an unsolved problem. However, in between new plans being created, the patient's anatomy can change due to multiple factors including reduction in tumor size and loss of weight, which results in poorer patient outcomes. Cloud computing is a newer technology that is slowly being used for medical applications with promising results. The objective of this work was to design and build a system that could analyze a database of previously created treatment plans, which are stored with their associated anatomical information in studies, to find the one with the most similar anatomy to a new patient. The analyses would be performed in parallel on the cloud to decrease the computation time of finding this plan. METHODS: The system used SlicerRT, a radiation therapy toolkit for the open-source platform 3D Slicer, for its tools to perform the similarity analysis algorithm. Amazon Web Services was used for the cloud instances on which the analyses were performed, as well as for storage of the radiation therapy studies and messaging between the instances and a master local computer. A module was built in SlicerRT to provide the user with an interface to direct the system on the cloud, as well as to perform other related tasks. RESULTS: The cloud-based system out-performed previous methods of conducting the similarity analyses in terms of time, as it analyzed 100 studies in approximately 13 minutes, and produced the same similarity values as those methods. It also scaled up to larger numbers of studies to analyze in the database with a small increase in computation time of just over 2 minutes. CONCLUSION: This system successfully analyzes a large database of radiation therapy studies and finds the one that is most similar to a new patient, which represents a potential step forward in achieving feasible adaptive radiation therapy replanning.

CLOUDCLOUD : general-purpose instrument monitoring and data managing software

NASA Astrophysics Data System (ADS)

Dias, António; Amorim, António; Tomé, António

2016-04-01

An effective experiment is dependent on the ability to store and deliver data and information to all participant parties regardless of their degree of involvement in the specific parts that make the experiment a whole. Having fast, efficient and ubiquitous access to data will increase visibility and discussion, such that the outcome will have already been reviewed several times, strengthening the conclusions. The CLOUD project aims at providing users with a general purpose data acquisition, management and instrument monitoring platform that is fast, easy to use, lightweight and accessible to all participants of an experiment. This work is now implemented in the CLOUD experiment at CERN and will be fully integrated with the experiment as of 2016. Despite being used in an experiment of the scale of CLOUD, this software can also be used in any size of experiment or monitoring station, from single computers to large networks of computers to monitor any sort of instrument output without influencing the individual instrument's DAQ. Instrument data and meta data is stored and accessed via a specially designed database architecture and any type of instrument output is accepted using our continuously growing parsing application. Multiple databases can be used to separate different data taking periods or a single database can be used if for instance an experiment is continuous. A simple web-based application gives the user total control over the monitored instruments and their data, allowing data visualization and download, upload of processed data and the ability to edit existing instruments or add new instruments to the experiment. When in a network, new computers are immediately recognized and added to the system and are able to monitor instruments connected to them. Automatic computer integration is achieved by a locally running python-based parsing agent that communicates with a main server application guaranteeing that all instruments assigned to that computer are monitored with parsing intervals as fast as milliseconds. This software (server+agents+interface+database) comes in easy and ready-to-use packages that can be installed in any operating system, including Android and iOS systems. This software is ideal for use in modular experiments or monitoring stations with large variability in instruments and measuring methods or in large collaborations, where data requires homogenization in order to be effectively transmitted to all involved parties. This work presents the software and provides performance comparison with previously used monitoring systems in the CLOUD experiment at CERN.

Do Clouds Compute? A Framework for Estimating the Value of Cloud Computing

NASA Astrophysics Data System (ADS)

Klems, Markus; Nimis, Jens; Tai, Stefan

On-demand provisioning of scalable and reliable compute services, along with a cost model that charges consumers based on actual service usage, has been an objective in distributed computing research and industry for a while. Cloud Computing promises to deliver on this objective: consumers are able to rent infrastructure in the Cloud as needed, deploy applications and store data, and access them via Web protocols on a pay-per-use basis. The acceptance of Cloud Computing, however, depends on the ability for Cloud Computing providers and consumers to implement a model for business value co-creation. Therefore, a systematic approach to measure costs and benefits of Cloud Computing is needed. In this paper, we discuss the need for valuation of Cloud Computing, identify key components, and structure these components in a framework. The framework assists decision makers in estimating Cloud Computing costs and to compare these costs to conventional IT solutions. We demonstrate by means of representative use cases how our framework can be applied to real world scenarios.

Waggle: A Framework for Intelligent Attentive Sensing and Actuation

NASA Astrophysics Data System (ADS)

Sankaran, R.; Jacob, R. L.; Beckman, P. H.; Catlett, C. E.; Keahey, K.

2014-12-01

Advances in sensor-driven computation and computationally steered sensing will greatly enable future research in fields including environmental and atmospheric sciences. We will present "Waggle," an open-source hardware and software infrastructure developed with two goals: (1) reducing the separation and latency between sensing and computing and (2) improving the reliability and longevity of sensing-actuation platforms in challenging and costly deployments. Inspired by "deep-space probe" systems, the Waggle platform design includes features that can support longitudinal studies, deployments with varying communication links, and remote management capabilities. Waggle lowers the barrier for scientists to incorporate real-time data from their sensors into their computations and to manipulate the sensors or provide feedback through actuators. A standardized software and hardware design allows quick addition of new sensors/actuators and associated software in the nodes and enables them to be coupled with computational codes both insitu and on external compute infrastructure. The Waggle framework currently drives the deployment of two observational systems - a portable and self-sufficient weather platform for study of small-scale effects in Chicago's urban core and an open-ended distributed instrument in Chicago that aims to support several research pursuits across a broad range of disciplines including urban planning, microbiology and computer science. Built around open-source software, hardware, and Linux OS, the Waggle system comprises two components - the Waggle field-node and Waggle cloud-computing infrastructure. Waggle field-node affords a modular, scalable, fault-tolerant, secure, and extensible platform for hosting sensors and actuators in the field. It supports insitu computation and data storage, and integration with cloud-computing infrastructure. The Waggle cloud infrastructure is designed with the goal of scaling to several hundreds of thousands of Waggle nodes. It supports aggregating data from sensors hosted by the nodes, staging computation, relaying feedback to the nodes and serving data to end-users. We will discuss the Waggle design principles and their applicability to various observational research pursuits, and demonstrate its capabilities.

Analysis of Sea Level Rise in Action

NASA Astrophysics Data System (ADS)

Gill, K. M.; Huang, T.; Quach, N. T.; Boening, C.

2016-12-01

NASA's Sea Level Change Portal provides scientists and the general public with "one-stop" source for current sea level change information and data. Sea Level Rise research is a multidisciplinary research and in order to understand its causes, scientists must be able to access different measurements and to be able to compare them. The portal includes an interactive tool, called the Data Analysis Tool (DAT), for accessing, visualizing, and analyzing observations and models relevant to the study of Sea Level Rise. Using NEXUS, an open source, big data analytic technology developed at the Jet Propulsion Laboratory, the DAT is able provide user on-the-fly data analysis on all relevant parameters. DAT is composed of three major components: A dedicated instance of OnEarth (a WMTS service), NEXUS deep data analytic platform, and the JPL Common Mapping Client (CMC) for web browser based user interface (UI). Utilizing the global imagery, a user is capable of browsing the data in a visual manner and isolate areas of interest for further study. The interfaces "Analysis" tool provides tools for area or point selection, single and/or comparative dataset selection, and a range of options, algorithms, and plotting. This analysis component utilizes the Nexus cloud computing platform to provide on-demand processing of the data within the user-selected parameters and immediate display of the results. A RESTful web API is exposed for users comfortable with other interfaces and who may want to take advantage of the cloud computing capabilities. This talk discuss how DAT enables on-the-fly sea level research. The talk will introduce the DAT with an end-to-end tour of the tool with exploration and animating of available imagery, a demonstration of comparative analysis and plotting, and how to share and export data along with images for use in publications/presentations. The session will cover what kind of data is available, what kind of analysis is possible, and what are the outputs.