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.

Cloud Computing: Short Term Impacts of 1:1 Computing in the Sixth Grade

ERIC Educational Resources Information Center

Bebell, Damian; Clarkson, Apryl; Burraston, James

2014-01-01

Many parents, educators, and policy makers see great potential for leveraging tools like laptop computers, tablets, and smartphones in the classrooms of the world. Under budget constraints and shared access to equipment for students and teachers, the impacts have been irregular but hint at greater possibilities in 1:1 student computing settings.…

Genomic cloud computing: legal and ethical points to consider

PubMed Central

Dove, Edward S; Joly, Yann; Tassé, Anne-Marie; Burton, Paul; Chisholm, Rex; Fortier, Isabel; Goodwin, Pat; Harris, Jennifer; Hveem, Kristian; Kaye, Jane; Kent, Alistair; Knoppers, Bartha Maria; Lindpaintner, Klaus; Little, Julian; Riegman, Peter; Ripatti, Samuli; Stolk, Ronald; Bobrow, Martin; Cambon-Thomsen, Anne; Dressler, Lynn; Joly, Yann; Kato, Kazuto; Knoppers, Bartha Maria; Rodriguez, Laura Lyman; McPherson, Treasa; Nicolás, Pilar; Ouellette, Francis; Romeo-Casabona, Carlos; Sarin, Rajiv; Wallace, Susan; Wiesner, Georgia; Wilson, Julia; Zeps, Nikolajs; Simkevitz, Howard; De Rienzo, Assunta; Knoppers, Bartha M

2015-01-01

The biggest challenge in twenty-first century data-intensive genomic science, is developing vast computer infrastructure and advanced software tools to perform comprehensive analyses of genomic data sets for biomedical research and clinical practice. Researchers are increasingly turning to cloud computing both as a solution to integrate data from genomics, systems biology and biomedical data mining and as an approach to analyze data to solve biomedical problems. Although cloud computing provides several benefits such as lower costs and greater efficiency, it also raises legal and ethical issues. In this article, we discuss three key ‘points to consider' (data control; data security, confidentiality and transfer; and accountability) based on a preliminary review of several publicly available cloud service providers' Terms of Service. These ‘points to consider' should be borne in mind by genomic research organizations when negotiating legal arrangements to store genomic data on a large commercial cloud service provider's servers. Diligent genomic cloud computing means leveraging security standards and evaluation processes as a means to protect data and entails many of the same good practices that researchers should always consider in securing their local infrastructure. PMID:25248396

Genomic cloud computing: legal and ethical points to consider.

PubMed

Dove, Edward S; Joly, Yann; Tassé, Anne-Marie; Knoppers, Bartha M

2015-10-01

The biggest challenge in twenty-first century data-intensive genomic science, is developing vast computer infrastructure and advanced software tools to perform comprehensive analyses of genomic data sets for biomedical research and clinical practice. Researchers are increasingly turning to cloud computing both as a solution to integrate data from genomics, systems biology and biomedical data mining and as an approach to analyze data to solve biomedical problems. Although cloud computing provides several benefits such as lower costs and greater efficiency, it also raises legal and ethical issues. In this article, we discuss three key 'points to consider' (data control; data security, confidentiality and transfer; and accountability) based on a preliminary review of several publicly available cloud service providers' Terms of Service. These 'points to consider' should be borne in mind by genomic research organizations when negotiating legal arrangements to store genomic data on a large commercial cloud service provider's servers. Diligent genomic cloud computing means leveraging security standards and evaluation processes as a means to protect data and entails many of the same good practices that researchers should always consider in securing their local infrastructure.

Marine Corps Private Cloud Computing Environment Strategy

DTIC Science & Technology

2012-05-15

leveraging economies of scale through the MCEITS PCCE, the Marine Corps will measure consumed IT resources more effectively, increase or decrease...flexible broad network access, resource pooling, elastic provisioning and measured services. By leveraging economies of scale the Marine Corps will be able...IaaS SaaS / IaaS 1 1 LCE I ACE Dets I I I I ------------------~ GIG / CJ Internet Security Boundary MCEN I DISN r :------------------ MCEN

TethysCluster: A comprehensive approach for harnessing cloud resources for hydrologic modeling

NASA Astrophysics Data System (ADS)

Nelson, J.; Jones, N.; Ames, D. P.

2015-12-01

Advances in water resources modeling are improving the information that can be supplied to support decisions affecting the safety and sustainability of society. However, as water resources models become more sophisticated and data-intensive they require more computational power to run. Purchasing and maintaining the computing facilities needed to support certain modeling tasks has been cost-prohibitive for many organizations. With the advent of the cloud, the computing resources needed to address this challenge are now available and cost-effective, yet there still remains a significant technical barrier to leverage these resources. This barrier inhibits many decision makers and even trained engineers from taking advantage of the best science and tools available. Here we present the Python tools TethysCluster and CondorPy, that have been developed to lower the barrier to model computation in the cloud by providing (1) programmatic access to dynamically scalable computing resources, (2) a batch scheduling system to queue and dispatch the jobs to the computing resources, (3) data management for job inputs and outputs, and (4) the ability to dynamically create, submit, and monitor computing jobs. These Python tools leverage the open source, computing-resource management, and job management software, HTCondor, to offer a flexible and scalable distributed-computing environment. While TethysCluster and CondorPy can be used independently to provision computing resources and perform large modeling tasks, they have also been integrated into Tethys Platform, a development platform for water resources web apps, to enable computing support for modeling workflows and decision-support systems deployed as web apps.

Motion/imagery secure cloud enterprise architecture analysis

NASA Astrophysics Data System (ADS)

DeLay, John L.

2012-06-01

Cloud computing with storage virtualization and new service-oriented architectures brings a new perspective to the aspect of a distributed motion imagery and persistent surveillance enterprise. Our existing research is focused mainly on content management, distributed analytics, WAN distributed cloud networking performance issues of cloud based technologies. The potential of leveraging cloud based technologies for hosting motion imagery, imagery and analytics workflows for DOD and security applications is relatively unexplored. This paper will examine technologies for managing, storing, processing and disseminating motion imagery and imagery within a distributed network environment. Finally, we propose areas for future research in the area of distributed cloud content management enterprises.

A High Performance Cloud-Based Protein-Ligand Docking Prediction Algorithm

PubMed Central

Chen, Jui-Le; Yang, Chu-Sing

2013-01-01

The potential of predicting druggability for a particular disease by integrating biological and computer science technologies has witnessed success in recent years. Although the computer science technologies can be used to reduce the costs of the pharmaceutical research, the computation time of the structure-based protein-ligand docking prediction is still unsatisfied until now. Hence, in this paper, a novel docking prediction algorithm, named fast cloud-based protein-ligand docking prediction algorithm (FCPLDPA), is presented to accelerate the docking prediction algorithm. The proposed algorithm works by leveraging two high-performance operators: (1) the novel migration (information exchange) operator is designed specially for cloud-based environments to reduce the computation time; (2) the efficient operator is aimed at filtering out the worst search directions. Our simulation results illustrate that the proposed method outperforms the other docking algorithms compared in this paper in terms of both the computation time and the quality of the end result. PMID:23762864

Does Cloud Computing in the Atmospheric Sciences Make Sense? A case study of hybrid cloud computing at NASA Langley Research Center

NASA Astrophysics Data System (ADS)

Nguyen, L.; Chee, T.; Minnis, P.; Spangenberg, D.; Ayers, J. K.; Palikonda, R.; Vakhnin, A.; Dubois, R.; Murphy, P. R.

2014-12-01

The processing, storage and dissemination of satellite cloud and radiation products produced at NASA Langley Research Center are key activities for the Climate Science Branch. A constellation of systems operates in sync to accomplish these goals. Because of the complexity involved with operating such intricate systems, there are both high failure rates and high costs for hardware and system maintenance. Cloud computing has the potential to ameliorate cost and complexity issues. Over time, the cloud computing model has evolved and hybrid systems comprising off-site as well as on-site resources are now common. Towards our mission of providing the highest quality research products to the widest audience, we have explored the use of the Amazon Web Services (AWS) Cloud and Storage and present a case study of our results and efforts. This project builds upon NASA Langley Cloud and Radiation Group's experience with operating large and complex computing infrastructures in a reliable and cost effective manner to explore novel ways to leverage cloud computing resources in the atmospheric science environment. Our case study presents the project requirements and then examines the fit of AWS with the LaRC computing model. We also discuss the evaluation metrics, feasibility, and outcomes and close the case study with the lessons we learned that would apply to others interested in exploring the implementation of the AWS system in their own atmospheric science computing environments.

Scalable and responsive event processing in the cloud

PubMed Central

Suresh, Visalakshmi; Ezhilchelvan, Paul; Watson, Paul

2013-01-01

Event processing involves continuous evaluation of queries over streams of events. Response-time optimization is traditionally done over a fixed set of nodes and/or by using metrics measured at query-operator levels. Cloud computing makes it easy to acquire and release computing nodes as required. Leveraging this flexibility, we propose a novel, queueing-theory-based approach for meeting specified response-time targets against fluctuating event arrival rates by drawing only the necessary amount of computing resources from a cloud platform. In the proposed approach, the entire processing engine of a distinct query is modelled as an atomic unit for predicting response times. Several such units hosted on a single node are modelled as a multiple class M/G/1 system. These aspects eliminate intrusive, low-level performance measurements at run-time, and also offer portability and scalability. Using model-based predictions, cloud resources are efficiently used to meet response-time targets. The efficacy of the approach is demonstrated through cloud-based experiments. PMID:23230164

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.

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.

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.

Universities and Libraries Move to the Mobile Web

ERIC Educational Resources Information Center

Aldrich, Alan W.

2010-01-01

The convergence of web-enabled smartphones, the applications designed for smartphone interfaces, and cloud computing is rapidly changing how people interact with each other and with their environments. The commercial sector has taken the lead in creating mobile websites that leverage the capacities of smartphones, and the academic community has…

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.

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

Science in the cloud (SIC): A use case in MRI connectomics.

PubMed

Kiar, Gregory; Gorgolewski, Krzysztof J; Kleissas, Dean; Roncal, William Gray; Litt, Brian; Wandell, Brian; Poldrack, Russel A; Wiener, Martin; Vogelstein, R Jacob; Burns, Randal; Vogelstein, Joshua T

2017-05-01

Modern technologies are enabling scientists to collect extraordinary amounts of complex and sophisticated data across a huge range of scales like never before. With this onslaught of data, we can allow the focal point to shift from data collection to data analysis. Unfortunately, lack of standardized sharing mechanisms and practices often make reproducing or extending scientific results very difficult. With the creation of data organization structures and tools that drastically improve code portability, we now have the opportunity to design such a framework for communicating extensible scientific discoveries. Our proposed solution leverages these existing technologies and standards, and provides an accessible and extensible model for reproducible research, called 'science in the cloud' (SIC). Exploiting scientific containers, cloud computing, and cloud data services, we show the capability to compute in the cloud and run a web service that enables intimate interaction with the tools and data presented. We hope this model will inspire the community to produce reproducible and, importantly, extensible results that will enable us to collectively accelerate the rate at which scientific breakthroughs are discovered, replicated, and extended. © The Author 2017. Published by Oxford University Press.

Cloudbursting - Solving the 3-body problem

NASA Astrophysics Data System (ADS)

Chang, G.; Heistand, S.; Vakhnin, A.; Huang, T.; Zimdars, P.; Hua, H.; Hood, R.; Koenig, J.; Mehrotra, P.; Little, M. M.; Law, E.

2014-12-01

Many science projects in the future will be accomplished through collaboration among 2 or more NASA centers along with, potentially, external scientists. Science teams will be composed of more geographically dispersed individuals and groups. However, the current computing environment does not make this easy and seamless. By being able to share computing resources among members of a multi-center team working on a science/ engineering project, limited pre-competition funds could be more efficiently applied and technical work could be conducted more effectively with less time spent moving data or waiting for computing resources to free up. Based on the work from an NASA CIO IT Labs task, this presentation will highlight our prototype work in identifying the feasibility and identify the obstacles, both technical and management, to perform "Cloudbursting" among private clouds located at three different centers. We will demonstrate the use of private cloud computing infrastructure at the Jet Propulsion Laboratory, Langley Research Center, and Ames Research Center to provide elastic computation to each other to perform parallel Earth Science data imaging. We leverage elastic load balancing and auto-scaling features at each data center so that each location can independently define how many resources to allocate to a particular job that was "bursted" from another data center and demonstrate that compute capacity scales up and down with the job. We will also discuss future work in the area, which could include the use of cloud infrastructure from different cloud framework providers as well as other cloud service providers.

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.

Towards a Service-Oriented Enterprise: The Design of a Cloud Business Integration Platform in a Medium-Sized Manufacturing Enterprise

ERIC Educational Resources Information Center

Stamas, Paul J.

2013-01-01

This case study research followed the two-year transition of a medium-sized manufacturing firm towards a service-oriented enterprise. A service-oriented enterprise is an emerging architecture of the firm that leverages the paradigm of services computing to integrate the capabilities of the firm with the complementary competencies of business…

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.

Virtualized Multi-Mission Operations Center (vMMOC) and its Cloud Services

NASA Technical Reports Server (NTRS)

Ido, Haisam Kassim

2017-01-01

His presentation will cover, the current and future, technical and organizational opportunities and challenges with virtualizing a multi-mission operations center. The full deployment of Goddard Space Flight Centers (GSFC) Virtualized Multi-Mission Operations Center (vMMOC) is nearly complete. The Space Science Mission Operations (SSMO) organizations spacecraft ACE, Fermi, LRO, MMS(4), OSIRIS-REx, SDO, SOHO, Swift, and Wind are in the process of being fully migrated to the vMMOC. The benefits of the vMMOC will be the normalization and the standardization of IT services, mission operations, maintenance, and development as well as ancillary services and policies such as collaboration tools, change management systems, and IT Security. The vMMOC will also provide operational efficiencies regarding hardware, IT domain expertise, training, maintenance and support.The presentation will also cover SSMO's secure Situational Awareness Dashboard in an integrated, fleet centric, cloud based web services fashion. Additionally the SSMO Telemetry as a Service (TaaS) will be covered, which allows authorized users and processes to access telemetry for the entire SSMO fleet, and for the entirety of each spacecrafts history. Both services leverage cloud services in a secure FISMA High and FedRamp environment, and also leverage distributed object stores in order to house and provide the telemetry. The services are also in the process of leveraging the cloud computing services elasticity and horizontal scalability. In the design phase is the Navigation as a Service (NaaS) which will provide a standardized, efficient, and normalized service for the fleet's space flight dynamics operations. Additional future services that may be considered are Ground Segment as a Service (GSaaS), Telemetry and Command as a Service (TCaaS), Flight Software Simulation as a Service, etc.

From Analysis to Impact: Challenges and Outcomes from Google's Cloud-based Platforms for Analyzing and Leveraging Petapixels of Geospatial Data

NASA Astrophysics Data System (ADS)

Thau, D.

2017-12-01

For the past seven years, Google has made petabytes of Earth observation data, and the tools to analyze it, freely available to researchers around the world via cloud computing. These data and tools were initially available via Google Earth Engine and are increasingly available on the Google Cloud Platform. We have introduced a number of APIs for both the analysis and presentation of geospatial data that have been successfully used to create impactful datasets and web applications, including studies of global surface water availability, global tree cover change, and crop yield estimation. Each of these projects used the cloud to analyze thousands to millions of Landsat scenes. The APIs support a range of publishing options, from outputting imagery and data for inclusion in papers, to providing tools for full scale web applications that provide analysis tools of their own. Over the course of developing these tools, we have learned a number of lessons about how to build a publicly available cloud platform for geospatial analysis, and about how the characteristics of an API can affect the kinds of impacts a platform can enable. This study will present an overview of how Google Earth Engine works and how Google's geospatial capabilities are extending to Google Cloud Platform. We will provide a number of case studies describing how these platforms, and the data they host, have been leveraged to build impactful decision support tools used by governments, researchers, and other institutions, and we will describe how the available APIs have shaped (or constrained) those tools. [Image Credit: Tyler A. Erickson

PRESAGE: PRivacy-preserving gEnetic testing via SoftwAre Guard Extension.

PubMed

Chen, Feng; Wang, Chenghong; Dai, Wenrui; Jiang, Xiaoqian; Mohammed, Noman; Al Aziz, Md Momin; Sadat, Md Nazmus; Sahinalp, Cenk; Lauter, Kristin; Wang, Shuang

2017-07-26

Advances in DNA sequencing technologies have prompted a wide range of genomic applications to improve healthcare and facilitate biomedical research. However, privacy and security concerns have emerged as a challenge for utilizing cloud computing to handle sensitive genomic data. We present one of the first implementations of Software Guard Extension (SGX) based securely outsourced genetic testing framework, which leverages multiple cryptographic protocols and minimal perfect hash scheme to enable efficient and secure data storage and computation outsourcing. We compared the performance of the proposed PRESAGE framework with the state-of-the-art homomorphic encryption scheme, as well as the plaintext implementation. The experimental results demonstrated significant performance over the homomorphic encryption methods and a small computational overhead in comparison to plaintext implementation. The proposed PRESAGE provides an alternative solution for secure and efficient genomic data outsourcing in an untrusted cloud by using a hybrid framework that combines secure hardware and multiple crypto protocols.

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.

Biomedical Informatics on the Cloud: A Treasure Hunt for Advancing Cardiovascular Medicine.

PubMed

Ping, Peipei; Hermjakob, Henning; Polson, Jennifer S; Benos, Panagiotis V; Wang, Wei

2018-04-27

In the digital age of cardiovascular medicine, the rate of biomedical discovery can be greatly accelerated by the guidance and resources required to unearth potential collections of knowledge. A unified computational platform leverages metadata to not only provide direction but also empower researchers to mine a wealth of biomedical information and forge novel mechanistic insights. This review takes the opportunity to present an overview of the cloud-based computational environment, including the functional roles of metadata, the architecture schema of indexing and search, and the practical scenarios of machine learning-supported molecular signature extraction. By introducing several established resources and state-of-the-art workflows, we share with our readers a broadly defined informatics framework to phenotype cardiovascular health and disease. © 2018 American Heart Association, Inc.

Science in the cloud (SIC): A use case in MRI connectomics

PubMed Central

Gorgolewski, Krzysztof J.; Kleissas, Dean; Roncal, William Gray; Litt, Brian; Wandell, Brian; Poldrack, Russel A.; Wiener, Martin; Vogelstein, R. Jacob; Burns, Randal

2017-01-01

Abstract Modern technologies are enabling scientists to collect extraordinary amounts of complex and sophisticated data across a huge range of scales like never before. With this onslaught of data, we can allow the focal point to shift from data collection to data analysis. Unfortunately, lack of standardized sharing mechanisms and practices often make reproducing or extending scientific results very difficult. With the creation of data organization structures and tools that drastically improve code portability, we now have the opportunity to design such a framework for communicating extensible scientific discoveries. Our proposed solution leverages these existing technologies and standards, and provides an accessible and extensible model for reproducible research, called ‘science in the cloud’ (SIC). Exploiting scientific containers, cloud computing, and cloud data services, we show the capability to compute in the cloud and run a web service that enables intimate interaction with the tools and data presented. We hope this model will inspire the community to produce reproducible and, importantly, extensible results that will enable us to collectively accelerate the rate at which scientific breakthroughs are discovered, replicated, and extended. PMID:28327935

Enabling Big Geoscience Data Analytics with a Cloud-Based, MapReduce-Enabled and Service-Oriented Workflow Framework

PubMed Central

Li, Zhenlong; Yang, Chaowei; Jin, Baoxuan; Yu, Manzhu; Liu, Kai; Sun, Min; Zhan, Matthew

2015-01-01

Geoscience observations and model simulations are generating vast amounts of multi-dimensional data. Effectively analyzing these data are essential for geoscience studies. However, the tasks are challenging for geoscientists because processing the massive amount of data is both computing and data intensive in that data analytics requires complex procedures and multiple tools. To tackle these challenges, a scientific workflow framework is proposed for big geoscience data analytics. In this framework techniques are proposed by leveraging cloud computing, MapReduce, and Service Oriented Architecture (SOA). Specifically, HBase is adopted for storing and managing big geoscience data across distributed computers. MapReduce-based algorithm framework is developed to support parallel processing of geoscience data. And service-oriented workflow architecture is built for supporting on-demand complex data analytics in the cloud environment. A proof-of-concept prototype tests the performance of the framework. Results show that this innovative framework significantly improves the efficiency of big geoscience data analytics by reducing the data processing time as well as simplifying data analytical procedures for geoscientists. PMID:25742012

Enabling big geoscience data analytics with a cloud-based, MapReduce-enabled and service-oriented workflow framework.

PubMed

Li, Zhenlong; Yang, Chaowei; Jin, Baoxuan; Yu, Manzhu; Liu, Kai; Sun, Min; Zhan, Matthew

2015-01-01

Geoscience observations and model simulations are generating vast amounts of multi-dimensional data. Effectively analyzing these data are essential for geoscience studies. However, the tasks are challenging for geoscientists because processing the massive amount of data is both computing and data intensive in that data analytics requires complex procedures and multiple tools. To tackle these challenges, a scientific workflow framework is proposed for big geoscience data analytics. In this framework techniques are proposed by leveraging cloud computing, MapReduce, and Service Oriented Architecture (SOA). Specifically, HBase is adopted for storing and managing big geoscience data across distributed computers. MapReduce-based algorithm framework is developed to support parallel processing of geoscience data. And service-oriented workflow architecture is built for supporting on-demand complex data analytics in the cloud environment. A proof-of-concept prototype tests the performance of the framework. Results show that this innovative framework significantly improves the efficiency of big geoscience data analytics by reducing the data processing time as well as simplifying data analytical procedures for geoscientists.

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.

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/).

Exploiting NASA's Cumulus Earth Science Cloud Archive with Services and Computation

NASA Astrophysics Data System (ADS)

Pilone, D.; Quinn, P.; Jazayeri, A.; Schuler, I.; Plofchan, P.; Baynes, K.; Ramachandran, R.

2017-12-01

NASA's Earth Observing System Data and Information System (EOSDIS) houses nearly 30PBs of critical Earth Science data and with upcoming missions is expected to balloon to between 200PBs-300PBs over the next seven years. In addition to the massive increase in data collected, researchers and application developers want more and faster access - enabling complex visualizations, long time-series analysis, and cross dataset research without needing to copy and manage massive amounts of data locally. NASA has started prototyping with commercial cloud providers to make this data available in elastic cloud compute environments, allowing application developers direct access to the massive EOSDIS holdings. In this talk we'll explain the principles behind the archive architecture and share our experience of dealing with large amounts of data with serverless architectures including AWS Lambda, the Elastic Container Service (ECS) for long running jobs, and why we dropped thousands of lines of code for AWS Step Functions. We'll discuss best practices and patterns for accessing and using data available in a shared object store (S3) and leveraging events and message passing for sophisticated and highly scalable processing and analysis workflows. Finally we'll share capabilities NASA and cloud services are making available on the archives to enable massively scalable analysis and computation in a variety of formats and tools.

IAServ: an intelligent home care web services platform in a cloud for aging-in-place.

PubMed

Su, Chuan-Jun; Chiang, Chang-Yu

2013-11-12

As the elderly population has been rapidly expanding and the core tax-paying population has been shrinking, the need for adequate elderly health and housing services continues to grow while the resources to provide such services are becoming increasingly scarce. Thus, increasing the efficiency of the delivery of healthcare services through the use of modern technology is a pressing issue. The seamless integration of such enabling technologies as ontology, intelligent agents, web services, and cloud computing is transforming healthcare from hospital-based treatments to home-based self-care and preventive care. A ubiquitous healthcare platform based on this technological integration, which synergizes service providers with patients' needs to be developed to provide personalized healthcare services at the right time, in the right place, and the right manner. This paper presents the development and overall architecture of IAServ (the Intelligent Aging-in-place Home care Web Services Platform) to provide personalized healthcare service ubiquitously in a cloud computing setting to support the most desirable and cost-efficient method of care for the aged-aging in place. The IAServ is expected to offer intelligent, pervasive, accurate and contextually-aware personal care services. Architecturally the implemented IAServ leverages web services and cloud computing to provide economic, scalable, and robust healthcare services over the Internet.

IAServ: An Intelligent Home Care Web Services Platform in a Cloud for Aging-in-Place

PubMed Central

Su, Chuan-Jun; Chiang, Chang-Yu

2013-01-01

As the elderly population has been rapidly expanding and the core tax-paying population has been shrinking, the need for adequate elderly health and housing services continues to grow while the resources to provide such services are becoming increasingly scarce. Thus, increasing the efficiency of the delivery of healthcare services through the use of modern technology is a pressing issue. The seamless integration of such enabling technologies as ontology, intelligent agents, web services, and cloud computing is transforming healthcare from hospital-based treatments to home-based self-care and preventive care. A ubiquitous healthcare platform based on this technological integration, which synergizes service providers with patients’ needs to be developed to provide personalized healthcare services at the right time, in the right place, and the right manner. This paper presents the development and overall architecture of IAServ (the Intelligent Aging-in-place Home care Web Services Platform) to provide personalized healthcare service ubiquitously in a cloud computing setting to support the most desirable and cost-efficient method of care for the aged-aging in place. The IAServ is expected to offer intelligent, pervasive, accurate and contextually-aware personal care services. Architecturally the implemented IAServ leverages web services and cloud computing to provide economic, scalable, and robust healthcare services over the Internet. PMID:24225647

Aether: leveraging linear programming for optimal cloud computing in genomics

PubMed Central

Luber, Jacob M; Tierney, Braden T; Cofer, Evan M; Patel, Chirag J

2018-01-01

Abstract Motivation Across biology, we are seeing rapid developments in scale of data production without a corresponding increase in data analysis capabilities. Results 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. Availability and implementation 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. Contact chirag_patel@hms.harvard.edu or aleksandar.kostic@joslin.harvard.edu Supplementary information Supplementary data are available at Bioinformatics online. PMID:29228186

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.

Analysis of cloud top height and cloud coverage from satellites using the O2 A and B bands

NASA Technical Reports Server (NTRS)

Kuze, Akihiko; Chance, Kelly V.

1994-01-01

Cloud height and cloud coverage detection are important for total ozone retrieval using ultraviolet and visible scattered light. Use of the O2 A and B bands, around 761 and 687 nm, by a satellite-borne instrument of moderately high spectral resolution viewing in the nadir makes it possible to detect cloud top height and related parameters, including fractional coverage. The measured values of a satellite-borne spectrometer are convolutions of the instrument slit function and the atmospheric transmittance between cloud top and satellite. Studies here determine the optical depth between a satellite orbit and the Earth or cloud top height to high accuracy using FASCODE 3. Cloud top height and a cloud coverage parameter are determined by least squares fitting to calculated radiance ratios in the oxygen bands. A grid search method is used to search the parameter space of cloud top height and the coverage parameter to minimize an appropriate sum of squares of deviations. For this search, nonlinearity of the atmospheric transmittance (i.e., leverage based on varying amounts of saturation in the absorption spectrum) is important for distinguishing between cloud top height and fractional coverage. Using the above-mentioned method, an operational cloud detection algorithm which uses minimal computation time can be implemented.

Privacy-Aware Relevant Data Access with Semantically Enriched Search Queries for Untrusted Cloud Storage Services.

PubMed

Pervez, Zeeshan; Ahmad, Mahmood; Khattak, Asad Masood; Lee, Sungyoung; Chung, Tae Choong

2016-01-01

Privacy-aware search of outsourced data ensures relevant data access in the untrusted domain of a public cloud service provider. Subscriber of a public cloud storage service can determine the presence or absence of a particular keyword by submitting search query in the form of a trapdoor. However, these trapdoor-based search queries are limited in functionality and cannot be used to identify secure outsourced data which contains semantically equivalent information. In addition, trapdoor-based methodologies are confined to pre-defined trapdoors and prevent subscribers from searching outsourced data with arbitrarily defined search criteria. To solve the problem of relevant data access, we have proposed an index-based privacy-aware search methodology that ensures semantic retrieval of data from an untrusted domain. This method ensures oblivious execution of a search query and leverages authorized subscribers to model conjunctive search queries without relying on predefined trapdoors. A security analysis of our proposed methodology shows that, in a conspired attack, unauthorized subscribers and untrusted cloud service providers cannot deduce any information that can lead to the potential loss of data privacy. A computational time analysis on commodity hardware demonstrates that our proposed methodology requires moderate computational resources to model a privacy-aware search query and for its oblivious evaluation on a cloud service provider.

Privacy-Aware Relevant Data Access with Semantically Enriched Search Queries for Untrusted Cloud Storage Services

PubMed Central

Pervez, Zeeshan; Ahmad, Mahmood; Khattak, Asad Masood; Lee, Sungyoung; Chung, Tae Choong

2016-01-01

Privacy-aware search of outsourced data ensures relevant data access in the untrusted domain of a public cloud service provider. Subscriber of a public cloud storage service can determine the presence or absence of a particular keyword by submitting search query in the form of a trapdoor. However, these trapdoor-based search queries are limited in functionality and cannot be used to identify secure outsourced data which contains semantically equivalent information. In addition, trapdoor-based methodologies are confined to pre-defined trapdoors and prevent subscribers from searching outsourced data with arbitrarily defined search criteria. To solve the problem of relevant data access, we have proposed an index-based privacy-aware search methodology that ensures semantic retrieval of data from an untrusted domain. This method ensures oblivious execution of a search query and leverages authorized subscribers to model conjunctive search queries without relying on predefined trapdoors. A security analysis of our proposed methodology shows that, in a conspired attack, unauthorized subscribers and untrusted cloud service providers cannot deduce any information that can lead to the potential loss of data privacy. A computational time analysis on commodity hardware demonstrates that our proposed methodology requires moderate computational resources to model a privacy-aware search query and for its oblivious evaluation on a cloud service provider. PMID:27571421

Reaching for the cloud: on the lessons learned from grid computing technology transfer process to the biomedical community.

PubMed

Mohammed, Yassene; Dickmann, Frank; Sax, Ulrich; von Voigt, Gabriele; Smith, Matthew; Rienhoff, Otto

2010-01-01

Natural scientists such as physicists pioneered the sharing of computing resources, which led to the creation of the Grid. The inter domain transfer process of this technology has hitherto been an intuitive process without in depth analysis. Some difficulties facing the life science community in this transfer can be understood using the Bozeman's "Effectiveness Model of Technology Transfer". Bozeman's and classical technology transfer approaches deal with technologies which have achieved certain stability. Grid and Cloud solutions are technologies, which are still in flux. We show how Grid computing creates new difficulties in the transfer process that are not considered in Bozeman's model. We show why the success of healthgrids should be measured by the qualified scientific human capital and the opportunities created, and not primarily by the market impact. We conclude with recommendations that can help improve the adoption of Grid and Cloud solutions into the biomedical community. These results give a more concise explanation of the difficulties many life science IT projects are facing in the late funding periods, and show leveraging steps that can help overcoming the "vale of tears".

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

Duro, Francisco Rodrigo; Blas, Javier Garcia; Isaila, Florin

The increasing volume of scientific data and the limited scalability and performance of storage systems are currently presenting a significant limitation for the productivity of the scientific workflows running on both high-performance computing (HPC) and cloud platforms. Clearly needed is better integration of storage systems and workflow engines to address this problem. This paper presents and evaluates a novel solution that leverages codesign principles for integrating Hercules—an in-memory data store—with a workflow management system. We consider four main aspects: workflow representation, task scheduling, task placement, and task termination. As a result, the experimental evaluation on both cloud and HPC systemsmore » demonstrates significant performance and scalability improvements over existing state-of-the-art approaches.« less

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 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

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.

Cloud Computing Strategy

DTIC Science & Technology

2012-07-01

regardless of  access point or the device being used across the Global Information Grid ( GIG ).  These data  centers will host existing applications...state.  It  illustrates that the DoD Enterprise Cloud is an integrated environment on the  GIG , consisting of  DoD Components, commercial entities...Operations and Maintenance (O&M) costs by  leveraging  economies  of scale, and automate monitoring and provisioning to reduce the  human cost of service

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.

Towards Large-area Field-scale Operational Evapotranspiration for Water Use Mapping

NASA Astrophysics Data System (ADS)

Senay, G. B.; Friedrichs, M.; Morton, C.; Huntington, J. L.; Verdin, J.

2017-12-01

Field-scale evapotranspiration (ET) estimates are needed for improving surface and groundwater use and water budget studies. Ideally, field-scale ET estimates would be at regional to national levels and cover long time periods. As a result of large data storage and computational requirements associated with processing field-scale satellite imagery such as Landsat, numerous challenges remain to develop operational ET estimates over large areas for detailed water use and availability studies. However, the combination of new science, data availability, and cloud computing technology is enabling unprecedented capabilities for ET mapping. To demonstrate this capability, we used Google's Earth Engine cloud computing platform to create nationwide annual ET estimates with 30-meter resolution Landsat ( 16,000 images) and gridded weather data using the Operational Simplified Surface Energy Balance (SSEBop) model in support of the National Water Census, a USGS research program designed to build decision support capacity for water management agencies and other natural resource managers. By leveraging Google's Earth Engine Application Programming Interface (API) and developing software in a collaborative, open-platform environment, we rapidly advance from research towards applications for large-area field-scale ET mapping. Cloud computing of the Landsat image archive combined with other satellite, climate, and weather data, is creating never imagined opportunities for assessing ET model behavior and uncertainty, and ultimately providing the ability for more robust operational monitoring and assessment of water use at field-scales.

Securing SIFT: Privacy-preserving Outsourcing Computation of Feature Extractions Over Encrypted Image Data.

PubMed

Hu, Shengshan; Wang, Qian; Wang, Jingjun; Qin, Zhan; Ren, Kui

2016-05-13

Advances in cloud computing have greatly motivated data owners to outsource their huge amount of personal multimedia data and/or computationally expensive tasks onto the cloud by leveraging its abundant resources for cost saving and flexibility. Despite the tremendous benefits, the outsourced multimedia data and its originated applications may reveal the data owner's private information, such as the personal identity, locations or even financial profiles. This observation has recently aroused new research interest on privacy-preserving computations over outsourced multimedia data. In this paper, we propose an effective and practical privacy-preserving computation outsourcing protocol for the prevailing scale-invariant feature transform (SIFT) over massive encrypted image data. We first show that previous solutions to this problem have either efficiency/security or practicality issues, and none can well preserve the important characteristics of the original SIFT in terms of distinctiveness and robustness. We then present a new scheme design that achieves efficiency and security requirements simultaneously with the preservation of its key characteristics, by randomly splitting the original image data, designing two novel efficient protocols for secure multiplication and comparison, and carefully distributing the feature extraction computations onto two independent cloud servers. We both carefully analyze and extensively evaluate the security and effectiveness of our design. The results show that our solution is practically secure, outperforms the state-of-theart, and performs comparably to the original SIFT in terms of various characteristics, including rotation invariance, image scale invariance, robust matching across affine distortion, addition of noise and change in 3D viewpoint and illumination.

SecSIFT: Privacy-preserving Outsourcing Computation of Feature Extractions Over Encrypted Image Data.

PubMed

Hu, Shengshan; Wang, Qian; Wang, Jingjun; Qin, Zhan; Ren, Kui

2016-05-13

Advances in cloud computing have greatly motivated data owners to outsource their huge amount of personal multimedia data and/or computationally expensive tasks onto the cloud by leveraging its abundant resources for cost saving and flexibility. Despite the tremendous benefits, the outsourced multimedia data and its originated applications may reveal the data owner's private information, such as the personal identity, locations or even financial profiles. This observation has recently aroused new research interest on privacy-preserving computations over outsourced multimedia data. In this paper, we propose an effective and practical privacy-preserving computation outsourcing protocol for the prevailing scale-invariant feature transform (SIFT) over massive encrypted image data. We first show that previous solutions to this problem have either efficiency/security or practicality issues, and none can well preserve the important characteristics of the original SIFT in terms of distinctiveness and robustness. We then present a new scheme design that achieves efficiency and security requirements simultaneously with the preservation of its key characteristics, by randomly splitting the original image data, designing two novel efficient protocols for secure multiplication and comparison, and carefully distributing the feature extraction computations onto two independent cloud servers. We both carefully analyze and extensively evaluate the security and effectiveness of our design. The results show that our solution is practically secure, outperforms the state-of-theart, and performs comparably to the original SIFT in terms of various characteristics, including rotation invariance, image scale invariance, robust matching across affine distortion, addition of noise and change in 3D viewpoint and illumination.

Experimental evaluation of a flexible I/O architecture for accelerating workflow engines in ultrascale environments

DOE PAGES

Duro, Francisco Rodrigo; Blas, Javier Garcia; Isaila, Florin; ...

2016-10-06

The increasing volume of scientific data and the limited scalability and performance of storage systems are currently presenting a significant limitation for the productivity of the scientific workflows running on both high-performance computing (HPC) and cloud platforms. Clearly needed is better integration of storage systems and workflow engines to address this problem. This paper presents and evaluates a novel solution that leverages codesign principles for integrating Hercules—an in-memory data store—with a workflow management system. We consider four main aspects: workflow representation, task scheduling, task placement, and task termination. As a result, the experimental evaluation on both cloud and HPC systemsmore » demonstrates significant performance and scalability improvements over existing state-of-the-art approaches.« less

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

Private and Efficient Query Processing on Outsourced Genomic Databases.

PubMed

Ghasemi, Reza; Al Aziz, Md Momin; Mohammed, Noman; Dehkordi, Massoud Hadian; Jiang, Xiaoqian

2017-09-01

Applications of genomic studies are spreading rapidly in many domains of science and technology such as healthcare, biomedical research, direct-to-consumer services, and legal and forensic. However, there are a number of obstacles that make it hard to access and process a big genomic database for these applications. First, sequencing genomic sequence is a time consuming and expensive process. Second, it requires large-scale computation and storage systems to process genomic sequences. Third, genomic databases are often owned by different organizations, and thus, not available for public usage. Cloud computing paradigm can be leveraged to facilitate the creation and sharing of big genomic databases for these applications. Genomic data owners can outsource their databases in a centralized cloud server to ease the access of their databases. However, data owners are reluctant to adopt this model, as it requires outsourcing the data to an untrusted cloud service provider that may cause data breaches. In this paper, we propose a privacy-preserving model for outsourcing genomic data to a cloud. The proposed model enables query processing while providing privacy protection of genomic databases. Privacy of the individuals is guaranteed by permuting and adding fake genomic records in the database. These techniques allow cloud to evaluate count and top-k queries securely and efficiently. Experimental results demonstrate that a count and a top-k query over 40 Single Nucleotide Polymorphisms (SNPs) in a database of 20 000 records takes around 100 and 150 s, respectively.

Private and Efficient Query Processing on Outsourced Genomic Databases

PubMed Central

Ghasemi, Reza; Al Aziz, Momin; Mohammed, Noman; Dehkordi, Massoud Hadian; Jiang, Xiaoqian

2017-01-01

Applications of genomic studies are spreading rapidly in many domains of science and technology such as healthcare, biomedical research, direct-to-consumer services, and legal and forensic. However, there are a number of obstacles that make it hard to access and process a big genomic database for these applications. First, sequencing genomic sequence is a time-consuming and expensive process. Second, it requires large-scale computation and storage systems to processes genomic sequences. Third, genomic databases are often owned by different organizations and thus not available for public usage. Cloud computing paradigm can be leveraged to facilitate the creation and sharing of big genomic databases for these applications. Genomic data owners can outsource their databases in a centralized cloud server to ease the access of their databases. However, data owners are reluctant to adopt this model, as it requires outsourcing the data to an untrusted cloud service provider that may cause data breaches. In this paper, we propose a privacy-preserving model for outsourcing genomic data to a cloud. The proposed model enables query processing while providing privacy protection of genomic databases. Privacy of the individuals is guaranteed by permuting and adding fake genomic records in the database. These techniques allow cloud to evaluate count and top-k queries securely and efficiently. Experimental results demonstrate that a count and a top-k query over 40 SNPs in a database of 20,000 records takes around 100 and 150 seconds, respectively. PMID:27834660

Enabling a new Paradigm to Address Big Data and Open Science Challenges

NASA Astrophysics Data System (ADS)

Ramamurthy, Mohan; Fisher, Ward

2017-04-01

Data are not only the lifeblood of the geosciences but they have become the currency of the modern world in science and society. Rapid advances in computing, communi¬cations, and observational technologies — along with concomitant advances in high-resolution modeling, ensemble and coupled-systems predictions of the Earth system — are revolutionizing nearly every aspect of our field. Modern data volumes from high-resolution ensemble prediction/projection/simulation systems and next-generation remote-sensing systems like hyper-spectral satellite sensors and phased-array radars are staggering. For example, CMIP efforts alone will generate many petabytes of climate projection data for use in assessments of climate change. And NOAA's National Climatic Data Center projects that it will archive over 350 petabytes by 2030. For researchers and educators, this deluge and the increasing complexity of data brings challenges along with the opportunities for discovery and scientific breakthroughs. The potential for big data to transform the geosciences is enormous, but realizing the next frontier depends on effectively managing, analyzing, and exploiting these heterogeneous data sources, extracting knowledge and useful information from heterogeneous data sources in ways that were previously impossible, to enable discoveries and gain new insights. At the same time, there is a growing focus on the area of "Reproducibility or Replicability in Science" that has implications for Open Science. The advent of cloud computing has opened new avenues for not only addressing both big data and Open Science challenges to accelerate scientific discoveries. However, to successfully leverage the enormous potential of cloud technologies, it will require the data providers and the scientific communities to develop new paradigms to enable next-generation workflows and transform the conduct of science. Making data readily available is a necessary but not a sufficient condition. Data providers also need to give scientists an ecosystem that includes data, tools, workflows and other services needed to perform analytics, integration, interpretation, and synthesis - all in the same environment or platform. Instead of moving data to processing systems near users, as is the tradition, the cloud permits one to bring processing, computing, analysis and visualization to data - so called data proximate workbench capabilities, also known as server-side processing. In this talk, I will present the ongoing work at Unidata to facilitate a new paradigm for doing science by offering a suite of tools, resources, and platforms to leverage cloud technologies for addressing both big data and Open Science/reproducibility challenges. That work includes the development and deployment of new protocols for data access and server-side operations and Docker container images of key applications, JupyterHub Python notebook tools, and cloud-based analysis and visualization capability via the CloudIDV tool to enable reproducible workflows and effectively use the accessed data.

Towards a Multi-Mission, Airborne Science Data System Environment

NASA Astrophysics Data System (ADS)

Crichton, D. J.; Hardman, S.; Law, E.; Freeborn, D.; Kay-Im, E.; Lau, G.; Oswald, J.

2011-12-01

NASA earth science instruments are increasingly relying on airborne missions. However, traditionally, there has been limited common infrastructure support available to principal investigators in the area of science data systems. As a result, each investigator has been required to develop their own computing infrastructures for the science data system. Typically there is little software reuse and many projects lack sufficient resources to provide a robust infrastructure to capture, process, distribute and archive the observations acquired from airborne flights. At NASA's Jet Propulsion Laboratory (JPL), we have been developing a multi-mission data system infrastructure for airborne instruments called the Airborne Cloud Computing Environment (ACCE). ACCE encompasses the end-to-end lifecycle covering planning, provisioning of data system capabilities, and support for scientific analysis in order to improve the quality, cost effectiveness, and capabilities to enable new scientific discovery and research in earth observation. This includes improving data system interoperability across each instrument. A principal characteristic is being able to provide an agile infrastructure that is architected to allow for a variety of configurations of the infrastructure from locally installed compute and storage services to provisioning those services via the "cloud" from cloud computer vendors such as Amazon.com. Investigators often have different needs that require a flexible configuration. The data system infrastructure is built on the Apache's Object Oriented Data Technology (OODT) suite of components which has been used for a number of spaceborne missions and provides a rich set of open source software components and services for constructing science processing and data management systems. In 2010, a partnership was formed between the ACCE team and the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) mission to support the data processing and data management needs. A principal goal is to provide support for the Fourier Transform Spectrometer (FTS) instrument which will produce over 700,000 soundings over the life of their three-year mission. The cost to purchase and operate a cluster-based system in order to generate Level 2 Full Physics products from this data was prohibitive. Through an evaluation of cloud computing solutions, Amazon's Elastic Compute Cloud (EC2) was selected for the CARVE deployment. As the ACCE infrastructure is developed and extended to form an infrastructure for airborne missions, the experience of working with CARVE has provided a number of lessons learned and has proven to be important in reinforcing the unique aspects of airborne missions and the importance of the ACCE infrastructure in developing a cost effective, flexible multi-mission capability that leverages emerging capabilities in cloud computing, workflow management, and distributed computing.

Top-Ten IT Issues, 2013: Welcome to the Connected Age

ERIC Educational Resources Information Center

Grajek, Susan

2013-01-01

The EDUCAUSE IT Issues Panel has identified its annual top-ten IT issues for higher education, as follows: (1) Leveraging the wireless and device explosion on campus; (2) Improving student outcomes through an approach that leverages technology; (3) Developing an institution-wide cloud strategy to help the institution select the right sourcing and…

Hybrid architecture for building secure sensor networks

NASA Astrophysics Data System (ADS)

Owens, Ken R., Jr.; Watkins, Steve E.

2012-04-01

Sensor networks have various communication and security architectural concerns. Three approaches are defined to address these concerns for sensor networks. The first area is the utilization of new computing architectures that leverage embedded virtualization software on the sensor. Deploying a small, embedded virtualization operating system on the sensor nodes that is designed to communicate to low-cost cloud computing infrastructure in the network is the foundation to delivering low-cost, secure sensor networks. The second area focuses on securing the sensor. Sensor security components include developing an identification scheme, and leveraging authentication algorithms and protocols that address security assurance within the physical, communication network, and application layers. This function will primarily be accomplished through encrypting the communication channel and integrating sensor network firewall and intrusion detection/prevention components to the sensor network architecture. Hence, sensor networks will be able to maintain high levels of security. The third area addresses the real-time and high priority nature of the data that sensor networks collect. This function requires that a quality-of-service (QoS) definition and algorithm be developed for delivering the right data at the right time. A hybrid architecture is proposed that combines software and hardware features to handle network traffic with diverse QoS requirements.

Sentinel-1 Interferometry from the Cloud to the Scientist

NASA Astrophysics Data System (ADS)

Garron, J.; Stoner, C.; Johnston, A.; Arko, S. A.

2017-12-01

Big data problems and solutions are growing in the technological and scientific sectors daily. Cloud computing is a vertically and horizontally scalable solution available now for archiving and processing large volumes of data quickly, without significant on-site computing hardware costs. Be that as it may, the conversion of scientific data processors to these powerful platforms requires not only the proof of concept, but the demonstration of credibility in an operational setting. The Alaska Satellite Facility (ASF) Distributed Active Archive Center (DAAC), in partnership with NASA's Jet Propulsion Laboratory, is exploring the functional architecture of Amazon Web Services cloud computing environment for the processing, distribution and archival of Synthetic Aperture Radar data in preparation for the NASA-ISRO Synthetic Aperture Radar (NISAR) Mission. Leveraging built-in AWS services for logging, monitoring and dashboarding, the GRFN (Getting Ready for NISAR) team has built a scalable processing, distribution and archival system of Sentinel-1 L2 interferograms produced using the ISCE algorithm. This cloud-based functional prototype provides interferograms over selected global land deformation features (volcanoes, land subsidence, seismic zones) and are accessible to scientists via NASA's EarthData Search client and the ASF DAACs primary SAR interface, Vertex, for direct download. The interferograms are produced using nearest-neighbor logic for identifying pairs of granules for interferometric processing, creating deep stacks of BETA products from almost every satellite orbit for scientists to explore. This presentation highlights the functional lessons learned to date from this exercise, including the cost analysis of various data lifecycle policies as implemented through AWS. While demonstrating the architecture choices in support of efficient big science data management, we invite feedback and questions about the process and products from the InSAR community.

Considerations for Software Defined Networking (SDN): Approaches and use cases

NASA Astrophysics Data System (ADS)

Bakshi, K.

Software Defined Networking (SDN) is an evolutionary approach to network design and functionality based on the ability to programmatically modify the behavior of network devices. SDN uses user-customizable and configurable software that's independent of hardware to enable networked systems to expand data flow control. SDN is in large part about understanding and managing a network as a unified abstraction. It will make networks more flexible, dynamic, and cost-efficient, while greatly simplifying operational complexity. And this advanced solution provides several benefits including network and service customizability, configurability, improved operations, and increased performance. There are several approaches to SDN and its practical implementation. Among them, two have risen to prominence with differences in pedigree and implementation. This paper's main focus will be to define, review, and evaluate salient approaches and use cases of the OpenFlow and Virtual Network Overlay approaches to SDN. OpenFlow is a communication protocol that gives access to the forwarding plane of a network's switches and routers. The Virtual Network Overlay relies on a completely virtualized network infrastructure and services to abstract the underlying physical network, which allows the overlay to be mobile to other physical networks. This is an important requirement for cloud computing, where applications and associated network services are migrated to cloud service providers and remote data centers on the fly as resource demands dictate. The paper will discuss how and where SDN can be applied and implemented, including research and academia, virtual multitenant data center, and cloud computing applications. Specific attention will be given to the cloud computing use case, where automated provisioning and programmable overlay for scalable multi-tenancy is leveraged via the SDN approach.

A Lab Based Method for Exoplanet Cloud and Aerosol Characterization

NASA Astrophysics Data System (ADS)

Johnson, A. V.; Schneiderman, T. M.; Bauer, A. J. R.; Cziczo, D. J.

2017-12-01

The atmospheres of some smaller, cooler exoplanets, like GJ 1214b, lack strong spectral features. This may suggest the presence of a high, optically thick cloud layer and poses great challenges for atmospheric characterization, but there is hope. The study of extraterrestrial atmospheres with terrestrial based techniques has proven useful for understanding the cloud-laden atmospheres of our solar system. Here we build on this by leveraging laboratory-based, terrestrial cloud particle instrumentation to better understand the microphysical and radiative properties of proposed exoplanet cloud and aerosol particles. The work to be presented focuses on the scattering properties of single particles, that may be representative of those suspended in exoplanet atmospheres, levitated in an Electrodynamic Balance (EDB). I will discuss how we leverage terrestrial based cloud microphysics for exoplanet applications, the instruments for single and ensemble particle studies used in this work, our investigation of ammonium nitrate (NH4NO3) scattering across temperature dependent crystalline phase changes, and the steps we are taking toward the collection of scattering phase functions and polarization of scattered light for exoplanet cloud analogs. Through this and future studies we hope to better understand how upper level cloud and/or aerosol particles in exoplanet atmospheres interact with incoming radiation from their host stars and what atmospheric information may still be obtainable through remote observations when no spectral features are observed.

Earth Science Data Fusion with Event Building Approach

NASA Technical Reports Server (NTRS)

Lukashin, C.; Bartle, Ar.; Callaway, E.; Gyurjyan, V.; Mancilla, S.; Oyarzun, R.; Vakhnin, A.

2015-01-01

Objectives of the NASA Information And Data System (NAIADS) project are to develop a prototype of a conceptually new middleware framework to modernize and significantly improve efficiency of the Earth Science data fusion, big data processing and analytics. The key components of the NAIADS include: Service Oriented Architecture (SOA) multi-lingual framework, multi-sensor coincident data Predictor, fast into-memory data Staging, multi-sensor data-Event Builder, complete data-Event streaming (a work flow with minimized IO), on-line data processing control and analytics services. The NAIADS project is leveraging CLARA framework, developed in Jefferson Lab, and integrated with the ZeroMQ messaging library. The science services are prototyped and incorporated into the system. Merging the SCIAMACHY Level-1 observations and MODIS/Terra Level-2 (Clouds and Aerosols) data products, and ECMWF re- analysis will be used for NAIADS demonstration and performance tests in compute Cloud and Cluster environments.

The Synthetic Aperture Radar Science Data Processing Foundry Concept for Earth Science

NASA Astrophysics Data System (ADS)

Rosen, P. A.; Hua, H.; Norton, C. D.; Little, M. M.

2015-12-01

Since 2008, NASA's Earth Science Technology Office and the Advanced Information Systems Technology Program have invested in two technology evolutions to meet the needs of the community of scientists exploiting the rapidly growing database of international synthetic aperture radar (SAR) data. JPL, working with the science community, has developed the InSAR Scientific Computing Environment (ISCE), a next-generation interferometric SAR processing system that is designed to be flexible and extensible. ISCE currently supports many international space borne data sets but has been primarily focused on geodetic science and applications. A second evolutionary path, the Advanced Rapid Imaging and Analysis (ARIA) science data system, uses ISCE as its core science data processing engine and produces automated science and response products, quality assessments and metadata. The success of this two-front effort has been demonstrated in NASA's ability to respond to recent events with useful disaster support. JPL has enabled high-volume and low latency data production by the re-use of the hybrid cloud computing science data system (HySDS) that runs ARIA, leveraging on-premise cloud computing assets that are able to burst onto the Amazon Web Services (AWS) services as needed. Beyond geodetic applications, needs have emerged to process large volumes of time-series SAR data collected for estimation of biomass and its change, in such campaigns as the upcoming AfriSAR field campaign. ESTO is funding JPL to extend the ISCE-ARIA model to a "SAR Science Data Processing Foundry" to on-ramp new data sources and to produce new science data products to meet the needs of science teams and, in general, science community members. An extension of the ISCE-ARIA model to support on-demand processing will permit PIs to leverage this Foundry to produce data products from accepted data sources when they need them. This paper will describe each of the elements of the SAR SDP Foundry and describe their integration into a new conceptual approach to enable more effective use of SAR instruments.

Cloud Based Resource for Data Hosting, Visualization and Analysis Using UCSC Cancer Genomics Browser | Informatics Technology for Cancer Research (ITCR)

Cancer.gov

The Cancer Analysis Virtual Machine (CAVM) project will leverage cloud technology, the UCSC Cancer Genomics Browser, and the Galaxy analysis workflow system to provide investigators with a flexible, scalable platform for hosting, visualizing and analyzing their own genomic data.

FACE-IT. A Science Gateway for Food Security Research

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

Montella, Raffaele; Kelly, David; Xiong, Wei

Progress in sustainability science is hindered by challenges in creating and managing complex data acquisition, processing, simulation, post-processing, and intercomparison pipelines. To address these challenges, we developed the Framework to Advance Climate, Economic, and Impact Investigations with Information Technology (FACE-IT) for crop and climate impact assessments. This integrated data processing and simulation framework enables data ingest from geospatial archives; data regridding, aggregation, and other processing prior to simulation; large-scale climate impact simulations with agricultural and other models, leveraging high-performance and cloud computing; and post-processing to produce aggregated yields and ensemble variables needed for statistics, for model intercomparison, and to connectmore » biophysical models to global and regional economic models. FACE-IT leverages the capabilities of the Globus Galaxies platform to enable the capture of workflows and outputs in well-defined, reusable, and comparable forms. We describe FACE-IT and applications within the Agricultural Model Intercomparison and Improvement Project and the Center for Robust Decision-making on Climate and Energy Policy.« less

Cloud Computing Applications in Support of Earth Science Activities at Marshall Space Flight Center

NASA Astrophysics Data System (ADS)

Molthan, A.; Limaye, A. S.

2011-12-01

Currently, the NASA Nebula Cloud Computing Platform is available to Agency personnel in a pre-release status as the system undergoes a formal operational readiness review. Over the past year, two projects within the Earth Science Office at NASA Marshall Space Flight Center have been investigating the performance and value of Nebula's "Infrastructure as a Service", or "IaaS" concept and applying cloud computing concepts to advance their respective mission goals. The Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique NASA satellite observations and weather forecasting capabilities for use within the operational forecasting community through partnerships with NOAA's National Weather Service (NWS). SPoRT has evaluated the performance of the Weather Research and Forecasting (WRF) model on virtual machines deployed within Nebula and used Nebula instances to simulate local forecasts in support of regional forecast studies of interest to select NWS forecast offices. In addition to weather forecasting applications, rapidly deployable Nebula virtual machines have supported the processing of high resolution NASA satellite imagery to support disaster assessment following the historic severe weather and tornado outbreak of April 27, 2011. Other modeling and satellite analysis activities are underway in support of NASA's SERVIR program, which integrates satellite observations, ground-based data and forecast models to monitor environmental change and improve disaster response in Central America, the Caribbean, Africa, and the Himalayas. Leveraging SPoRT's experience, SERVIR is working to establish a real-time weather forecasting model for Central America. Other modeling efforts include hydrologic forecasts for Kenya, driven by NASA satellite observations and reanalysis data sets provided by the broader meteorological community. Forecast modeling efforts are supplemented by short-term forecasts of convective initiation, determined by geostationary satellite observations processed on virtual machines powered by Nebula. This presentation will provide an overview of these activities from a scientific and cloud computing applications perspective, identifying the strengths and weaknesses for deploying each project within an IaaS environment, and ways to collaborate with the Nebula or other cloud-user communities to collaborate on projects as they go forward.

Clinical operations generation next… The age of technology and outsourcing

PubMed Central

Temkar, Priya

2015-01-01

Huge cost pressures and the need to drive faster approvals has driven a technology transformation in the clinical trial (CT) industry. The CT industry is thus leveraging mobile data, cloud computing, social media, robotic automation, and electronic source to drive efficiencies in a big way. Outsourcing of clinical operations support services to technology companies with a clinical edge is gaining tremendous importance. This paper provides an overview of current technology trends, applicable Food and Drug Administration (FDA) guidelines, basic challenges that the pharma industry is facing in trying to implement such changes and its shift towards outsourcing these services to enable it to focus on site operations. PMID:26623386

Clinical operations generation next… The age of technology and outsourcing.

PubMed

Temkar, Priya

2015-01-01

Huge cost pressures and the need to drive faster approvals has driven a technology transformation in the clinical trial (CT) industry. The CT industry is thus leveraging mobile data, cloud computing, social media, robotic automation, and electronic source to drive efficiencies in a big way. Outsourcing of clinical operations support services to technology companies with a clinical edge is gaining tremendous importance. This paper provides an overview of current technology trends, applicable Food and Drug Administration (FDA) guidelines, basic challenges that the pharma industry is facing in trying to implement such changes and its shift towards outsourcing these services to enable it to focus on site operations.

Towards Scalable Graph Computation on Mobile Devices.

PubMed

Chen, Yiqi; Lin, Zhiyuan; Pienta, Robert; Kahng, Minsuk; Chau, Duen Horng

2014-10-01

Mobile devices have become increasingly central to our everyday activities, due to their portability, multi-touch capabilities, and ever-improving computational power. Such attractive features have spurred research interest in leveraging mobile devices for computation. We explore a novel approach that aims to use a single mobile device to perform scalable graph computation on large graphs that do not fit in the device's limited main memory, opening up the possibility of performing on-device analysis of large datasets, without relying on the cloud. Based on the familiar memory mapping capability provided by today's mobile operating systems, our approach to scale up computation is powerful and intentionally kept simple to maximize its applicability across the iOS and Android platforms. Our experiments demonstrate that an iPad mini can perform fast computation on large real graphs with as many as 272 million edges (Google+ social graph), at a speed that is only a few times slower than a 13″ Macbook Pro. Through creating a real world iOS app with this technique, we demonstrate the strong potential application for scalable graph computation on a single mobile device using our approach.

Towards Scalable Graph Computation on Mobile Devices

PubMed Central

Chen, Yiqi; Lin, Zhiyuan; Pienta, Robert; Kahng, Minsuk; Chau, Duen Horng

2015-01-01

Mobile devices have become increasingly central to our everyday activities, due to their portability, multi-touch capabilities, and ever-improving computational power. Such attractive features have spurred research interest in leveraging mobile devices for computation. We explore a novel approach that aims to use a single mobile device to perform scalable graph computation on large graphs that do not fit in the device's limited main memory, opening up the possibility of performing on-device analysis of large datasets, without relying on the cloud. Based on the familiar memory mapping capability provided by today's mobile operating systems, our approach to scale up computation is powerful and intentionally kept simple to maximize its applicability across the iOS and Android platforms. Our experiments demonstrate that an iPad mini can perform fast computation on large real graphs with as many as 272 million edges (Google+ social graph), at a speed that is only a few times slower than a 13″ Macbook Pro. Through creating a real world iOS app with this technique, we demonstrate the strong potential application for scalable graph computation on a single mobile device using our approach. PMID:25859564

Using Field and Satellite Measurements to Improve Snow and Riming Processes in Cloud Resolving Models

NASA Technical Reports Server (NTRS)

Colle, Brian A.; Molthan, Andrew L.

2013-01-01

The representation of clouds in climate and weather models is a driver in forecast uncertainty. Cloud microphysics parameterizations are challenged by having to represent a diverse range of ice species. Key characteristics of predicted ice species include habit and fall speed, and complex interactions that result from mixed-phased processes like riming. Our proposed activity leverages Global Precipitation Measurement (GPM) Mission ground validation studies to improve parameterizations

Models and Simulations as a Service: Exploring the Use of Galaxy for Delivering Computational Models

PubMed Central

Walker, Mark A.; Madduri, Ravi; Rodriguez, Alex; Greenstein, Joseph L.; Winslow, Raimond L.

2016-01-01

We describe the ways in which Galaxy, a web-based reproducible research platform, can be used for web-based sharing of complex computational models. Galaxy allows users to seamlessly customize and run simulations on cloud computing resources, a concept we refer to as Models and Simulations as a Service (MaSS). To illustrate this application of Galaxy, we have developed a tool suite for simulating a high spatial-resolution model of the cardiac Ca2+ spark that requires supercomputing resources for execution. We also present tools for simulating models encoded in the SBML and CellML model description languages, thus demonstrating how Galaxy’s reproducible research features can be leveraged by existing technologies. Finally, we demonstrate how the Galaxy workflow editor can be used to compose integrative models from constituent submodules. This work represents an important novel approach, to our knowledge, to making computational simulations more accessible to the broader scientific community. PMID:26958881

Cloud parallel processing of tandem mass spectrometry based proteomics data.

PubMed

Mohammed, Yassene; Mostovenko, Ekaterina; Henneman, Alex A; Marissen, Rob J; Deelder, André M; Palmblad, Magnus

2012-10-05

Data analysis in mass spectrometry based proteomics struggles to keep pace with the advances in instrumentation and the increasing rate of data acquisition. Analyzing this data involves multiple steps requiring diverse software, using different algorithms and data formats. Speed and performance of the mass spectral search engines are continuously improving, although not necessarily as needed to face the challenges of acquired big data. Improving and parallelizing the search algorithms is one possibility; data decomposition presents another, simpler strategy for introducing parallelism. We describe a general method for parallelizing identification of tandem mass spectra using data decomposition that keeps the search engine intact and wraps the parallelization around it. We introduce two algorithms for decomposing mzXML files and recomposing resulting pepXML files. This makes the approach applicable to different search engines, including those relying on sequence databases and those searching spectral libraries. We use cloud computing to deliver the computational power and scientific workflow engines to interface and automate the different processing steps. We show how to leverage these technologies to achieve faster data analysis in proteomics and present three scientific workflows for parallel database as well as spectral library search using our data decomposition programs, X!Tandem and SpectraST.

Hybrid Pluggable Processing Pipeline (HyP3): A cloud-based infrastructure for generic processing of SAR data

NASA Astrophysics Data System (ADS)

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

2016-12-01

A problem often faced by Earth science researchers is how to scale algorithms that were developed against few datasets and take them to regional or global scales. One significant hurdle can be 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, while remaining generic enough to incorporate new algorithms with limited administration time or expense. Principally built by three undergraduate students at the ASF DAAC, the HyP3 system relies on core Amazon services such as Lambda, the Simple Notification Service (SNS), Relational Database Service (RDS), Elastic Compute Cloud (EC2), Simple Storage Service (S3), and Elastic Beanstalk. The HyP3 user interface was written using elastic beanstalk, and the system uses SNS and Lamdba to handle creating, instantiating, executing, and terminating EC2 instances automatically. Data are sent to S3 for delivery to customers and removed using standard data lifecycle management rules. In HyP3 all data processing is ephemeral; there are no persistent processes taking compute and storage resources or generating added cost. When complete, HyP3 will leverage the automatic scaling up and down of EC2 compute power to respond to event-driven demand surges correlated with natural disaster or reprocessing efforts. Massive simultaneous processing within EC2 will be able match the demand spike in ways conventional physical computing power never could, and then tail off incurring no costs when not needed. This presentation will focus on the development techniques and technologies that were used in developing the HyP3 system. Data and process flow will be shown, highlighting the benefits of the cloud for each step. Finally, the steps for integrating a new processing algorithm will be demonstrated. This is the true power of HyP3; allowing people to upload their own algorithms and execute them at archive level scales.

Wearable Internet of Things - from human activity tracking to clinical integration.

PubMed

Kumari, Poonam; Lopez-Benitez, Miguel; Gyu Myoung Lee; Tae-Seong Kim; Minhas, Atul S

2017-07-01

Wearable devices for human activity tracking have been emerging rapidly. Most of them are capable of sending health statistics to smartphones, smartwatches or smart bands. However, they only provide the data for individual analysis and their data is not integrated into clinical practice. Leveraging on the Internet of Things (IoT), edge and cloud computing technologies, we propose an architecture which is capable of providing cloud based clinical services using human activity data. Such services could supplement the shortage of staff in primary healthcare centers thereby reducing the burden on healthcare service providers. The enormous amount of data created from such services could also be utilized for planning future therapies by studying recovery cycles of existing patients. We provide a prototype based on our architecture and discuss its salient features. We also provide use cases of our system in personalized and home based healthcare services. We propose an International Telecommunication Union based standardization (ITU-T) for our design and discuss future directions in wearable IoT.

Leveraging Cloud Computing to Improve Storage Durability, Availability, and Cost for MER Maestro

NASA Technical Reports Server (NTRS)

Chang, George W.; Powell, Mark W.; Callas, John L.; Torres, Recaredo J.; Shams, Khawaja S.

2012-01-01

The Maestro for MER (Mars Exploration Rover) software is the premiere operation and activity planning software for the Mars rovers, and it is required to deliver all of the processed image products to scientists on demand. These data span multiple storage arrays sized at 2 TB, and a backup scheme ensures data is not lost. In a catastrophe, these data would currently recover at 20 GB/hour, taking several days for a restoration. A seamless solution provides access to highly durable, highly available, scalable, and cost-effective storage capabilities. This approach also employs a novel technique that enables storage of the majority of data on the cloud and some data locally. This feature is used to store the most recent data locally in order to guarantee utmost reliability in case of an outage or disconnect from the Internet. This also obviates any changes to the software that generates the most recent data set as it still has the same interface to the file system as it did before updates

Towards Transparent Throughput Elasticity for IaaS Cloud Storage: Exploring the Benefits of Adaptive Block-Level Caching

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

Nicolae, Bogdan; Riteau, Pierre; Keahey, Kate

Storage elasticity on IaaS clouds is a crucial feature in the age of data-intensive computing, especially when considering fluctuations of I/O throughput. This paper provides a transparent solution that automatically boosts I/O bandwidth during peaks for underlying virtual disks, effectively avoiding over-provisioning without performance loss. The authors' proposal relies on the idea of leveraging short-lived virtual disks of better performance characteristics (and thus more expensive) to act during peaks as a caching layer for the persistent virtual disks where the application data is stored. Furthermore, they introduce a performance and cost prediction methodology that can be used both independently tomore » estimate in advance what trade-off between performance and cost is possible, as well as an optimization technique that enables better cache size selection to meet the desired performance level with minimal cost. The authors demonstrate the benefits of their proposal both for microbenchmarks and for two real-life applications using large-scale experiments.« less

ASDC Advances in the Utilization of Microservices and Hybrid Cloud Environments

NASA Astrophysics Data System (ADS)

Baskin, W. E.; Herbert, A.; Mazaika, A.; Walter, J.

2017-12-01

The Atmospheric Science Data Center (ASDC) is transitioning many of its software tools and applications to standalone microservices deployable in a hybrid cloud, offering benefits such as scalability and efficient environment management. This presentation features several projects the ASDC staff have implemented leveraging the OpenShift Container Application Platform and OpenStack Hybrid Cloud Environment focusing on key tools and techniques applied to: Earth Science data processing Spatial-Temporal metadata generation, validation, repair, and curation Archived Data discovery, visualization, and access

Advancing computational methods for calibration of the Soil and Water Assessment Tool (SWAT): Application for modeling climate change impacts on water resources in the Upper Neuse Watershed of North Carolina

NASA Astrophysics Data System (ADS)

Ercan, Mehmet Bulent

Watershed-scale hydrologic models are used for a variety of applications from flood prediction, to drought analysis, to water quality assessments. A particular challenge in applying these models is calibration of the model parameters, many of which are difficult to measure at the watershed-scale. A primary goal of this dissertation is to contribute new computational methods and tools for calibration of watershed-scale hydrologic models and the Soil and Water Assessment Tool (SWAT) model, in particular. SWAT is a physically-based, watershed-scale hydrologic model developed to predict the impact of land management practices on water quality and quantity. The dissertation follows a manuscript format meaning it is comprised of three separate but interrelated research studies. The first two research studies focus on SWAT model calibration, and the third research study presents an application of the new calibration methods and tools to study climate change impacts on water resources in the Upper Neuse Watershed of North Carolina using SWAT. The objective of the first two studies is to overcome computational challenges associated with calibration of SWAT models. The first study evaluates a parallel SWAT calibration tool built using the Windows Azure cloud environment and a parallel version of the Dynamically Dimensioned Search (DDS) calibration method modified to run in Azure. The calibration tool was tested for six model scenarios constructed using three watersheds of increasing size (the Eno, Upper Neuse, and Neuse) for both a 2 year and 10 year simulation duration. Leveraging the cloud as an on demand computing resource allowed for a significantly reduced calibration time such that calibration of the Neuse watershed went from taking 207 hours on a personal computer to only 3.4 hours using 256 cores in the Azure cloud. The second study aims at increasing SWAT model calibration efficiency by creating an open source, multi-objective calibration tool using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). This tool was demonstrated through an application for the Upper Neuse Watershed in North Carolina, USA. The objective functions used for the calibration were Nash-Sutcliffe (E) and Percent Bias (PB), and the objective sites were the Flat, Little, and Eno watershed outlets. The results show that the use of multi-objective calibration algorithms for SWAT calibration improved model performance especially in terms of minimizing PB compared to the single objective model calibration. The third study builds upon the first two studies by leveraging the new calibration methods and tools to study future climate impacts on the Upper Neuse watershed. Statistically downscaled outputs from eight Global Circulation Models (GCMs) were used for both low and high emission scenarios to drive a well calibrated SWAT model of the Upper Neuse watershed. The objective of the study was to understand the potential hydrologic response of the watershed, which serves as a public water supply for the growing Research Triangle Park region of North Carolina, under projected climate change scenarios. The future climate change scenarios, in general, indicate an increase in precipitation and temperature for the watershed in coming decades. The SWAT simulations using the future climate scenarios, in general, suggest an increase in soil water and water yield, and a decrease in evapotranspiration within the Upper Neuse watershed. In summary, this dissertation advances the field of watershed-scale hydrologic modeling by (i) providing some of the first work to apply cloud computing for the computationally-demanding task of model calibration; (ii) providing a new, open source library that can be used by SWAT modelers to perform multi-objective calibration of their models; and (iii) advancing understanding of climate change impacts on water resources for an important watershed in the Research Triangle Park region of North Carolina. The third study leveraged the methodological advances presented in the first two studies. Therefore, the dissertation contains three independent by interrelated studies that collectively advance the field of watershed-scale hydrologic modeling and analysis.

Collaborative WorkBench (cwb): Enabling Experiment Execution, Analysis and Visualization with Increased Scientific Productivity

NASA Astrophysics Data System (ADS)

Maskey, Manil; Ramachandran, Rahul; Kuo, Kwo-Sen

2015-04-01

The Collaborative WorkBench (CWB) has been successfully developed to support collaborative science algorithm development. It incorporates many features that enable and enhance science collaboration, including the support for both asynchronous and synchronous modes of interactions in collaborations. With the former, members in a team can share a full range of research artifacts, e.g. data, code, visualizations, and even virtual machine images. With the latter, they can engage in dynamic interactions such as notification, instant messaging, file exchange, and, most notably, collaborative programming. CWB also implements behind-the-scene provenance capture as well as version control to relieve scientists of these chores. Furthermore, it has achieved a seamless integration between researchers' local compute environments and those of the Cloud. CWB has also been successfully extended to support instrument verification and validation. Adopted by almost every researcher, the current practice of downloading data to local compute resources for analysis results in much duplication and inefficiency. CWB leverages Cloud infrastructure to provide a central location for data used by an entire science team, thereby eliminating much of this duplication and waste. Furthermore, use of CWB in concert with this same Cloud infrastructure enables co-located analysis with data where opportunities of data-parallelism can be better exploited, thereby further improving efficiency. With its collaboration-enabling features apposite to steps throughout the scientific process, we expect CWB to fundamentally transform research collaboration and realize maximum science productivity.

Technical Challenges and Opportunities of Centralizing Space Science Mission Operations (SSMO) at NASA Goddard Space Flight Center

NASA Technical Reports Server (NTRS)

Ido, Haisam; Burns, Rich

2015-01-01

The NASA Goddard Space Science Mission Operations project (SSMO) is performing a technical cost-benefit analysis for centralizing and consolidating operations of a diverse set of missions into a unified and integrated technical infrastructure. The presentation will focus on the notion of normalizing spacecraft operations processes, workflows, and tools. It will also show the processes of creating a standardized open architecture, creating common security models and implementations, interfaces, services, automations, notifications, alerts, logging, publish, subscribe and middleware capabilities. The presentation will also discuss how to leverage traditional capabilities, along with virtualization, cloud computing services, control groups and containers, and possibly Big Data concepts.

Identifying opportune landing sites in degraded visual environments with terrain and cultural databases

NASA Astrophysics Data System (ADS)

Moody, Marc; Fisher, Robert; Little, J. Kristin

2014-06-01

Boeing has developed a degraded visual environment navigational aid that is flying on the Boeing AH-6 light attack helicopter. The navigational aid is a two dimensional software digital map underlay generated by the Boeing™ Geospatial Embedded Mapping Software (GEMS) and fully integrated with the operational flight program. The page format on the aircraft's multi function displays (MFD) is termed the Approach page. The existing work utilizes Digital Terrain Elevation Data (DTED) and OpenGL ES 2.0 graphics capabilities to compute the pertinent graphics underlay entirely on the graphics processor unit (GPU) within the AH-6 mission computer. The next release will incorporate cultural databases containing Digital Vertical Obstructions (DVO) to warn the crew of towers, buildings, and power lines when choosing an opportune landing site. Future IRAD will include Light Detection and Ranging (LIDAR) point cloud generating sensors to provide 2D and 3D synthetic vision on the final approach to the landing zone. Collision detection with respect to terrain, cultural, and point cloud datasets may be used to further augment the crew warning system. The techniques for creating the digital map underlay leverage the GPU almost entirely, making this solution viable on most embedded mission computing systems with an OpenGL ES 2.0 capable GPU. This paper focuses on the AH-6 crew interface process for determining a landing zone and flying the aircraft to it.

Cloud Computing Applications in Support of Earth Science Activities at Marshall Space Flight Center

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Limaye, Ashutosh S.; Srikishen, Jayanthi

2011-01-01

Currently, the NASA Nebula Cloud Computing Platform is available to Agency personnel in a pre-release status as the system undergoes a formal operational readiness review. Over the past year, two projects within the Earth Science Office at NASA Marshall Space Flight Center have been investigating the performance and value of Nebula s "Infrastructure as a Service", or "IaaS" concept and applying cloud computing concepts to advance their respective mission goals. The Short-term Prediction Research and Transition (SPoRT) Center focuses on the transition of unique NASA satellite observations and weather forecasting capabilities for use within the operational forecasting community through partnerships with NOAA s National Weather Service (NWS). SPoRT has evaluated the performance of the Weather Research and Forecasting (WRF) model on virtual machines deployed within Nebula and used Nebula instances to simulate local forecasts in support of regional forecast studies of interest to select NWS forecast offices. In addition to weather forecasting applications, rapidly deployable Nebula virtual machines have supported the processing of high resolution NASA satellite imagery to support disaster assessment following the historic severe weather and tornado outbreak of April 27, 2011. Other modeling and satellite analysis activities are underway in support of NASA s SERVIR program, which integrates satellite observations, ground-based data and forecast models to monitor environmental change and improve disaster response in Central America, the Caribbean, Africa, and the Himalayas. Leveraging SPoRT s experience, SERVIR is working to establish a real-time weather forecasting model for Central America. Other modeling efforts include hydrologic forecasts for Kenya, driven by NASA satellite observations and reanalysis data sets provided by the broader meteorological community. Forecast modeling efforts are supplemented by short-term forecasts of convective initiation, determined by geostationary satellite observations processed on virtual machines powered by Nebula.

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.

A hybrid computational strategy to address WGS variant analysis in >5000 samples.

PubMed

Huang, Zhuoyi; Rustagi, Navin; Veeraraghavan, Narayanan; Carroll, Andrew; Gibbs, Richard; Boerwinkle, Eric; Venkata, Manjunath Gorentla; Yu, Fuli

2016-09-10

The decreasing costs of sequencing are driving the need for cost effective and real time variant calling of whole genome sequencing data. The scale of these projects are far beyond the capacity of typical computing resources available with most research labs. Other infrastructures like the cloud AWS environment and supercomputers also have limitations due to which large scale joint variant calling becomes infeasible, and infrastructure specific variant calling strategies either fail to scale up to large datasets or abandon joint calling strategies. We present a high throughput framework including multiple variant callers for single nucleotide variant (SNV) calling, which leverages hybrid computing infrastructure consisting of cloud AWS, supercomputers and local high performance computing infrastructures. We present a novel binning approach for large scale joint variant calling and imputation which can scale up to over 10,000 samples while producing SNV callsets with high sensitivity and specificity. As a proof of principle, we present results of analysis on Cohorts for Heart And Aging Research in Genomic Epidemiology (CHARGE) WGS freeze 3 dataset in which joint calling, imputation and phasing of over 5300 whole genome samples was produced in under 6 weeks using four state-of-the-art callers. The callers used were SNPTools, GATK-HaplotypeCaller, GATK-UnifiedGenotyper and GotCloud. We used Amazon AWS, a 4000-core in-house cluster at Baylor College of Medicine, IBM power PC Blue BioU at Rice and Rhea at Oak Ridge National Laboratory (ORNL) for the computation. AWS was used for joint calling of 180 TB of BAM files, and ORNL and Rice supercomputers were used for the imputation and phasing step. All other steps were carried out on the local compute cluster. The entire operation used 5.2 million core hours and only transferred a total of 6 TB of data across the platforms. Even with increasing sizes of whole genome datasets, ensemble joint calling of SNVs for low coverage data can be accomplished in a scalable, cost effective and fast manner by using heterogeneous computing platforms without compromising on the quality of variants.

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.

Analyzing and leveraging self-similarity for variable resolution atmospheric models

NASA Astrophysics Data System (ADS)

O'Brien, Travis; Collins, William

2015-04-01

Variable resolution modeling techniques are rapidly becoming a popular strategy for achieving high resolution in a global atmospheric models without the computational cost of global high resolution. However, recent studies have demonstrated a variety of resolution-dependent, and seemingly artificial, features. We argue that the scaling properties of the atmosphere are key to understanding how the statistics of an atmospheric model should change with resolution. We provide two such examples. In the first example we show that the scaling properties of the cloud number distribution define how the ratio of resolved to unresolved clouds should increase with resolution. We show that the loss of resolved clouds, in the high resolution region of variable resolution simulations, with the Community Atmosphere Model version 4 (CAM4) is an artifact of the model's treatment of condensed water (this artifact is significantly reduced in CAM5). In the second example we show that the scaling properties of the horizontal velocity field, combined with the incompressibility assumption, necessarily result in an intensification of vertical mass flux as resolution increases. We show that such an increase is present in a wide variety of models, including CAM and the regional climate models of the ENSEMBLES intercomparision. We present theoretical arguments linking this increase to the intensification of precipitation with increasing resolution.

Multi-sensor measurements of mixed-phase clouds above Greenland

NASA Astrophysics Data System (ADS)

Stillwell, Robert A.; Shupe, Matthew D.; Thayer, Jeffrey P.; Neely, Ryan R.; Turner, David D.

2018-04-01

Liquid-only and mixed-phase clouds in the Arctic strongly affect the regional surface energy and ice mass budgets, yet much remains unknown about the nature of these clouds due to the lack of intensive measurements. Lidar measurements of these clouds are challenged by very large signal dynamic range, which makes even seemingly simple tasks, such as thermodynamic phase classification, difficult. This work focuses on a set of measurements made by the Clouds Aerosol Polarization and Backscatter Lidar at Summit, Greenland and its retrieval algorithms, which use both analog and photon counting as well as orthogonal and non-orthogonal polarization retrievals to extend dynamic range and improve overall measurement quality and quantity. Presented here is an algorithm for cloud parameter retrievals that leverages enhanced dynamic range retrievals to classify mixed-phase clouds. This best guess retrieval is compared to co-located instruments for validation.

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

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.

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.

From One Pixel to One Earth: Building a Living Atlas in the Cloud to Analyze and Monitor Global Patterns

NASA Astrophysics Data System (ADS)

Moody, D.; Brumby, S. P.; Chartrand, R.; Franco, E.; Keisler, R.; Kelton, T.; Kontgis, C.; Mathis, M.; Raleigh, D.; Rudelis, X.; Skillman, S.; Warren, M. S.; Longbotham, N.

2016-12-01

The recent computing performance revolution has driven improvements in sensor, communication, and storage technology. Historical, multi-decadal remote sensing datasets at the petabyte scale are now available in commercial clouds, with new satellite constellations generating petabytes per year of high-resolution imagery with daily global coverage. Cloud computing and storage, combined with recent advances in machine learning and open software, are enabling understanding of the world at an unprecedented scale and detail. We have assembled all available satellite imagery from the USGS Landsat, NASA MODIS, and ESA Sentinel programs, as well as commercial PlanetScope and RapidEye imagery, and have analyzed over 2.8 quadrillion multispectral pixels. We leveraged the commercial cloud to generate a tiled, spatio-temporal mosaic of the Earth for fast iteration and development of new algorithms combining analysis techniques from remote sensing, machine learning, and scalable compute infrastructure. Our data platform enables processing at petabytes per day rates using multi-source data to produce calibrated, georeferenced imagery stacks at desired points in time and space that can be used for pixel level or global scale analysis. We demonstrate our data platform capability by using the European Space Agency's (ESA) published 2006 and 2009 GlobCover 20+ category label maps to train and test a Land Cover Land Use (LCLU) classifier, and generate current self-consistent LCLU maps in Brazil. We train a standard classifier on 2006 GlobCover categories using temporal imagery stacks, and we validate our results on co-registered 2009 Globcover LCLU maps and 2009 imagery. We then extend the derived LCLU model to current imagery stacks to generate an updated, in-season label map. Changes in LCLU labels can now be seamlessly monitored for a given location across the years in order to track, for example, cropland expansion, forest growth, and urban developments. An example of change monitoring is illustrated in the included figure showing rainfed cropland change in the Mato Grosso region of Brazil between 2006 and 2009.

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 .

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

Slattery, Stuart R.

In this study we analyze and extend mesh-free algorithms for three-dimensional data transfer problems in partitioned multiphysics simulations. We first provide a direct comparison between a mesh-based weighted residual method using the common-refinement scheme and two mesh-free algorithms leveraging compactly supported radial basis functions: one using a spline interpolation and one using a moving least square reconstruction. Through the comparison we assess both the conservation and accuracy of the data transfer obtained from each of the methods. We do so for a varying set of geometries with and without curvature and sharp features and for functions with and without smoothnessmore » and with varying gradients. Our results show that the mesh-based and mesh-free algorithms are complementary with cases where each was demonstrated to perform better than the other. We then focus on the mesh-free methods by developing a set of algorithms to parallelize them based on sparse linear algebra techniques. This includes a discussion of fast parallel radius searching in point clouds and restructuring the interpolation algorithms to leverage data structures and linear algebra services designed for large distributed computing environments. The scalability of our new algorithms is demonstrated on a leadership class computing facility using a set of basic scaling studies. Finally, these scaling studies show that for problems with reasonable load balance, our new algorithms for both spline interpolation and moving least square reconstruction demonstrate both strong and weak scalability using more than 100,000 MPI processes with billions of degrees of freedom in the data transfer operation.« less

KAGLVis - On-line 3D Visualisation of Earth-observing-satellite Data

NASA Astrophysics Data System (ADS)

Szuba, Marek; Ameri, Parinaz; Grabowski, Udo; Maatouki, Ahmad; Meyer, Jörg

2015-04-01

One of the goals of the Large-Scale Data Management and Analysis project is to provide a high-performance framework facilitating management of data acquired by Earth-observing satellites such as Envisat. On the client-facing facet of this framework, we strive to provide visualisation and basic analysis tool which could be used by scientists with minimal to no knowledge of the underlying infrastructure. Our tool, KAGLVis, is a JavaScript client-server Web application which leverages modern Web technologies to provide three-dimensional visualisation of satellite observables on a wide range of client systems. It takes advantage of the WebGL API to employ locally available GPU power for 3D rendering; this approach has been demonstrated to perform well even on relatively weak hardware such as integrated graphics chipsets found in modern laptop computers and with some user-interface tuning could even be usable on embedded devices such as smartphones or tablets. Data is fetched from the database back-end using a ReST API and cached locally, both in memory and using HTML5 Web Storage, to minimise network use. Computations, calculation of cloud altitude from cloud-index measurements for instance, can depending on configuration be performed on either the client or the server side. Keywords: satellite data, Envisat, visualisation, 3D graphics, Web application, WebGL, MEAN stack.

Challenges for data storage in medical imaging research.

PubMed

Langer, Steve G

2011-04-01

Researchers in medical imaging have multiple challenges for storing, indexing, maintaining viability, and sharing their data. Addressing all these concerns requires a constellation of tools, but not all of them need to be local to the site. In particular, the data storage challenges faced by researchers can begin to require professional information technology skills. With limited human resources and funds, the medical imaging researcher may be better served with an outsourcing strategy for some management aspects. This paper outlines an approach to manage the main objectives faced by medical imaging scientists whose work includes processing and data mining on non-standard file formats, and relating those files to the their DICOM standard descendents. The capacity of the approach scales as the researcher's need grows by leveraging the on-demand provisioning ability of cloud computing.

Clinician accessible tools for GUI computational models of transcranial electrical stimulation: BONSAI and SPHERES.

PubMed

Truong, Dennis Q; Hüber, Mathias; Xie, Xihe; Datta, Abhishek; Rahman, Asif; Parra, Lucas C; Dmochowski, Jacek P; Bikson, Marom

2014-01-01

Computational models of brain current flow during transcranial electrical stimulation (tES), including transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), are increasingly used to understand and optimize clinical trials. We propose that broad dissemination requires a simple graphical user interface (GUI) software that allows users to explore and design montages in real-time, based on their own clinical/experimental experience and objectives. We introduce two complimentary open-source platforms for this purpose: BONSAI and SPHERES. BONSAI is a web (cloud) based application (available at neuralengr.com/bonsai) that can be accessed through any flash-supported browser interface. SPHERES (available at neuralengr.com/spheres) is a stand-alone GUI application that allow consideration of arbitrary montages on a concentric sphere model by leveraging an analytical solution. These open-source tES modeling platforms are designed go be upgraded and enhanced. Trade-offs between open-access approaches that balance ease of access, speed, and flexibility are discussed. Copyright © 2014 Elsevier Inc. All rights reserved.

EDGE COMPUTING AND CONTEXTUAL INFORMATION FOR THE INTERNET OF THINGS SENSORS

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

Klein, Levente

Interpreting sensor data require knowledge about sensor placement and the surrounding environment. For a single sensor measurement, it is easy to document the context by visual observation, however for millions of sensors reporting data back to a server, the contextual information needs to be automatically extracted from either data analysis or leveraging complimentary data sources. Data layers that overlap spatially or temporally with sensor locations, can be used to extract the context and to validate the measurement. To minimize the amount of data transmitted through the internet, while preserving signal information content, two methods are explored; computation at the edgemore » and compressed sensing. We validate the above methods on wind and chemical sensor data (1) eliminate redundant measurement from wind sensors and (2) extract peak value of a chemical sensor measuring a methane plume. We present a general cloud based framework to validate sensor data based on statistical and physical modeling and contextual data extracted from geospatial data.« less

Data Mining as a Service (DMaaS)

NASA Astrophysics Data System (ADS)

Tejedor, E.; Piparo, D.; Mascetti, L.; Moscicki, J.; Lamanna, M.; Mato, P.

2016-10-01

Data Mining as a Service (DMaaS) is a software and computing infrastructure that allows interactive mining of scientific data in the cloud. It allows users to run advanced data analyses by leveraging the widely adopted Jupyter notebook interface. Furthermore, the system makes it easier to share results and scientific code, access scientific software, produce tutorials and demonstrations as well as preserve the analyses of scientists. This paper describes how a first pilot of the DMaaS service is being deployed at CERN, starting from the notebook interface that has been fully integrated with the ROOT analysis framework, in order to provide all the tools for scientists to run their analyses. Additionally, we characterise the service backend, which combines a set of IT services such as user authentication, virtual computing infrastructure, mass storage, file synchronisation, development portals or batch systems. The added value acquired by the combination of the aforementioned categories of services is discussed, focusing on the opportunities offered by the CERNBox synchronisation service and its massive storage backend, EOS.

Addressing security, collaboration, and usability with tactical edge mobile devices and strategic cloud-based systems

NASA Astrophysics Data System (ADS)

Graham, Christopher J.

2012-05-01

Success in the future battle space is increasingly dependent on rapid access to the right information. Faced with a shrinking budget, the Government has a mandate to improve intelligence productivity, quality, and reliability. To achieve increased ISR effectiveness, leverage of tactical edge mobile devices via integration with strategic cloud-based infrastructure is the single, most likely candidate area for dramatic near-term impact. This paper discusses security, collaboration, and usability components of this evolving space. These three paramount tenets outlined below, embody how mission information is exchanged securely, efficiently, with social media cooperativeness. Tenet 1: Complete security, privacy, and data integrity, must be ensured within the net-centric battle space. This paper discusses data security on a mobile device, data at rest on a cloud-based system, authorization and access control, and securing data transport between entities. Tenet 2: Lack of collaborative information sharing and content reliability jeopardizes mission objectives and limits the end user capability. This paper discusses cooperative pairing of mobile devices and cloud systems, enabling social media style interaction via tagging, meta-data refinement, and sharing of pertinent data. Tenet 3: Fielded mobile solutions must address usability and complexity. Simplicity is a powerful paradigm on mobile platforms, where complex applications are not utilized, and simple, yet powerful, applications flourish. This paper discusses strategies for ensuring mobile applications are streamlined and usable at the tactical edge through focused features sets, leveraging the power of the back-end cloud, minimization of differing HMI concepts, and directed end-user feedback.teInput=

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

PubMed Central

Joyce, Blake L.; Haug-Baltzell, Asher K.; Hulvey, Jonathan P.; McCarthy, Fiona; Devisetty, Upendra Kumar; Lyons, Eric

2017-01-01

This workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative transcriptomics. It also serves as a primer for biologists to develop data scientist computational skills, e.g. executing bash commands, visualization and management of large data sets. All command line code and further explanations of each command or step can be found on the wiki (https://wiki.cyverse.org/wiki/x/dgGtAQ). The Discovery Environment and Atmosphere platforms are connected together through the CyVerse Data Store. As such, once the initial raw sequencing data has been uploaded there is no more need to transfer large data files over an Internet connection, minimizing the amount of time needed to conduct analyses. This protocol is designed to analyze only two experimental treatments or conditions. Differential gene expression analysis is conducted through pairwise comparisons, and will not be suitable to test multiple factors. This workflow is also designed to be manual rather than automated. Each step must be executed and investigated by the user, yielding a better understanding of data and analytical outputs, and therefore better results for the user. Once complete, this protocol will yield de novo assembled transcriptome(s) for underserved (non-model) organisms without the need to map to previously assembled reference genomes (which are usually not available in underserved organism). These de novo transcriptomes are further used in pairwise differential gene expression analysis to investigate genes differing between two experimental conditions. Differentially expressed genes are then functionally annotated to understand the genetic response organisms have to experimental conditions. In total, the data derived from this protocol is used to test hypotheses about biological responses of underserved organisms. PMID:28518075

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.

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

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.

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

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.

Cost Optimal Elastic Auto-Scaling in Cloud Infrastructure

NASA Astrophysics Data System (ADS)

Mukhopadhyay, S.; Sidhanta, S.; Ganguly, S.; Nemani, R. R.

2014-12-01

Today, elastic scaling is critical part of leveraging cloud. Elastic scaling refers to adding resources only when it is needed and deleting resources when not in use. Elastic scaling ensures compute/server resources are not over provisioned. Today, Amazon and Windows Azure are the only two platform provider that allow auto-scaling of cloud resources where servers are automatically added and deleted. However, these solution falls short of following key features: A) Requires explicit policy definition such server load and therefore lacks any predictive intelligence to make optimal decision; B) Does not decide on the right size of resource and thereby does not result in cost optimal resource pool. In a typical cloud deployment model, we consider two types of application scenario: A. Batch processing jobs → Hadoop/Big Data case B. Transactional applications → Any application that process continuous transactions (Requests/response) In reference of classical queuing model, we are trying to model a scenario where servers have a price and capacity (size) and system can add delete servers to maintain a certain queue length. Classical queueing models applies to scenario where number of servers are constant. So we cannot apply stationary system analysis in this case. We investigate the following questions 1. Can we define Job queue and use the metric to define such a queue to predict the resource requirement in a quasi-stationary way? Can we map that into an optimal sizing problem? 2. Do we need to get into a level of load (CPU/Data) on server level to characterize the size requirement? How do we learn that based on Job type?

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.

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.)

IBM Cloud Computing Powering a Smarter Planet

NASA Astrophysics Data System (ADS)

Zhu, Jinzy; Fang, Xing; Guo, Zhe; Niu, Meng Hua; Cao, Fan; Yue, Shuang; Liu, Qin Yu

With increasing need for intelligent systems supporting the world's businesses, Cloud Computing has emerged as a dominant trend to provide a dynamic infrastructure to make such intelligence possible. The article introduced how to build a smarter planet with cloud computing technology. First, it introduced why we need cloud, and the evolution of cloud technology. Secondly, it analyzed the value of cloud computing and how to apply cloud technology. Finally, it predicted the future of cloud in the smarter planet.

Cloud Computing Security Issue: Survey

NASA Astrophysics Data System (ADS)

Kamal, Shailza; Kaur, Rajpreet

2011-12-01

Cloud computing is the growing field in IT industry since 2007 proposed by IBM. Another company like Google, Amazon, and Microsoft provides further products to cloud computing. The cloud computing is the internet based computing that shared recourses, information on demand. It provides the services like SaaS, IaaS and PaaS. The services and recourses are shared by virtualization that run multiple operation applications on cloud computing. This discussion gives the survey on the challenges on security issues during cloud computing and describes some standards and protocols that presents how security can be managed.

T-Check in System-of-Systems Technologies: Cloud Computing

DTIC Science & Technology

2010-09-01

T-Check in System-of-Systems Technologies: Cloud Computing Harrison D. Strowd Grace A. Lewis September 2010 TECHNICAL NOTE CMU/SEI-2010... Cloud Computing 1 1.2 Types of Cloud Computing 2 1.3 Drivers and Barriers to Cloud Computing Adoption 5 2 Using the T-Check Method 7 2.1 T-Check...Hypothesis 3 25 3.4.2 Deployment View of the Solution for Testing Hypothesis 3 27 3.5 Selecting Cloud Computing Providers 30 3.6 Implementing the T-Check

Information Security: Governmentwide Guidance Needed to Assist Agencies in Implementing Cloud Computing

DTIC Science & Technology

2010-07-01

Cloud computing , an emerging form of computing in which users have access to scalable, on-demand capabilities that are provided through Internet... cloud computing , (2) the information security implications of using cloud computing services in the Federal Government, and (3) federal guidance and...efforts to address information security when using cloud computing . The complete report is titled Information Security: Federal Guidance Needed to

Risk in the Clouds?: Security Issues Facing Government Use of Cloud Computing

NASA Astrophysics Data System (ADS)

Wyld, David C.

Cloud computing is poised to become one of the most important and fundamental shifts in how computing is consumed and used. Forecasts show that government will play a lead role in adopting cloud computing - for data storage, applications, and processing power, as IT executives seek to maximize their returns on limited procurement budgets in these challenging economic times. After an overview of the cloud computing concept, this article explores the security issues facing public sector use of cloud computing and looks to the risk and benefits of shifting to cloud-based models. It concludes with an analysis of the challenges that lie ahead for government use of cloud resources.

A Review Study on Cloud Computing Issues

NASA Astrophysics Data System (ADS)

Kanaan Kadhim, Qusay; Yusof, Robiah; Sadeq Mahdi, Hamid; Al-shami, Sayed Samer Ali; Rahayu Selamat, Siti

2018-05-01

Cloud computing is the most promising current implementation of utility computing in the business world, because it provides some key features over classic utility computing, such as elasticity to allow clients dynamically scale-up and scale-down the resources in execution time. Nevertheless, cloud computing is still in its premature stage and experiences lack of standardization. The security issues are the main challenges to cloud computing adoption. Thus, critical industries such as government organizations (ministries) are reluctant to trust cloud computing due to the fear of losing their sensitive data, as it resides on the cloud with no knowledge of data location and lack of transparency of Cloud Service Providers (CSPs) mechanisms used to secure their data and applications which have created a barrier against adopting this agile computing paradigm. This study aims to review and classify the issues that surround the implementation of cloud computing which a hot area that needs to be addressed by future research.

High Fidelity Modeling of Turbulent Mixing and Chemical Kinetics Interactions in a Post-Detonation Flow Field

NASA Astrophysics Data System (ADS)

Sinha, Neeraj; Zambon, Andrea; Ott, James; Demagistris, Michael

2015-06-01

Driven by the continuing rapid advances in high-performance computing, multi-dimensional high-fidelity modeling is an increasingly reliable predictive tool capable of providing valuable physical insight into complex post-detonation reacting flow fields. Utilizing a series of test cases featuring blast waves interacting with combustible dispersed clouds in a small-scale test setup under well-controlled conditions, the predictive capabilities of a state-of-the-art code are demonstrated and validated. Leveraging physics-based, first principle models and solving large system of equations on highly-resolved grids, the combined effects of finite-rate/multi-phase chemical processes (including thermal ignition), turbulent mixing and shock interactions are captured across the spectrum of relevant time-scales and length scales. Since many scales of motion are generated in a post-detonation environment, even if the initial ambient conditions are quiescent, turbulent mixing plays a major role in the fireball afterburning as well as in dispersion, mixing, ignition and burn-out of combustible clouds in its vicinity. Validating these capabilities at the small scale is critical to establish a reliable predictive tool applicable to more complex and large-scale geometries of practical interest.

Facets : a Cloudcompare Plugin to Extract Geological Planes from Unstructured 3d Point Clouds

NASA Astrophysics Data System (ADS)

Dewez, T. J. B.; Girardeau-Montaut, D.; Allanic, C.; Rohmer, J.

2016-06-01

Geological planar facets (stratification, fault, joint…) are key features to unravel the tectonic history of rock outcrop or appreciate the stability of a hazardous rock cliff. Measuring their spatial attitude (dip and strike) is generally performed by hand with a compass/clinometer, which is time consuming, requires some degree of censoring (i.e. refusing to measure some features judged unimportant at the time), is not always possible for fractures higher up on the outcrop and is somewhat hazardous. 3D virtual geological outcrop hold the potential to alleviate these issues. Efficiently segmenting massive 3D point clouds into individual planar facets, inside a convenient software environment was lacking. FACETS is a dedicated plugin within CloudCompare v2.6.2 (http://cloudcompare.org/ ) implemented to perform planar facet extraction, calculate their dip and dip direction (i.e. azimuth of steepest decent) and report the extracted data in interactive stereograms. Two algorithms perform the segmentation: Kd-Tree and Fast Marching. Both divide the point cloud into sub-cells, then compute elementary planar objects and aggregate them progressively according to a planeity threshold into polygons. The boundaries of the polygons are adjusted around segmented points with a tension parameter, and the facet polygons can be exported as 3D polygon shapefiles towards third party GIS software or simply as ASCII comma separated files. One of the great features of FACETS is the capability to explore planar objects but also 3D points with normals with the stereogram tool. Poles can be readily displayed, queried and manually segmented interactively. The plugin blends seamlessly into CloudCompare to leverage all its other 3D point cloud manipulation features. A demonstration of the tool is presented to illustrate these different features. While designed for geological applications, FACETS could be more widely applied to any planar objects.

78 FR 54453 - Notice of Public Meeting-Intersection of Cloud Computing and Mobility Forum and Workshop

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-04

...--Intersection of Cloud Computing and Mobility Forum and Workshop AGENCY: National Institute of Standards and.../intersection-of-cloud-and-mobility.cfm . SUPPLEMENTARY INFORMATION: NIST hosted six prior Cloud Computing Forum... interoperability, portability, and security, discuss the Federal Government's experience with cloud computing...

Embracing the Cloud: Six Ways to Look at the Shift to Cloud Computing

ERIC Educational Resources Information Center

Ullman, David F.; Haggerty, Blake

2010-01-01

Cloud computing is the latest paradigm shift for the delivery of IT services. Where previous paradigms (centralized, decentralized, distributed) were based on fairly straightforward approaches to technology and its management, cloud computing is radical in comparison. The literature on cloud computing, however, suffers from many divergent…

The Research of the Parallel Computing Development from the Angle of Cloud Computing

NASA Astrophysics Data System (ADS)

Peng, Zhensheng; Gong, Qingge; Duan, Yanyu; Wang, Yun

2017-10-01

Cloud computing is the development of parallel computing, distributed computing and grid computing. The development of cloud computing makes parallel computing come into people’s lives. Firstly, this paper expounds the concept of cloud computing and introduces two several traditional parallel programming model. Secondly, it analyzes and studies the principles, advantages and disadvantages of OpenMP, MPI and Map Reduce respectively. Finally, it takes MPI, OpenMP models compared to Map Reduce from the angle of cloud computing. The results of this paper are intended to provide a reference for the development of parallel computing.

Genetic Constructor: An Online DNA Design Platform.

PubMed

Bates, Maxwell; Lachoff, Joe; Meech, Duncan; Zulkower, Valentin; Moisy, Anaïs; Luo, Yisha; Tekotte, Hille; Franziska Scheitz, Cornelia Johanna; Khilari, Rupal; Mazzoldi, Florencio; Chandran, Deepak; Groban, Eli

2017-12-15

Genetic Constructor is a cloud Computer Aided Design (CAD) application developed to support synthetic biologists from design intent through DNA fabrication and experiment iteration. The platform allows users to design, manage, and navigate complex DNA constructs and libraries, using a new visual language that focuses on functional parts abstracted from sequence. Features like combinatorial libraries and automated primer design allow the user to separate design from construction by focusing on functional intent, and design constraints aid iterative refinement of designs. A plugin architecture enables contributions from scientists and coders to leverage existing powerful software and connect to DNA foundries. The software is easily accessible and platform agnostic, free for academics, and available in an open-source community edition. Genetic Constructor seeks to democratize DNA design, manufacture, and access to tools and services from the synthetic biology community.

Application of Open Source Technologies for Oceanographic Data Analysis

NASA Astrophysics Data System (ADS)

Huang, T.; Gangl, M.; Quach, N. T.; Wilson, B. D.; Chang, G.; Armstrong, E. M.; Chin, T. M.; Greguska, F.

2015-12-01

NEXUS is a data-intensive analysis solution developed with a new approach for handling science data that enables large-scale data analysis by leveraging open source technologies such as Apache Cassandra, Apache Spark, Apache Solr, and Webification. NEXUS has been selected to provide on-the-fly time-series and histogram generation for the Soil Moisture Active Passive (SMAP) mission for Level 2 and Level 3 Active, Passive, and Active Passive products. It also provides an on-the-fly data subsetting capability. NEXUS is designed to scale horizontally, enabling it to handle massive amounts of data in parallel. It takes a new approach on managing time and geo-referenced array data by dividing data artifacts into chunks and stores them in an industry-standard, horizontally scaled NoSQL database. This approach enables the development of scalable data analysis services that can infuse and leverage the elastic computing infrastructure of the Cloud. It is equipped with a high-performance geospatial and indexed data search solution, coupled with a high-performance data Webification solution free from file I/O bottlenecks, as well as a high-performance, in-memory data analysis engine. In this talk, we will focus on the recently funded AIST 2014 project by using NEXUS as the core for oceanographic anomaly detection service and web portal. We call it, OceanXtremes

Cloud computing basics for librarians.

PubMed

Hoy, Matthew B

2012-01-01

"Cloud computing" is the name for the recent trend of moving software and computing resources to an online, shared-service model. This article briefly defines cloud computing, discusses different models, explores the advantages and disadvantages, and describes some of the ways cloud computing can be used in libraries. Examples of cloud services are included at the end of the article. Copyright © Taylor & Francis Group, LLC

A Novel College Network Resource Management Method using Cloud Computing

NASA Astrophysics Data System (ADS)

Lin, Chen

At present information construction of college mainly has construction of college networks and management information system; there are many problems during the process of information. Cloud computing is development of distributed processing, parallel processing and grid computing, which make data stored on the cloud, make software and services placed in the cloud and build on top of various standards and protocols, you can get it through all kinds of equipments. This article introduces cloud computing and function of cloud computing, then analyzes the exiting problems of college network resource management, the cloud computing technology and methods are applied in the construction of college information sharing platform.

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

Research on Quantum Authentication Methods for the Secure Access Control Among Three Elements of Cloud Computing

NASA Astrophysics Data System (ADS)

Dong, Yumin; Xiao, Shufen; Ma, Hongyang; Chen, Libo

2016-12-01

Cloud computing and big data have become the developing engine of current information technology (IT) as a result of the rapid development of IT. However, security protection has become increasingly important for cloud computing and big data, and has become a problem that must be solved to develop cloud computing. The theft of identity authentication information remains a serious threat to the security of cloud computing. In this process, attackers intrude into cloud computing services through identity authentication information, thereby threatening the security of data from multiple perspectives. Therefore, this study proposes a model for cloud computing protection and management based on quantum authentication, introduces the principle of quantum authentication, and deduces the quantum authentication process. In theory, quantum authentication technology can be applied in cloud computing for security protection. This technology cannot be cloned; thus, it is more secure and reliable than classical methods.

Mesh-free data transfer algorithms for partitioned multiphysics problems: Conservation, accuracy, and parallelism

DOE PAGES

Slattery, Stuart R.

2015-12-02

In this study we analyze and extend mesh-free algorithms for three-dimensional data transfer problems in partitioned multiphysics simulations. We first provide a direct comparison between a mesh-based weighted residual method using the common-refinement scheme and two mesh-free algorithms leveraging compactly supported radial basis functions: one using a spline interpolation and one using a moving least square reconstruction. Through the comparison we assess both the conservation and accuracy of the data transfer obtained from each of the methods. We do so for a varying set of geometries with and without curvature and sharp features and for functions with and without smoothnessmore » and with varying gradients. Our results show that the mesh-based and mesh-free algorithms are complementary with cases where each was demonstrated to perform better than the other. We then focus on the mesh-free methods by developing a set of algorithms to parallelize them based on sparse linear algebra techniques. This includes a discussion of fast parallel radius searching in point clouds and restructuring the interpolation algorithms to leverage data structures and linear algebra services designed for large distributed computing environments. The scalability of our new algorithms is demonstrated on a leadership class computing facility using a set of basic scaling studies. Finally, these scaling studies show that for problems with reasonable load balance, our new algorithms for both spline interpolation and moving least square reconstruction demonstrate both strong and weak scalability using more than 100,000 MPI processes with billions of degrees of freedom in the data transfer operation.« less

Performance Analysis of Cloud Computing Architectures Using Discrete Event Simulation

NASA Technical Reports Server (NTRS)

Stocker, John C.; Golomb, Andrew M.

2011-01-01

Cloud computing offers the economic benefit of on-demand resource allocation to meet changing enterprise computing needs. However, the flexibility of cloud computing is disadvantaged when compared to traditional hosting in providing predictable application and service performance. Cloud computing relies on resource scheduling in a virtualized network-centric server environment, which makes static performance analysis infeasible. We developed a discrete event simulation model to evaluate the overall effectiveness of organizations in executing their workflow in traditional and cloud computing architectures. The two part model framework characterizes both the demand using a probability distribution for each type of service request as well as enterprise computing resource constraints. Our simulations provide quantitative analysis to design and provision computing architectures that maximize overall mission effectiveness. We share our analysis of key resource constraints in cloud computing architectures and findings on the appropriateness of cloud computing in various applications.

Establishing a Cloud Computing Success Model for Hospitals in Taiwan.

PubMed

Lian, Jiunn-Woei

2017-01-01

The purpose of this study is to understand the critical quality-related factors that affect cloud computing success of hospitals in Taiwan. In this study, private cloud computing is the major research target. The chief information officers participated in a questionnaire survey. The results indicate that the integration of trust into the information systems success model will have acceptable explanatory power to understand cloud computing success in the hospital. Moreover, information quality and system quality directly affect cloud computing satisfaction, whereas service quality indirectly affects the satisfaction through trust. In other words, trust serves as the mediator between service quality and satisfaction. This cloud computing success model will help hospitals evaluate or achieve success after adopting private cloud computing health care services.

Establishing a Cloud Computing Success Model for Hospitals in Taiwan

PubMed Central

Lian, Jiunn-Woei

2017-01-01

The purpose of this study is to understand the critical quality-related factors that affect cloud computing success of hospitals in Taiwan. In this study, private cloud computing is the major research target. The chief information officers participated in a questionnaire survey. The results indicate that the integration of trust into the information systems success model will have acceptable explanatory power to understand cloud computing success in the hospital. Moreover, information quality and system quality directly affect cloud computing satisfaction, whereas service quality indirectly affects the satisfaction through trust. In other words, trust serves as the mediator between service quality and satisfaction. This cloud computing success model will help hospitals evaluate or achieve success after adopting private cloud computing health care services. PMID:28112020

Implementation of cloud computing in higher education

NASA Astrophysics Data System (ADS)

Asniar; Budiawan, R.

2016-04-01

Cloud computing research is a new trend in distributed computing, where people have developed service and SOA (Service Oriented Architecture) based application. This technology is very useful to be implemented, especially for higher education. This research is studied the need and feasibility for the suitability of cloud computing in higher education then propose the model of cloud computing service in higher education in Indonesia that can be implemented in order to support academic activities. Literature study is used as the research methodology to get a proposed model of cloud computing in higher education. Finally, SaaS and IaaS are cloud computing service that proposed to be implemented in higher education in Indonesia and cloud hybrid is the service model that can be recommended.

Research on Key Technologies of Cloud Computing

NASA Astrophysics Data System (ADS)

Zhang, Shufen; Yan, Hongcan; Chen, Xuebin

With the development of multi-core processors, virtualization, distributed storage, broadband Internet and automatic management, a new type of computing mode named cloud computing is produced. It distributes computation task on the resource pool which consists of massive computers, so the application systems can obtain the computing power, the storage space and software service according to its demand. It can concentrate all the computing resources and manage them automatically by the software without intervene. This makes application offers not to annoy for tedious details and more absorbed in his business. It will be advantageous to innovation and reduce cost. It's the ultimate goal of cloud computing to provide calculation, services and applications as a public facility for the public, So that people can use the computer resources just like using water, electricity, gas and telephone. Currently, the understanding of cloud computing is developing and changing constantly, cloud computing still has no unanimous definition. This paper describes three main service forms of cloud computing: SAAS, PAAS, IAAS, compared the definition of cloud computing which is given by Google, Amazon, IBM and other companies, summarized the basic characteristics of cloud computing, and emphasized on the key technologies such as data storage, data management, virtualization and programming model.

The Many Colors and Shapes of Cloud

NASA Astrophysics Data System (ADS)

Yeh, James T.

While many enterprises and business entities are deploying and exploiting Cloud Computing, the academic institutes and researchers are also busy trying to wrestle this beast and put a leash on this possible paradigm changing computing model. Many have argued that Cloud Computing is nothing more than a name change of Utility Computing. Others have argued that Cloud Computing is a revolutionary change of the computing architecture. So it has been difficult to put a boundary of what is in Cloud Computing, and what is not. I assert that it is equally difficult to find a group of people who would agree on even the definition of Cloud Computing. In actuality, may be all that arguments are not necessary, as Clouds have many shapes and colors. In this presentation, the speaker will attempt to illustrate that the shape and the color of the cloud depend very much on the business goals one intends to achieve. It will be a very rich territory for both the businesses to take the advantage of the benefits of Cloud Computing and the academia to integrate the technology research and business research.

ATLAS Cloud R&D

NASA Astrophysics Data System (ADS)

Panitkin, Sergey; Barreiro Megino, Fernando; Caballero Bejar, Jose; Benjamin, Doug; Di Girolamo, Alessandro; Gable, Ian; Hendrix, Val; Hover, John; Kucharczyk, Katarzyna; Medrano Llamas, Ramon; Love, Peter; Ohman, Henrik; Paterson, Michael; Sobie, Randall; Taylor, Ryan; Walker, Rodney; Zaytsev, Alexander; Atlas Collaboration

2014-06-01

The computing model of the ATLAS experiment was designed around the concept of grid computing and, since the start of data taking, this model has proven very successful. However, new cloud computing technologies bring attractive features to improve the operations and elasticity of scientific distributed computing. ATLAS sees grid and cloud computing as complementary technologies that will coexist at different levels of resource abstraction, and two years ago created an R&D working group to investigate the different integration scenarios. The ATLAS Cloud Computing R&D has been able to demonstrate the feasibility of offloading work from grid to cloud sites and, as of today, is able to integrate transparently various cloud resources into the PanDA workload management system. The ATLAS Cloud Computing R&D is operating various PanDA queues on private and public resources and has provided several hundred thousand CPU days to the experiment. As a result, the ATLAS Cloud Computing R&D group has gained a significant insight into the cloud computing landscape and has identified points that still need to be addressed in order to fully utilize this technology. This contribution will explain the cloud integration models that are being evaluated and will discuss ATLAS' learning during the collaboration with leading commercial and academic cloud providers.

The Education Value of Cloud Computing

ERIC Educational Resources Information Center

Katzan, Harry, Jr.

2010-01-01

Cloud computing is a technique for supplying computer facilities and providing access to software via the Internet. Cloud computing represents a contextual shift in how computers are provisioned and accessed. One of the defining characteristics of cloud software service is the transfer of control from the client domain to the service provider.…

Cloud Computing. Technology Briefing. Number 1

ERIC Educational Resources Information Center

Alberta Education, 2013

2013-01-01

Cloud computing is Internet-based computing in which shared resources, software and information are delivered as a service that computers or mobile devices can access on demand. Cloud computing is already used extensively in education. Free or low-cost cloud-based services are used daily by learners and educators to support learning, social…

Can cloud computing benefit health services? - a SWOT analysis.

PubMed

Kuo, Mu-Hsing; Kushniruk, Andre; Borycki, Elizabeth

2011-01-01

In this paper, we discuss cloud computing, the current state of cloud computing in healthcare, and the challenges and opportunities of adopting cloud computing in healthcare. A Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis was used to evaluate the feasibility of adopting this computing model in healthcare. The paper concludes that cloud computing could have huge benefits for healthcare but there are a number of issues that will need to be addressed before its widespread use in healthcare.

State of the Art of Network Security Perspectives in Cloud Computing

NASA Astrophysics Data System (ADS)

Oh, Tae Hwan; Lim, Shinyoung; Choi, Young B.; Park, Kwang-Roh; Lee, Heejo; Choi, Hyunsang

Cloud computing is now regarded as one of social phenomenon that satisfy customers' needs. It is possible that the customers' needs and the primary principle of economy - gain maximum benefits from minimum investment - reflects realization of cloud computing. We are living in the connected society with flood of information and without connected computers to the Internet, our activities and work of daily living will be impossible. Cloud computing is able to provide customers with custom-tailored features of application software and user's environment based on the customer's needs by adopting on-demand outsourcing of computing resources through the Internet. It also provides cloud computing users with high-end computing power and expensive application software package, and accordingly the users will access their data and the application software where they are located at the remote system. As the cloud computing system is connected to the Internet, network security issues of cloud computing are considered as mandatory prior to real world service. In this paper, survey and issues on the network security in cloud computing are discussed from the perspective of real world service environments.

Towards Monitoring-as-a-service for Scientific Computing Cloud applications using the ElasticSearch ecosystem

NASA Astrophysics Data System (ADS)

Bagnasco, S.; Berzano, D.; Guarise, A.; Lusso, S.; Masera, M.; Vallero, S.

2015-12-01

The INFN computing centre in Torino hosts a private Cloud, which is managed with the OpenNebula cloud controller. The infrastructure offers Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) services to different scientific computing applications. The main stakeholders of the facility are a grid Tier-2 site for the ALICE collaboration at LHC, an interactive analysis facility for the same experiment and a grid Tier-2 site for the BESIII collaboration, plus an increasing number of other small tenants. The dynamic allocation of resources to tenants is partially automated. This feature requires detailed monitoring and accounting of the resource usage. We set up a monitoring framework to inspect the site activities both in terms of IaaS and applications running on the hosted virtual instances. For this purpose we used the ElasticSearch, Logstash and Kibana (ELK) stack. The infrastructure relies on a MySQL database back-end for data preservation and to ensure flexibility to choose a different monitoring solution if needed. The heterogeneous accounting information is transferred from the database to the ElasticSearch engine via a custom Logstash plugin. Each use-case is indexed separately in ElasticSearch and we setup a set of Kibana dashboards with pre-defined queries in order to monitor the relevant information in each case. For the IaaS metering, we developed sensors for the OpenNebula API. The IaaS level information gathered through the API is sent to the MySQL database through an ad-hoc developed RESTful web service. Moreover, we have developed a billing system for our private Cloud, which relies on the RabbitMQ message queue for asynchronous communication to the database and on the ELK stack for its graphical interface. The Italian Grid accounting framework is also migrating to a similar set-up. Concerning the application level, we used the Root plugin TProofMonSenderSQL to collect accounting data from the interactive analysis facility. The BESIII virtual instances used to be monitored with Zabbix, as a proof of concept we also retrieve the information contained in the Zabbix database. In this way we have achieved a uniform monitoring interface for both the IaaS and the scientific applications, mostly leveraging off-the-shelf tools. At present, we are working to define a model for monitoring-as-a-service, based on the tools described above, which the Cloud tenants can easily configure to suit their specific needs.

If It's in the Cloud, Get It on Paper: Cloud Computing Contract Issues

ERIC Educational Resources Information Center

Trappler, Thomas J.

2010-01-01

Much recent discussion has focused on the pros and cons of cloud computing. Some institutions are attracted to cloud computing benefits such as rapid deployment, flexible scalability, and low initial start-up cost, while others are concerned about cloud computing risks such as those related to data location, level of service, and security…

Introducing the Cloud in an Introductory IT Course

ERIC Educational Resources Information Center

Woods, David M.

2018-01-01

Cloud computing is a rapidly emerging topic, but should it be included in an introductory IT course? The magnitude of cloud computing use, especially cloud infrastructure, along with students' limited knowledge of the topic support adding cloud content to the IT curriculum. There are several arguments that support including cloud computing in an…

Enabling Earth Science Through Cloud Computing

NASA Technical Reports Server (NTRS)

Hardman, Sean; Riofrio, Andres; Shams, Khawaja; Freeborn, Dana; Springer, Paul; Chafin, Brian

2012-01-01

Cloud Computing holds tremendous potential for missions across the National Aeronautics and Space Administration. Several flight missions are already benefiting from an investment in cloud computing for mission critical pipelines and services through faster processing time, higher availability, and drastically lower costs available on cloud systems. However, these processes do not currently extend to general scientific algorithms relevant to earth science missions. The members of the Airborne Cloud Computing Environment task at the Jet Propulsion Laboratory have worked closely with the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) mission to integrate cloud computing into their science data processing pipeline. This paper details the efforts involved in deploying a science data system for the CARVE mission, evaluating and integrating cloud computing solutions with the system and porting their science algorithms for execution in a cloud environment.

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.

Learning and Design with Online Real-Time Collaboration

ERIC Educational Resources Information Center

Stevenson, Michael; Hedberg, John G.

2013-01-01

This paper explores the use of emerging Cloud technologies that support real-time online collaboration. It considers the extent to which these technologies can be leveraged to develop complex skillsets supporting interaction between multiple learners in online spaces. In a pilot study that closely examines how groups of learners translate two…

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.

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.

Using Cloud Computing infrastructure with CloudBioLinux, CloudMan and Galaxy

PubMed Central

Afgan, Enis; Chapman, Brad; Jadan, Margita; Franke, Vedran; Taylor, James

2012-01-01

Cloud computing has revolutionized availability and access to computing and storage resources; making it possible to provision a large computational infrastructure with only a few clicks in a web browser. However, those resources are typically provided in the form of low-level infrastructure components that need to be procured and configured before use. In this protocol, we demonstrate how to utilize cloud computing resources to perform open-ended bioinformatics analyses, with fully automated management of the underlying cloud infrastructure. By combining three projects, CloudBioLinux, CloudMan, and Galaxy into a cohesive unit, we have enabled researchers to gain access to more than 100 preconfigured bioinformatics tools and gigabytes of reference genomes on top of the flexible cloud computing infrastructure. The protocol demonstrates how to setup the available infrastructure and how to use the tools via a graphical desktop interface, a parallel command line interface, and the web-based Galaxy interface. PMID:22700313

Using cloud computing infrastructure with CloudBioLinux, CloudMan, and Galaxy.

PubMed

Afgan, Enis; Chapman, Brad; Jadan, Margita; Franke, Vedran; Taylor, James

2012-06-01

Cloud computing has revolutionized availability and access to computing and storage resources, making it possible to provision a large computational infrastructure with only a few clicks in a Web browser. However, those resources are typically provided in the form of low-level infrastructure components that need to be procured and configured before use. In this unit, we demonstrate how to utilize cloud computing resources to perform open-ended bioinformatic analyses, with fully automated management of the underlying cloud infrastructure. By combining three projects, CloudBioLinux, CloudMan, and Galaxy, into a cohesive unit, we have enabled researchers to gain access to more than 100 preconfigured bioinformatics tools and gigabytes of reference genomes on top of the flexible cloud computing infrastructure. The protocol demonstrates how to set up the available infrastructure and how to use the tools via a graphical desktop interface, a parallel command-line interface, and the Web-based Galaxy interface.

Identity-Based Authentication for Cloud Computing

NASA Astrophysics Data System (ADS)

Li, Hongwei; Dai, Yuanshun; Tian, Ling; Yang, Haomiao

Cloud computing is a recently developed new technology for complex systems with massive-scale services sharing among numerous users. Therefore, authentication of both users and services is a significant issue for the trust and security of the cloud computing. SSL Authentication Protocol (SAP), once applied in cloud computing, will become so complicated that users will undergo a heavily loaded point both in computation and communication. This paper, based on the identity-based hierarchical model for cloud computing (IBHMCC) and its corresponding encryption and signature schemes, presented a new identity-based authentication protocol for cloud computing and services. Through simulation testing, it is shown that the authentication protocol is more lightweight and efficient than SAP, specially the more lightweight user side. Such merit of our model with great scalability is very suited to the massive-scale cloud.

Cloud Based Educational Systems and Its Challenges and Opportunities and Issues

ERIC Educational Resources Information Center

Paul, Prantosh Kr.; Lata Dangwal, Kiran

2014-01-01

Cloud Computing (CC) is actually is a set of hardware, software, networks, storage, services an interface combines to deliver aspects of computing as a service. Cloud Computing (CC) actually uses the central remote servers to maintain data and applications. Practically Cloud Computing (CC) is extension of Grid computing with independency and…

A scoping review of cloud computing in healthcare.

PubMed

Griebel, Lena; Prokosch, Hans-Ulrich; Köpcke, Felix; Toddenroth, Dennis; Christoph, Jan; Leb, Ines; Engel, Igor; Sedlmayr, Martin

2015-03-19

Cloud computing is a recent and fast growing area of development in healthcare. Ubiquitous, on-demand access to virtually endless resources in combination with a pay-per-use model allow for new ways of developing, delivering and using services. Cloud computing is often used in an "OMICS-context", e.g. for computing in genomics, proteomics and molecular medicine, while other field of application still seem to be underrepresented. Thus, the objective of this scoping review was to identify the current state and hot topics in research on cloud computing in healthcare beyond this traditional domain. MEDLINE was searched in July 2013 and in December 2014 for publications containing the terms "cloud computing" and "cloud-based". Each journal and conference article was categorized and summarized independently by two researchers who consolidated their findings. 102 publications have been analyzed and 6 main topics have been found: telemedicine/teleconsultation, medical imaging, public health and patient self-management, hospital management and information systems, therapy, and secondary use of data. Commonly used features are broad network access for sharing and accessing data and rapid elasticity to dynamically adapt to computing demands. Eight articles favor the pay-for-use characteristics of cloud-based services avoiding upfront investments. Nevertheless, while 22 articles present very general potentials of cloud computing in the medical domain and 66 articles describe conceptual or prototypic projects, only 14 articles report from successful implementations. Further, in many articles cloud computing is seen as an analogy to internet-/web-based data sharing and the characteristics of the particular cloud computing approach are unfortunately not really illustrated. Even though cloud computing in healthcare is of growing interest only few successful implementations yet exist and many papers just use the term "cloud" synonymously for "using virtual machines" or "web-based" with no described benefit of the cloud paradigm. The biggest threat to the adoption in the healthcare domain is caused by involving external cloud partners: many issues of data safety and security are still to be solved. Until then, cloud computing is favored more for singular, individual features such as elasticity, pay-per-use and broad network access, rather than as cloud paradigm on its own.

Evolution of the ATLAS PanDA workload management system for exascale computational science

NASA Astrophysics Data System (ADS)

Maeno, T.; De, K.; Klimentov, A.; Nilsson, P.; Oleynik, D.; Panitkin, S.; Petrosyan, A.; Schovancova, J.; Vaniachine, A.; Wenaus, T.; Yu, D.; Atlas Collaboration

2014-06-01

An important foundation underlying the impressive success of data processing and analysis in the ATLAS experiment [1] at the LHC [2] is the Production and Distributed Analysis (PanDA) workload management system [3]. PanDA was designed specifically for ATLAS and proved to be highly successful in meeting all the distributed computing needs of the experiment. However, the core design of PanDA is not experiment specific. The PanDA workload management system is capable of meeting the needs of other data intensive scientific applications. Alpha-Magnetic Spectrometer [4], an astro-particle experiment on the International Space Station, and the Compact Muon Solenoid [5], an LHC experiment, have successfully evaluated PanDA and are pursuing its adoption. In this paper, a description of the new program of work to develop a generic version of PanDA will be given, as well as the progress in extending PanDA's capabilities to support supercomputers and clouds and to leverage intelligent networking. PanDA has demonstrated at a very large scale the value of automated dynamic brokering of diverse workloads across distributed computing resources. The next generation of PanDA will allow other data-intensive sciences and a wider exascale community employing a variety of computing platforms to benefit from ATLAS' experience and proven tools.

Modeling the Cloud to Enhance Capabilities for Crises and Catastrophe Management

DTIC Science & Technology

2016-11-16

order for cloud computing infrastructures to be successfully deployed in real world scenarios as tools for crisis and catastrophe management, where...Statement of the Problem Studied As cloud computing becomes the dominant computational infrastructure[1] and cloud technologies make a transition to hosting...1. Formulate rigorous mathematical models representing technological capabilities and resources in cloud computing for performance modeling and

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

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.

Mobile Cloud Learning for Higher Education: A Case Study of Moodle in the Cloud

ERIC Educational Resources Information Center

Wang, Minjuan; Chen, Yong; Khan, Muhammad Jahanzaib

2014-01-01

Mobile cloud learning, a combination of mobile learning and cloud computing, is a relatively new concept that holds considerable promise for future development and delivery in the education sectors. Cloud computing helps mobile learning overcome obstacles related to mobile computing. The main focus of this paper is to explore how cloud computing…

76 FR 13984 - Cloud Computing Forum & Workshop III

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-15

... DEPARTMENT OF COMMERCE National Institute of Standards and Technology Cloud Computing Forum... public workshop. SUMMARY: NIST announces the Cloud Computing Forum & Workshop III to be held on April 7... provide information on the NIST strategic and tactical Cloud Computing program, including progress on the...

Community Cloud Computing

NASA Astrophysics Data System (ADS)

Marinos, Alexandros; Briscoe, Gerard

Cloud Computing is rising fast, with its data centres growing at an unprecedented rate. However, this has come with concerns over privacy, efficiency at the expense of resilience, and environmental sustainability, because of the dependence on Cloud vendors such as Google, Amazon and Microsoft. Our response is an alternative model for the Cloud conceptualisation, providing a paradigm for Clouds in the community, utilising networked personal computers for liberation from the centralised vendor model. Community Cloud Computing (C3) offers an alternative architecture, created by combing the Cloud with paradigms from Grid Computing, principles from Digital Ecosystems, and sustainability from Green Computing, while remaining true to the original vision of the Internet. It is more technically challenging than Cloud Computing, having to deal with distributed computing issues, including heterogeneous nodes, varying quality of service, and additional security constraints. However, these are not insurmountable challenges, and with the need to retain control over our digital lives and the potential environmental consequences, it is a challenge we must pursue.

Inferring Large-Scale Terrestrial Water Storage Through GRACE and GPS Data Fusion in Cloud Computing Environments

NASA Astrophysics Data System (ADS)

Rude, C. M.; Li, J. D.; Gowanlock, M.; Herring, T.; Pankratius, V.

2016-12-01

Surface subsidence due to depletion of groundwater can lead to permanent compaction of aquifers and damaged infrastructure. However, studies of such effects on a large scale are challenging and compute intensive because they involve fusing a variety of data sets beyond direct measurements from groundwater wells, such as gravity change measurements from the Gravity Recovery and Climate Experiment (GRACE) or surface displacements measured by GPS receivers. Our work therefore leverages Amazon cloud computing to enable these types of analyses spanning the entire continental US. Changes in groundwater storage are inferred from surface displacements measured by GPS receivers stationed throughout the country. Receivers located on bedrock are anti-correlated with changes in water levels from elastic deformation due to loading, while stations on aquifers correlate with groundwater changes due to poroelastic expansion and compaction. Correlating linearly detrended equivalent water thickness measurements from GRACE with linearly detrended and Kalman filtered vertical displacements of GPS stations located throughout the United States helps compensate for the spatial and temporal limitations of GRACE. Our results show that the majority of GPS stations are negatively correlated with GRACE in a statistically relevant way, as most GPS stations are located on bedrock in order to provide stable reference locations and measure geophysical processes such as tectonic deformations. Additionally, stations located on the Central Valley California aquifer show statistically significant positive correlations. Through the identification of positive and negative correlations, deformation phenomena can be classified as loading or poroelastic expansion due to changes in groundwater. This method facilitates further studies of terrestrial water storage on a global scale. This work is supported by NASA AIST-NNX15AG84G (PI: V. Pankratius) and Amazon.

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.

Morphological diagnostics of star formation in molecular clouds

NASA Astrophysics Data System (ADS)

Beaumont, Christopher Norris

Molecular clouds are the birth sites of all star formation in the present-day universe. They represent the initial conditions of star formation, and are the primary medium by which stars transfer energy and momentum back to parsec scales. Yet, the physical evolution of molecular clouds remains poorly understood. This is not due to a lack of observational data, nor is it due to an inability to simulate the conditions inside molecular clouds. Instead, the physics and structure of the interstellar medium are sufficiently complex that interpreting molecular cloud data is very difficult. This dissertation mitigates this problem, by developing more sophisticated ways to interpret morphological information in molecular cloud observations and simulations. In particular, I have focused on leveraging machine learning techniques to identify physically meaningful substructures in the interstellar medium, as well as techniques to inter-compare molecular cloud simulations to observations. These contributions make it easier to understand the interplay between molecular clouds and star formation. Specific contributions include: new insight about the sheet-like geometry of molecular clouds based on observations of stellar bubbles; a new algorithm to disambiguate overlapping yet morphologically distinct cloud structures; a new perspective on the relationship between molecular cloud column density distributions and the sizes of cloud substructures; a quantitative analysis of how projection effects affect measurements of cloud properties; and an automatically generated, statistically-calibrated catalog of bubbles identified from their infrared morphologies.

Cost-effective cloud computing: a case study using the comparative genomics tool, roundup.

PubMed

Kudtarkar, Parul; Deluca, Todd F; Fusaro, Vincent A; Tonellato, Peter J; Wall, Dennis P

2010-12-22

Comparative genomics resources, such as ortholog detection tools and repositories are rapidly increasing in scale and complexity. Cloud computing is an emerging technological paradigm that enables researchers to dynamically build a dedicated virtual cluster and may represent a valuable alternative for large computational tools in bioinformatics. In the present manuscript, we optimize the computation of a large-scale comparative genomics resource-Roundup-using cloud computing, describe the proper operating principles required to achieve computational efficiency on the cloud, and detail important procedures for improving cost-effectiveness to ensure maximal computation at minimal costs. Utilizing the comparative genomics tool, Roundup, as a case study, we computed orthologs among 902 fully sequenced genomes on Amazon's Elastic Compute Cloud. For managing the ortholog processes, we designed a strategy to deploy the web service, Elastic MapReduce, and maximize the use of the cloud while simultaneously minimizing costs. Specifically, we created a model to estimate cloud runtime based on the size and complexity of the genomes being compared that determines in advance the optimal order of the jobs to be submitted. We computed orthologous relationships for 245,323 genome-to-genome comparisons on Amazon's computing cloud, a computation that required just over 200 hours and cost $8,000 USD, at least 40% less than expected under a strategy in which genome comparisons were submitted to the cloud randomly with respect to runtime. Our cost savings projections were based on a model that not only demonstrates the optimal strategy for deploying RSD to the cloud, but also finds the optimal cluster size to minimize waste and maximize usage. Our cost-reduction model is readily adaptable for other comparative genomics tools and potentially of significant benefit to labs seeking to take advantage of the cloud as an alternative to local computing infrastructure.

75 FR 64258 - Cloud Computing Forum & Workshop II

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-19

... DEPARTMENT OF COMMERCE National Institute of Standards and Technology Cloud Computing Forum... workshop. SUMMARY: NIST announces the Cloud Computing Forum & Workshop II to be held on November 4 and 5, 2010. This workshop will provide information on a Cloud Computing Roadmap Strategy as well as provide...

76 FR 62373 - Notice of Public Meeting-Cloud Computing Forum & Workshop IV

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-07

...--Cloud Computing Forum & Workshop IV AGENCY: National Institute of Standards and Technology (NIST), Commerce. ACTION: Notice. SUMMARY: NIST announces the Cloud Computing Forum & Workshop IV to be held on... to help develop open standards in interoperability, portability and security in cloud computing. This...

Notification: Fieldwork for CIGIE Cloud Computing Initiative – Status of Cloud-Computing Within the Federal Government

EPA Pesticide Factsheets

Project #OA-FY14-0126, January 15, 2014. The EPA OIG is starting fieldwork on the Council of the Inspectors General on Integrity and Efficiency (CIGIE) Cloud Computing Initiative – Status of Cloud-Computing Environments Within the Federal Government.

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.

WE-B-BRD-01: Innovation in Radiation Therapy Planning II: Cloud Computing in RT

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

Moore, K; Kagadis, G; Xing, L

As defined by the National Institute of Standards and Technology, cloud computing is “a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.” Despite the omnipresent role of computers in radiotherapy, cloud computing has yet to achieve widespread adoption in clinical or research applications, though the transition to such “on-demand” access is underway. As this transition proceeds, new opportunities for aggregate studies and efficient use of computational resources are set againstmore » new challenges in patient privacy protection, data integrity, and management of clinical informatics systems. In this Session, current and future applications of cloud computing and distributed computational resources will be discussed in the context of medical imaging, radiotherapy research, and clinical radiation oncology applications. Learning Objectives: Understand basic concepts of cloud computing. Understand how cloud computing could be used for medical imaging applications. Understand how cloud computing could be employed for radiotherapy research.4. Understand how clinical radiotherapy software applications would function in the cloud.« less

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.

"helix Nebula - the Science Cloud", a European Science Driven Cross-Domain Initiative Implemented in via AN Active Ppp Set-Up

NASA Astrophysics Data System (ADS)

Lengert, W.; Mondon, E.; Bégin, M. E.; Ferrer, M.; Vallois, F.; DelaMar, J.

2015-12-01

Helix Nebula, a European science cross-domain initiative building on an active PPP, is aiming to implement the concept of an open science commons[1] while using a cloud hybrid model[2] as the proposed implementation solution. This approach allows leveraging and merging of complementary data intensive Earth Science disciplines (e.g. instrumentation[3] and modeling), without introducing significant changes in the contributors' operational set-up. Considering the seamless integration with life-science (e.g. EMBL), scientific exploitation of meteorological, climate, and Earth Observation data and models open an enormous potential for new big data science. The work of Helix Nebula has shown that is it feasible to interoperate publicly funded infrastructures, such as EGI [5] and GEANT [6], with commercial cloud services. Such hybrid systems are in the interest of the existing users of publicly funded infrastructures and funding agencies because they will provide "freedom and choice" over the type of computing resources to be consumed and the manner in which they can be obtained. But to offer such freedom and choice across a spectrum of suppliers, various issues such as intellectual property, legal responsibility, service quality agreements and related issues need to be addressed. Finding solutions to these issues is one of the goals of the Helix Nebula initiative. [1] http://www.egi.eu/news-and-media/publications/OpenScienceCommons_v3.pdf [2] http://www.helix-nebula.eu/events/towards-the-european-open-science-cloud [3] e.g. https://sentinel.esa.int/web/sentinel/sentinel-data-access [5] http://www.egi.eu/ [6] http://www.geant.net/

Energy Consumption Management of Virtual Cloud Computing Platform

NASA Astrophysics Data System (ADS)

Li, Lin

2017-11-01

For energy consumption management research on virtual cloud computing platforms, energy consumption management of virtual computers and cloud computing platform should be understood deeper. Only in this way can problems faced by energy consumption management be solved. In solving problems, the key to solutions points to data centers with high energy consumption, so people are in great need to use a new scientific technique. Virtualization technology and cloud computing have become powerful tools in people’s real life, work and production because they have strong strength and many advantages. Virtualization technology and cloud computing now is in a rapid developing trend. It has very high resource utilization rate. In this way, the presence of virtualization and cloud computing technologies is very necessary in the constantly developing information age. This paper has summarized, explained and further analyzed energy consumption management questions of the virtual cloud computing platform. It eventually gives people a clearer understanding of energy consumption management of virtual cloud computing platform and brings more help to various aspects of people’s live, work and son on.

Cloud-free resolution element statistics program

NASA Technical Reports Server (NTRS)

Liley, B.; Martin, C. D.

1971-01-01

Computer program computes number of cloud-free elements in field-of-view and percentage of total field-of-view occupied by clouds. Human error is eliminated by using visual estimation to compute cloud statistics from aerial photographs.

The Hico Image Processing System: A Web-Accessible Hyperspectral Remote Sensing Toolbox

NASA Astrophysics Data System (ADS)

Harris, A. T., III; Goodman, J.; Justice, B.

2014-12-01

As the quantity of Earth-observation data increases, the use-case for hosting analytical tools in geospatial data centers becomes increasingly attractive. To address this need, HySpeed Computing and Exelis VIS have developed the HICO Image Processing System, a prototype cloud computing system that provides online, on-demand, scalable remote sensing image processing capabilities. The system provides a mechanism for delivering sophisticated image processing analytics and data visualization tools into the hands of a global user community, who will only need a browser and internet connection to perform analysis. Functionality of the HICO Image Processing System is demonstrated using imagery from the Hyperspectral Imager for the Coastal Ocean (HICO), an imaging spectrometer located on the International Space Station (ISS) that is optimized for acquisition of aquatic targets. Example applications include a collection of coastal remote sensing algorithms that are directed at deriving critical information on water and habitat characteristics of our vulnerable coastal environment. The project leverages the ENVI Services Engine as the framework for all image processing tasks, and can readily accommodate the rapid integration of new algorithms, datasets and processing tools.

On transferring the grid technology to the biomedical community.

PubMed

Mohammed, Yassene; Sax, Ulrich; Dickmann, Frank; Lippert, Joerg; Solodenko, Juri; von Voigt, Gabriele; Smith, Matthew; Rienhoff, Otto

2010-01-01

Natural scientists such as physicists pioneered the sharing of computing resources, which resulted in the Grid. The inter domain transfer process of this technology has been an intuitive process. Some difficulties facing the life science community can be understood using the Bozeman's "Effectiveness Model of Technology Transfer". Bozeman's and classical technology transfer approaches deal with technologies that have achieved certain stability. Grid and Cloud solutions are technologies that are still in flux. We illustrate how Grid computing creates new difficulties for the technology transfer process that are not considered in Bozeman's model. We show why the success of health Grids should be measured by the qualified scientific human capital and opportunities created, and not primarily by the market impact. With two examples we show how the Grid technology transfer theory corresponds to the reality. We conclude with recommendations that can help improve the adoption of Grid solutions into the biomedical community. These results give a more concise explanation of the difficulties most life science IT projects are facing in the late funding periods, and show some leveraging steps which can help to overcome the "vale of tears".

Research on Influence of Cloud Environment on Traditional Network Security

NASA Astrophysics Data System (ADS)

Ming, Xiaobo; Guo, Jinhua

2018-02-01

Cloud computing is a symbol of the progress of modern information network, cloud computing provides a lot of convenience to the Internet users, but it also brings a lot of risk to the Internet users. Second, one of the main reasons for Internet users to choose cloud computing is that the network security performance is great, it also is the cornerstone of cloud computing applications. This paper briefly explores the impact on cloud environment on traditional cybersecurity, and puts forward corresponding solutions.

77 FR 26509 - Notice of Public Meeting-Cloud Computing Forum & Workshop V

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-04

...--Cloud Computing Forum & Workshop V AGENCY: National Institute of Standards & Technology (NIST), Commerce. ACTION: Notice. SUMMARY: NIST announces the Cloud Computing Forum & Workshop V to be held on Tuesday... workshop. This workshop will provide information on the U.S. Government (USG) Cloud Computing Technology...

National electronic medical records integration on cloud computing system.

PubMed

Mirza, Hebah; El-Masri, Samir

2013-01-01

Few Healthcare providers have an advanced level of Electronic Medical Record (EMR) adoption. Others have a low level and most have no EMR at all. Cloud computing technology is a new emerging technology that has been used in other industry and showed a great success. Despite the great features of Cloud computing, they haven't been utilized fairly yet in healthcare industry. This study presents an innovative Healthcare Cloud Computing system for Integrating Electronic Health Record (EHR). The proposed Cloud system applies the Cloud Computing technology on EHR system, to present a comprehensive EHR integrated environment.

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.

The Adoption of Cloud Computing in the Field of Genomics Research: The Influence of Ethical and Legal Issues

PubMed Central

Charlebois, Kathleen; Palmour, Nicole; Knoppers, Bartha Maria

2016-01-01

This study aims to understand the influence of the ethical and legal issues on cloud computing adoption in the field of genomics research. To do so, we adapted Diffusion of Innovation (DoI) theory to enable understanding of how key stakeholders manage the various ethical and legal issues they encounter when adopting cloud computing. Twenty semi-structured interviews were conducted with genomics researchers, patient advocates and cloud service providers. Thematic analysis generated five major themes: 1) Getting comfortable with cloud computing; 2) Weighing the advantages and the risks of cloud computing; 3) Reconciling cloud computing with data privacy; 4) Maintaining trust and 5) Anticipating the cloud by creating the conditions for cloud adoption. Our analysis highlights the tendency among genomics researchers to gradually adopt cloud technology. Efforts made by cloud service providers to promote cloud computing adoption are confronted by researchers’ perpetual cost and security concerns, along with a lack of familiarity with the technology. Further underlying those fears are researchers’ legal responsibility with respect to the data that is stored on the cloud. Alternative consent mechanisms aimed at increasing patients’ control over the use of their data also provide a means to circumvent various institutional and jurisdictional hurdles that restrict access by creating siloed databases. However, the risk of creating new, cloud-based silos may run counter to the goal in genomics research to increase data sharing on a global scale. PMID:27755563

The Adoption of Cloud Computing in the Field of Genomics Research: The Influence of Ethical and Legal Issues.

PubMed

Charlebois, Kathleen; Palmour, Nicole; Knoppers, Bartha Maria

2016-01-01

This study aims to understand the influence of the ethical and legal issues on cloud computing adoption in the field of genomics research. To do so, we adapted Diffusion of Innovation (DoI) theory to enable understanding of how key stakeholders manage the various ethical and legal issues they encounter when adopting cloud computing. Twenty semi-structured interviews were conducted with genomics researchers, patient advocates and cloud service providers. Thematic analysis generated five major themes: 1) Getting comfortable with cloud computing; 2) Weighing the advantages and the risks of cloud computing; 3) Reconciling cloud computing with data privacy; 4) Maintaining trust and 5) Anticipating the cloud by creating the conditions for cloud adoption. Our analysis highlights the tendency among genomics researchers to gradually adopt cloud technology. Efforts made by cloud service providers to promote cloud computing adoption are confronted by researchers' perpetual cost and security concerns, along with a lack of familiarity with the technology. Further underlying those fears are researchers' legal responsibility with respect to the data that is stored on the cloud. Alternative consent mechanisms aimed at increasing patients' control over the use of their data also provide a means to circumvent various institutional and jurisdictional hurdles that restrict access by creating siloed databases. However, the risk of creating new, cloud-based silos may run counter to the goal in genomics research to increase data sharing on a global scale.

Cloud Computing for Complex Performance Codes.

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

Appel, Gordon John; Hadgu, Teklu; Klein, Brandon Thorin

This report describes the use of cloud computing services for running complex public domain performance assessment problems. The work consisted of two phases: Phase 1 was to demonstrate complex codes, on several differently configured servers, could run and compute trivial small scale problems in a commercial cloud infrastructure. Phase 2 focused on proving non-trivial large scale problems could be computed in the commercial cloud environment. The cloud computing effort was successfully applied using codes of interest to the geohydrology and nuclear waste disposal modeling community.

Cloud Fingerprinting: Using Clock Skews To Determine Co Location Of Virtual Machines

DTIC Science & Technology

2016-09-01

DISTRIBUTION CODE 13. ABSTRACT (maximum 200 words) Cloud computing has quickly revolutionized computing practices of organizations, to include the Department of... Cloud computing has quickly revolutionized computing practices of organizations, to in- clude the Department of Defense. However, security concerns...vi Table of Contents 1 Introduction 1 1.1 Proliferation of Cloud Computing . . . . . . . . . . . . . . . . . . 1 1.2 Problem Statement

A Rethink for Computing Education for Sustainability

ERIC Educational Resources Information Center

Mann, Samuel

2016-01-01

The premise of Computing Education for Sustainability (CEfS) is examined. CEfS is described as a leverage discipline, where the handprint is much larger than the footprint. The potential of this leverage is described and the development of the field explored. Unfortunately CEfS is found not to be making sufficient impact in terms of a contribution…

Learning from the Learners: Preparing Future Teachers to Leverage the Benefits of Laptop Computers

ERIC Educational Resources Information Center

Grundmeyer, Trent; Peters, Randal

2016-01-01

Technology is changing the teaching and learning landscape. Teacher preparation programs must produce teachers who have new skills and strategies to leverage the benefits of laptop computers in their classrooms. This study used a phenomenological strategy to explain first-year college students' perceptions of the effects of a 1:1 laptop experience…

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.

Cytobank: providing an analytics platform for community cytometry data analysis and collaboration.

PubMed

Chen, Tiffany J; Kotecha, Nikesh

2014-01-01

Cytometry is used extensively in clinical and laboratory settings to diagnose and track cell subsets in blood and tissue. High-throughput, single-cell approaches leveraging cytometry are developed and applied in the computational and systems biology communities by researchers, who seek to improve the diagnosis of human diseases, map the structures of cell signaling networks, and identify new cell types. Data analysis and management present a bottleneck in the flow of knowledge from bench to clinic. Multi-parameter flow and mass cytometry enable identification of signaling profiles of patient cell samples. Currently, this process is manual, requiring hours of work to summarize multi-dimensional data and translate these data for input into other analysis programs. In addition, the increase in the number and size of collaborative cytometry studies as well as the computational complexity of analytical tools require the ability to assemble sufficient and appropriately configured computing capacity on demand. There is a critical need for platforms that can be used by both clinical and basic researchers who routinely rely on cytometry. Recent advances provide a unique opportunity to facilitate collaboration and analysis and management of cytometry data. Specifically, advances in cloud computing and virtualization are enabling efficient use of large computing resources for analysis and backup. An example is Cytobank, a platform that allows researchers to annotate, analyze, and share results along with the underlying single-cell data.

Proposal for a Security Management in Cloud Computing for Health Care

PubMed Central

Dzombeta, Srdan; Brandis, Knud

2014-01-01

Cloud computing is actually one of the most popular themes of information systems research. Considering the nature of the processed information especially health care organizations need to assess and treat specific risks according to cloud computing in their information security management system. Therefore, in this paper we propose a framework that includes the most important security processes regarding cloud computing in the health care sector. Starting with a framework of general information security management processes derived from standards of the ISO 27000 family the most important information security processes for health care organizations using cloud computing will be identified considering the main risks regarding cloud computing and the type of information processed. The identified processes will help a health care organization using cloud computing to focus on the most important ISMS processes and establish and operate them at an appropriate level of maturity considering limited resources. PMID:24701137

Proposal for a security management in cloud computing for health care.

PubMed

Haufe, Knut; Dzombeta, Srdan; Brandis, Knud

2014-01-01

Cloud computing is actually one of the most popular themes of information systems research. Considering the nature of the processed information especially health care organizations need to assess and treat specific risks according to cloud computing in their information security management system. Therefore, in this paper we propose a framework that includes the most important security processes regarding cloud computing in the health care sector. Starting with a framework of general information security management processes derived from standards of the ISO 27000 family the most important information security processes for health care organizations using cloud computing will be identified considering the main risks regarding cloud computing and the type of information processed. The identified processes will help a health care organization using cloud computing to focus on the most important ISMS processes and establish and operate them at an appropriate level of maturity considering limited resources.

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

Klein, Levente

Interpreting sensor data require knowledge about sensor placement and the surrounding environment. For a single sensor measurement, it is easy to document the context by visual observation, however for millions of sensors reporting data back to a server, the contextual information needs to be automatically extracted from either data analysis or leveraging complimentary data sources. Data layers that overlap spatially or temporally with sensor locations, can be used to extract the context and to validate the measurement. To minimize the amount of data transmitted through the internet, while preserving signal information content, two methods are explored; computation at the edgemore » and compressed sensing. We validate the above methods on wind and chemical sensor data (1) eliminate redundant measurement from wind sensors and (2) extract peak value of a chemical sensor measuring a methane plume. We present a general cloud based framework to validate sensor data based on statistical and physical modeling and contextual data extracted from geospatial data.« less

Molecular digital pathology: progress and potential of exchanging molecular data.

PubMed

Roy, Somak; Pfeifer, John D; LaFramboise, William A; Pantanowitz, Liron

2016-09-01

Many of the demands to perform next generation sequencing (NGS) in the clinical laboratory can be resolved using the principles of telepathology. Molecular telepathology can allow facilities to outsource all or a portion of their NGS operation such as cloud computing, bioinformatics pipelines, variant data management, and knowledge curation. Clinical pathology laboratories can electronically share diverse types of molecular data with reference laboratories, technology service providers, and/or regulatory agencies. Exchange of electronic molecular data allows laboratories to perform validation of rare diseases using foreign data, check the accuracy of their test results against benchmarks, and leverage in silico proficiency testing. This review covers the emerging subject of molecular telepathology, describes clinical use cases for the appropriate exchange of molecular data, and highlights key issues such as data integrity, interoperable formats for massive genomic datasets, security, malpractice and emerging regulations involved with this novel practice.

Cloud Computing in Higher Education Sector for Sustainable Development

ERIC Educational Resources Information Center

Duan, Yuchao

2016-01-01

Cloud computing is considered a new frontier in the field of computing, as this technology comprises three major entities namely: software, hardware and network. The collective nature of all these entities is known as the Cloud. This research aims to examine the impacts of various aspects namely: cloud computing, sustainability, performance…

76 FR 67418 - Request for Comments on NIST Special Publication 500-293, US Government Cloud Computing...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-01

...-1659-01] Request for Comments on NIST Special Publication 500-293, US Government Cloud Computing... Publication 500-293, US Government Cloud Computing Technology Roadmap, Release 1.0 (Draft). This document is... (USG) agencies to accelerate their adoption of cloud computing. The roadmap has been developed through...

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.

A Cloud-based Approach to Medical NLP

PubMed Central

Chard, Kyle; Russell, Michael; Lussier, Yves A.; Mendonça, Eneida A; Silverstein, Jonathan C.

2011-01-01

Natural Language Processing (NLP) enables access to deep content embedded in medical texts. To date, NLP has not fulfilled its promise of enabling robust clinical encoding, clinical use, quality improvement, and research. We submit that this is in part due to poor accessibility, scalability, and flexibility of NLP systems. We describe here an approach and system which leverages cloud-based approaches such as virtual machines and Representational State Transfer (REST) to extract, process, synthesize, mine, compare/contrast, explore, and manage medical text data in a flexibly secure and scalable architecture. Available architectures in which our Smntx (pronounced as semantics) system can be deployed include: virtual machines in a HIPAA-protected hospital environment, brought up to run analysis over bulk data and destroyed in a local cloud; a commercial cloud for a large complex multi-institutional trial; and within other architectures such as caGrid, i2b2, or NHIN. PMID:22195072

A cloud-based approach to medical NLP.

PubMed

Chard, Kyle; Russell, Michael; Lussier, Yves A; Mendonça, Eneida A; Silverstein, Jonathan C

2011-01-01

Natural Language Processing (NLP) enables access to deep content embedded in medical texts. To date, NLP has not fulfilled its promise of enabling robust clinical encoding, clinical use, quality improvement, and research. We submit that this is in part due to poor accessibility, scalability, and flexibility of NLP systems. We describe here an approach and system which leverages cloud-based approaches such as virtual machines and Representational State Transfer (REST) to extract, process, synthesize, mine, compare/contrast, explore, and manage medical text data in a flexibly secure and scalable architecture. Available architectures in which our Smntx (pronounced as semantics) system can be deployed include: virtual machines in a HIPAA-protected hospital environment, brought up to run analysis over bulk data and destroyed in a local cloud; a commercial cloud for a large complex multi-institutional trial; and within other architectures such as caGrid, i2b2, or NHIN.

The use of LIDAR Technology for Measuring Mixing Heights under the Photochemical Assessment Monitoring Program; leveraging research under the joint DISCOVER-AQ/FRAPPÉ Missions

EPA Science Inventory

The operational use of ceilometers across the United States has been limited to detection of cloud-base heights across the Automatic Surface Observing Systems (ASOS) primarily operated by the National Weather Service and the Federal Aviation Administration. Continued improvements...

"Leveraged Footprints", "Dark Clouds" and "Bleeding Millions": Perspectives on Australian Universities' Offshore Campuses

ERIC Educational Resources Information Center

McBurnie, Grant

2006-01-01

Australia is a leading proponent of trade liberalisation, including education services. It is a major provider of fee-charging education to international students, both in Australia and offshore. This paper focuses on the three key motivations for Australian universities to establish offshore campuses--academic, financial, reputational--and…

Teaching in the Cloud: Leveraging Online Collaboration Tools to Enhance Student Engagement. CRLT Occasional Paper No. 31

ERIC Educational Resources Information Center

Hershock, Chad; LaVaque-Manty, Mika

2012-01-01

The rapid proliferation of technology can have profound effects on the evolution of teaching, learning, scholarship, and governance in higher education (Katz, 2008). However, instructors report that simply "keeping up" with new instructional technologies, let alone integrating them productively into one's teaching, can be a significant…

A Comprehensive Review of Existing Risk Assessment Models in Cloud Computing

NASA Astrophysics Data System (ADS)

Amini, Ahmad; Jamil, Norziana

2018-05-01

Cloud computing is a popular paradigm in information technology and computing as it offers numerous advantages in terms of economical saving and minimal management effort. Although elasticity and flexibility brings tremendous benefits, it still raises many information security issues due to its unique characteristic that allows ubiquitous computing. Therefore, the vulnerabilities and threats in cloud computing have to be identified and proper risk assessment mechanism has to be in place for better cloud computing management. Various quantitative and qualitative risk assessment models have been proposed but up to our knowledge, none of them is suitable for cloud computing environment. This paper, we compare and analyse the strengths and weaknesses of existing risk assessment models. We then propose a new risk assessment model that sufficiently address all the characteristics of cloud computing, which was not appeared in the existing models.

Impacts and Opportunities for Engineering in the Era of Cloud Computing Systems

DTIC Science & Technology

2012-01-31

2012 UNCLASSIFIED 1 of 58 Impacts and Opportunities for Engineering in the Era of Cloud Computing Systems A Report to the U.S. Department...2.1.7 Engineering of Computational Behavior .............................................................18 2.2 How the Cloud Will Impact Systems...58 Executive Summary This report discusses the impact of cloud computing and the broader revolution in computing on systems, on the disciplines of

Cloud Computing Value Chains: Understanding Businesses and Value Creation in the Cloud

NASA Astrophysics Data System (ADS)

Mohammed, Ashraf Bany; Altmann, Jörn; Hwang, Junseok

Based on the promising developments in Cloud Computing technologies in recent years, commercial computing resource services (e.g. Amazon EC2) or software-as-a-service offerings (e.g. Salesforce. com) came into existence. However, the relatively weak business exploitation, participation, and adoption of other Cloud Computing services remain the main challenges. The vague value structures seem to be hindering business adoption and the creation of sustainable business models around its technology. Using an extensive analyze of existing Cloud business models, Cloud services, stakeholder relations, market configurations and value structures, this Chapter develops a reference model for value chains in the Cloud. Although this model is theoretically based on porter's value chain theory, the proposed Cloud value chain model is upgraded to fit the diversity of business service scenarios in the Cloud computing markets. Using this model, different service scenarios are explained. Our findings suggest new services, business opportunities, and policy practices for realizing more adoption and value creation paths in the Cloud.

Virtualization and cloud computing in dentistry.

PubMed

Chow, Frank; Muftu, Ali; Shorter, Richard

2014-01-01

The use of virtualization and cloud computing has changed the way we use computers. Virtualization is a method of placing software called a hypervisor on the hardware of a computer or a host operating system. It allows a guest operating system to run on top of the physical computer with a virtual machine (i.e., virtual computer). Virtualization allows multiple virtual computers to run on top of one physical computer and to share its hardware resources, such as printers, scanners, and modems. This increases the efficient use of the computer by decreasing costs (e.g., hardware, electricity administration, and management) since only one physical computer is needed and running. This virtualization platform is the basis for cloud computing. It has expanded into areas of server and storage virtualization. One of the commonly used dental storage systems is cloud storage. Patient information is encrypted as required by the Health Insurance Portability and Accountability Act (HIPAA) and stored on off-site private cloud services for a monthly service fee. As computer costs continue to increase, so too will the need for more storage and processing power. Virtual and cloud computing will be a method for dentists to minimize costs and maximize computer efficiency in the near future. This article will provide some useful information on current uses of cloud computing.

Global Software Development with Cloud Platforms

NASA Astrophysics Data System (ADS)

Yara, Pavan; Ramachandran, Ramaseshan; Balasubramanian, Gayathri; Muthuswamy, Karthik; Chandrasekar, Divya

Offshore and outsourced distributed software development models and processes are facing challenges, previously unknown, with respect to computing capacity, bandwidth, storage, security, complexity, reliability, and business uncertainty. Clouds promise to address these challenges by adopting recent advances in virtualization, parallel and distributed systems, utility computing, and software services. In this paper, we envision a cloud-based platform that addresses some of these core problems. We outline a generic cloud architecture, its design and our first implementation results for three cloud forms - a compute cloud, a storage cloud and a cloud-based software service- in the context of global distributed software development (GSD). Our ”compute cloud” provides computational services such as continuous code integration and a compile server farm, ”storage cloud” offers storage (block or file-based) services with an on-line virtual storage service, whereas the on-line virtual labs represent a useful cloud service. We note some of the use cases for clouds in GSD, the lessons learned with our prototypes and identify challenges that must be conquered before realizing the full business benefits. We believe that in the future, software practitioners will focus more on these cloud computing platforms and see clouds as a means to supporting a ecosystem of clients, developers and other key stakeholders.

Cloud Based Applications and Platforms (Presentation)

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

Brodt-Giles, D.

2014-05-15

Presentation to the Cloud Computing East 2014 Conference, where we are highlighting our cloud computing strategy, describing the platforms on the cloud (including Smartgrid.gov), and defining our process for implementing cloud based applications.

76 FR 52353 - Assumption Buster Workshop: “Current Implementations of Cloud Computing Indicate a New Approach...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-22

... explored in this series is cloud computing. The workshop on this topic will be held in Gaithersburg, MD on October 21, 2011. Assertion: ``Current implementations of cloud computing indicate a new approach to security'' Implementations of cloud computing have provided new ways of thinking about how to secure data...

77 FR 74829 - Notice of Public Meeting-Cloud Computing and Big Data Forum and Workshop

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-18

...--Cloud Computing and Big Data Forum and Workshop AGENCY: National Institute of Standards and Technology... Standards and Technology (NIST) announces a Cloud Computing and Big Data Forum and Workshop to be held on... followed by a one-day hands-on workshop. The NIST Cloud Computing and Big Data Forum and Workshop will...

Examining the Relationship between Technological, Organizational, and Environmental Factors and Cloud Computing Adoption

ERIC Educational Resources Information Center

Tweel, Abdeneaser

2012-01-01

High uncertainties related to cloud computing adoption may hinder IT managers from making solid decisions about adopting cloud computing. The problem addressed in this study was the lack of understanding of the relationship between factors related to the adoption of cloud computing and IT managers' interest in adopting this technology. In…

When cloud computing meets bioinformatics: a review.

PubMed

Zhou, Shuigeng; Liao, Ruiqi; Guan, Jihong

2013-10-01

In the past decades, with the rapid development of high-throughput technologies, biology research has generated an unprecedented amount of data. In order to store and process such a great amount of data, cloud computing and MapReduce were applied to many fields of bioinformatics. In this paper, we first introduce the basic concepts of cloud computing and MapReduce, and their applications in bioinformatics. We then highlight some problems challenging the applications of cloud computing and MapReduce to bioinformatics. Finally, we give a brief guideline for using cloud computing in biology research.

Phenotype Instance Verification and Evaluation Tool (PIVET): A Scaled Phenotype Evidence Generation Framework Using Web-Based Medical Literature

PubMed Central

Ke, Junyuan; Ho, Joyce C; Ghosh, Joydeep; Wallace, Byron C

2018-01-01

Background 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. Objective 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. Methods 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. Results 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. Conclusions 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. PMID:29728351

Research on private cloud computing based on analysis on typical opensource platform: a case study with Eucalyptus and Wavemaker

NASA Astrophysics Data System (ADS)

Yu, Xiaoyuan; Yuan, Jian; Chen, Shi

2013-03-01

Cloud computing is one of the most popular topics in the IT industry and is recently being adopted by many companies. It has four development models, as: public cloud, community cloud, hybrid cloud and private cloud. Except others, private cloud can be implemented in a private network, and delivers some benefits of cloud computing without pitfalls. This paper makes a comparison of typical open source platforms through which we can implement a private cloud. After this comparison, we choose Eucalyptus and Wavemaker to do a case study on the private cloud. We also do some performance estimation of cloud platform services and development of prototype software as cloud services.

Cloud4Psi: cloud computing for 3D protein structure similarity searching.

PubMed

Mrozek, Dariusz; Małysiak-Mrozek, Bożena; Kłapciński, Artur

2014-10-01

Popular methods for 3D protein structure similarity searching, especially those that generate high-quality alignments such as Combinatorial Extension (CE) and Flexible structure Alignment by Chaining Aligned fragment pairs allowing Twists (FATCAT) are still time consuming. As a consequence, performing similarity searching against large repositories of structural data requires increased computational resources that are not always available. Cloud computing provides huge amounts of computational power that can be provisioned on a pay-as-you-go basis. We have developed the cloud-based system that allows scaling of the similarity searching process vertically and horizontally. Cloud4Psi (Cloud for Protein Similarity) was tested in the Microsoft Azure cloud environment and provided good, almost linearly proportional acceleration when scaled out onto many computational units. Cloud4Psi is available as Software as a Service for testing purposes at: http://cloud4psi.cloudapp.net/. For source code and software availability, please visit the Cloud4Psi project home page at http://zti.polsl.pl/dmrozek/science/cloud4psi.htm. © The Author 2014. Published by Oxford University Press.

Cloud4Psi: cloud computing for 3D protein structure similarity searching

PubMed Central

Mrozek, Dariusz; Małysiak-Mrozek, Bożena; Kłapciński, Artur

2014-01-01

Summary: Popular methods for 3D protein structure similarity searching, especially those that generate high-quality alignments such as Combinatorial Extension (CE) and Flexible structure Alignment by Chaining Aligned fragment pairs allowing Twists (FATCAT) are still time consuming. As a consequence, performing similarity searching against large repositories of structural data requires increased computational resources that are not always available. Cloud computing provides huge amounts of computational power that can be provisioned on a pay-as-you-go basis. We have developed the cloud-based system that allows scaling of the similarity searching process vertically and horizontally. Cloud4Psi (Cloud for Protein Similarity) was tested in the Microsoft Azure cloud environment and provided good, almost linearly proportional acceleration when scaled out onto many computational units. Availability and implementation: Cloud4Psi is available as Software as a Service for testing purposes at: http://cloud4psi.cloudapp.net/. For source code and software availability, please visit the Cloud4Psi project home page at http://zti.polsl.pl/dmrozek/science/cloud4psi.htm. Contact: dariusz.mrozek@polsl.pl PMID:24930141

Cost-Effective Cloud Computing: A Case Study Using the Comparative Genomics Tool, Roundup

PubMed Central

Kudtarkar, Parul; DeLuca, Todd F.; Fusaro, Vincent A.; Tonellato, Peter J.; Wall, Dennis P.

2010-01-01

Background Comparative genomics resources, such as ortholog detection tools and repositories are rapidly increasing in scale and complexity. Cloud computing is an emerging technological paradigm that enables researchers to dynamically build a dedicated virtual cluster and may represent a valuable alternative for large computational tools in bioinformatics. In the present manuscript, we optimize the computation of a large-scale comparative genomics resource—Roundup—using cloud computing, describe the proper operating principles required to achieve computational efficiency on the cloud, and detail important procedures for improving cost-effectiveness to ensure maximal computation at minimal costs. Methods Utilizing the comparative genomics tool, Roundup, as a case study, we computed orthologs among 902 fully sequenced genomes on Amazon’s Elastic Compute Cloud. For managing the ortholog processes, we designed a strategy to deploy the web service, Elastic MapReduce, and maximize the use of the cloud while simultaneously minimizing costs. Specifically, we created a model to estimate cloud runtime based on the size and complexity of the genomes being compared that determines in advance the optimal order of the jobs to be submitted. Results We computed orthologous relationships for 245,323 genome-to-genome comparisons on Amazon’s computing cloud, a computation that required just over 200 hours and cost $8,000 USD, at least 40% less than expected under a strategy in which genome comparisons were submitted to the cloud randomly with respect to runtime. Our cost savings projections were based on a model that not only demonstrates the optimal strategy for deploying RSD to the cloud, but also finds the optimal cluster size to minimize waste and maximize usage. Our cost-reduction model is readily adaptable for other comparative genomics tools and potentially of significant benefit to labs seeking to take advantage of the cloud as an alternative to local computing infrastructure. PMID:21258651

The emerging role of cloud computing in molecular modelling.

PubMed

Ebejer, Jean-Paul; Fulle, Simone; Morris, Garrett M; Finn, Paul W

2013-07-01

There is a growing recognition of the importance of cloud computing for large-scale and data-intensive applications. The distinguishing features of cloud computing and their relationship to other distributed computing paradigms are described, as are the strengths and weaknesses of the approach. We review the use made to date of cloud computing for molecular modelling projects and the availability of front ends for molecular modelling applications. Although the use of cloud computing technologies for molecular modelling is still in its infancy, we demonstrate its potential by presenting several case studies. Rapid growth can be expected as more applications become available and costs continue to fall; cloud computing can make a major contribution not just in terms of the availability of on-demand computing power, but could also spur innovation in the development of novel approaches that utilize that capacity in more effective ways. Copyright © 2013 Elsevier Inc. All rights reserved.

An Architecture for Cross-Cloud System Management

NASA Astrophysics Data System (ADS)

Dodda, Ravi Teja; Smith, Chris; van Moorsel, Aad

The emergence of the cloud computing paradigm promises flexibility and adaptability through on-demand provisioning of compute resources. As the utilization of cloud resources extends beyond a single provider, for business as well as technical reasons, the issue of effectively managing such resources comes to the fore. Different providers expose different interfaces to their compute resources utilizing varied architectures and implementation technologies. This heterogeneity poses a significant system management problem, and can limit the extent to which the benefits of cross-cloud resource utilization can be realized. We address this problem through the definition of an architecture to facilitate the management of compute resources from different cloud providers in an homogenous manner. This preserves the flexibility and adaptability promised by the cloud computing paradigm, whilst enabling the benefits of cross-cloud resource utilization to be realized. The practical efficacy of the architecture is demonstrated through an implementation utilizing compute resources managed through different interfaces on the Amazon Elastic Compute Cloud (EC2) service. Additionally, we provide empirical results highlighting the performance differential of these different interfaces, and discuss the impact of this performance differential on efficiency and profitability.

Enabling Data Intensive Science through Service Oriented Science: Virtual Laboratories and Science Gateways

NASA Astrophysics Data System (ADS)

Lescinsky, D. T.; Wyborn, L. A.; Evans, B. J. K.; Allen, C.; Fraser, R.; Rankine, T.

2014-12-01

We present collaborative work on a generic, modular infrastructure for virtual laboratories (VLs, similar to science gateways) that combine online access to data, scientific code, and computing resources as services that support multiple data intensive scientific computing needs across a wide range of science disciplines. We are leveraging access to 10+ PB of earth science data on Lustre filesystems at Australia's National Computational Infrastructure (NCI) Research Data Storage Infrastructure (RDSI) node, co-located with NCI's 1.2 PFlop Raijin supercomputer and a 3000 CPU core research cloud. The development, maintenance and sustainability of VLs is best accomplished through modularisation and standardisation of interfaces between components. Our approach has been to break up tightly-coupled, specialised application packages into modules, with identified best techniques and algorithms repackaged either as data services or scientific tools that are accessible across domains. The data services can be used to manipulate, visualise and transform multiple data types whilst the scientific tools can be used in concert with multiple scientific codes. We are currently designing a scalable generic infrastructure that will handle scientific code as modularised services and thereby enable the rapid/easy deployment of new codes or versions of codes. The goal is to build open source libraries/collections of scientific tools, scripts and modelling codes that can be combined in specially designed deployments. Additional services in development include: provenance, publication of results, monitoring, workflow tools, etc. The generic VL infrastructure will be hosted at NCI, but can access alternative computing infrastructures (i.e., public/private cloud, HPC).The Virtual Geophysics Laboratory (VGL) was developed as a pilot project to demonstrate the underlying technology. This base is now being redesigned and generalised to develop a Virtual Hazards Impact and Risk Laboratory (VHIRL); any enhancements and new capabilities will be incorporated into a generic VL infrastructure. At same time, we are scoping seven new VLs and in the process, identifying other common components to prioritise and focus development.

Collaborative workbench for cyberinfrastructure to accelerate science algorithm development

NASA Astrophysics Data System (ADS)

Ramachandran, R.; Maskey, M.; Kuo, K.; Lynnes, C.

2013-12-01

There are significant untapped resources for information and knowledge creation within the Earth Science community in the form of data, algorithms, services, analysis workflows or scripts, and the related knowledge about these resources. Despite the huge growth in social networking and collaboration platforms, these resources often reside on an investigator's workstation or laboratory and are rarely shared. A major reason for this is that there are very few scientific collaboration platforms, and those that exist typically require the use of a new set of analysis tools and paradigms to leverage the shared infrastructure. As a result, adoption of these collaborative platforms for science research is inhibited by the high cost to an individual scientist of switching from his or her own familiar environment and set of tools to a new environment and tool set. This presentation will describe an ongoing project developing an Earth Science Collaborative Workbench (CWB). The CWB approach will eliminate this barrier by augmenting a scientist's current research environment and tool set to allow him or her to easily share diverse data and algorithms. The CWB will leverage evolving technologies such as commodity computing and social networking to design an architecture for scalable collaboration that will support the emerging vision of an Earth Science Collaboratory. The CWB is being implemented on the robust and open source Eclipse framework and will be compatible with widely used scientific analysis tools such as IDL. The myScience Catalog built into CWB will capture and track metadata and provenance about data and algorithms for the researchers in a non-intrusive manner with minimal overhead. Seamless interfaces to multiple Cloud services will support sharing algorithms, data, and analysis results, as well as access to storage and computer resources. A Community Catalog will track the use of shared science artifacts and manage collaborations among researchers.

'Cloud computing' and clinical trials: report from an ECRIN workshop.

PubMed

Ohmann, Christian; Canham, Steve; Danielyan, Edgar; Robertshaw, Steve; Legré, Yannick; Clivio, Luca; Demotes, Jacques

2015-07-29

Growing use of cloud computing in clinical trials prompted the European Clinical Research Infrastructures Network, a European non-profit organisation established to support multinational clinical research, to organise a one-day workshop on the topic to clarify potential benefits and risks. The issues that arose in that workshop are summarised and include the following: the nature of cloud computing and the cloud computing industry; the risks in using cloud computing services now; the lack of explicit guidance on this subject, both generally and with reference to clinical trials; and some possible ways of reducing risks. There was particular interest in developing and using a European 'community cloud' specifically for academic clinical trial data. It was recognised that the day-long workshop was only the start of an ongoing process. Future discussion needs to include clarification of trial-specific regulatory requirements for cloud computing and involve representatives from the relevant regulatory bodies.

BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience.

PubMed

Van Geit, Werner; Gevaert, Michael; Chindemi, Giuseppe; Rössert, Christian; Courcol, Jean-Denis; Muller, Eilif B; Schürmann, Felix; Segev, Idan; Markram, Henry

2016-01-01

At many scales in neuroscience, appropriate mathematical models take the form of complex dynamical systems. Parameterizing such models to conform to the multitude of available experimental constraints is a global non-linear optimisation problem with a complex fitness landscape, requiring numerical techniques to find suitable approximate solutions. Stochastic optimisation approaches, such as evolutionary algorithms, have been shown to be effective, but often the setting up of such optimisations and the choice of a specific search algorithm and its parameters is non-trivial, requiring domain-specific expertise. Here we describe BluePyOpt, a Python package targeted at the broad neuroscience community to simplify this task. BluePyOpt is an extensible framework for data-driven model parameter optimisation that wraps and standardizes several existing open-source tools. It simplifies the task of creating and sharing these optimisations, and the associated techniques and knowledge. This is achieved by abstracting the optimisation and evaluation tasks into various reusable and flexible discrete elements according to established best-practices. Further, BluePyOpt provides methods for setting up both small- and large-scale optimisations on a variety of platforms, ranging from laptops to Linux clusters and cloud-based compute infrastructures. The versatility of the BluePyOpt framework is demonstrated by working through three representative neuroscience specific use cases.

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.

Research on the application in disaster reduction for using cloud computing technology

NASA Astrophysics Data System (ADS)

Tao, Liang; Fan, Yida; Wang, Xingling

Cloud Computing technology has been rapidly applied in different domains recently, promotes the progress of the domain's informatization. Based on the analysis of the state of application requirement in disaster reduction and combining the characteristics of Cloud Computing technology, we present the research on the application of Cloud Computing technology in disaster reduction. First of all, we give the architecture of disaster reduction cloud, which consists of disaster reduction infrastructure as a service (IAAS), disaster reduction cloud application platform as a service (PAAS) and disaster reduction software as a service (SAAS). Secondly, we talk about the standard system of disaster reduction in five aspects. Thirdly, we indicate the security system of disaster reduction cloud. Finally, we draw a conclusion the use of cloud computing technology will help us to solve the problems for disaster reduction and promote the development of disaster reduction.

Cloud computing security.

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

Shin, Dongwan; Claycomb, William R.; Urias, Vincent E.

Cloud computing is a paradigm rapidly being embraced by government and industry as a solution for cost-savings, scalability, and collaboration. While a multitude of applications and services are available commercially for cloud-based solutions, research in this area has yet to fully embrace the full spectrum of potential challenges facing cloud computing. This tutorial aims to provide researchers with a fundamental understanding of cloud computing, with the goals of identifying a broad range of potential research topics, and inspiring a new surge in research to address current issues. We will also discuss real implementations of research-oriented cloud computing systems for bothmore » academia and government, including configuration options, hardware issues, challenges, and solutions.« less

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…

Challenges and Security in Cloud Computing

NASA Astrophysics Data System (ADS)

Chang, Hyokyung; Choi, Euiin

People who live in this world want to solve any problems as they happen then. An IT technology called Ubiquitous computing should help the situations easier and we call a technology which makes it even better and powerful cloud computing. Cloud computing, however, is at the stage of the beginning to implement and use and it faces a lot of challenges in technical matters and security issues. This paper looks at the cloud computing security.

Scaling predictive modeling in drug development with cloud computing.

PubMed

Moghadam, Behrooz Torabi; Alvarsson, Jonathan; Holm, Marcus; Eklund, Martin; Carlsson, Lars; Spjuth, Ola

2015-01-26

Growing data sets with increased time for analysis is hampering predictive modeling in drug discovery. Model building can be carried out on high-performance computer clusters, but these can be expensive to purchase and maintain. We have evaluated ligand-based modeling on cloud computing resources where computations are parallelized and run on the Amazon Elastic Cloud. We trained models on open data sets of varying sizes for the end points logP and Ames mutagenicity and compare with model building parallelized on a traditional high-performance computing cluster. We show that while high-performance computing results in faster model building, the use of cloud computing resources is feasible for large data sets and scales well within cloud instances. An additional advantage of cloud computing is that the costs of predictive models can be easily quantified, and a choice can be made between speed and economy. The easy access to computational resources with no up-front investments makes cloud computing an attractive alternative for scientists, especially for those without access to a supercomputer, and our study shows that it enables cost-efficient modeling of large data sets on demand within reasonable time.

Big data mining analysis method based on cloud computing

NASA Astrophysics Data System (ADS)

Cai, Qing Qiu; Cui, Hong Gang; Tang, Hao

2017-08-01

Information explosion era, large data super-large, discrete and non-(semi) structured features have gone far beyond the traditional data management can carry the scope of the way. With the arrival of the cloud computing era, cloud computing provides a new technical way to analyze the massive data mining, which can effectively solve the problem that the traditional data mining method cannot adapt to massive data mining. This paper introduces the meaning and characteristics of cloud computing, analyzes the advantages of using cloud computing technology to realize data mining, designs the mining algorithm of association rules based on MapReduce parallel processing architecture, and carries out the experimental verification. The algorithm of parallel association rule mining based on cloud computing platform can greatly improve the execution speed of data mining.

Charting a Security Landscape in the Clouds: Data Protection and Collaboration in Cloud Storage

DTIC Science & Technology

2016-07-01

cloud computing is perhaps the most revolutionary force in the information technology industry today. This field encompasses many different domains...characteristic shared by all cloud computing tasks is that they involve storing data in the cloud . In this report, we therefore aim to describe and rank the...CONCLUSION The advent of cloud computing has caused government organizations to rethink their IT architectures so that they can take advantage of the

A Lightweight Remote Parallel Visualization Platform for Interactive Massive Time-varying Climate Data Analysis

NASA Astrophysics Data System (ADS)

Li, J.; Zhang, T.; Huang, Q.; Liu, Q.

2014-12-01

Today's climate datasets are featured with large volume, high degree of spatiotemporal complexity and evolving fast overtime. As visualizing large volume distributed climate datasets is computationally intensive, traditional desktop based visualization applications fail to handle the computational intensity. Recently, scientists have developed remote visualization techniques to address the computational issue. Remote visualization techniques usually leverage server-side parallel computing capabilities to perform visualization tasks and deliver visualization results to clients through network. In this research, we aim to build a remote parallel visualization platform for visualizing and analyzing massive climate data. Our visualization platform was built based on Paraview, which is one of the most popular open source remote visualization and analysis applications. To further enhance the scalability and stability of the platform, we have employed cloud computing techniques to support the deployment of the platform. In this platform, all climate datasets are regular grid data which are stored in NetCDF format. Three types of data access methods are supported in the platform: accessing remote datasets provided by OpenDAP servers, accessing datasets hosted on the web visualization server and accessing local datasets. Despite different data access methods, all visualization tasks are completed at the server side to reduce the workload of clients. As a proof of concept, we have implemented a set of scientific visualization methods to show the feasibility of the platform. Preliminary results indicate that the framework can address the computation limitation of desktop based visualization applications.

Data Sets, Ensemble Cloud Computing, and the University Library (Invited)

NASA Astrophysics Data System (ADS)

Plale, B. A.

2013-12-01

The environmental researcher at the public university has new resources at their disposal to aid in research and publishing. Cloud computing provides compute cycles on demand for analysis and modeling scenarios. Cloud computing is attractive for e-Science because of the ease with which cores can be accessed on demand, and because the virtual machine implementation that underlies cloud computing reduces the cost of porting a numeric or analysis code to a new platform. At the university, many libraries at larger universities are developing the e-Science skills to serve as repositories of record for publishable data sets. But these are confusing times for the publication of data sets from environmental research. The large publishers of scientific literature are advocating a process whereby data sets are tightly tied to a publication. In other words, a paper published in the scientific literature that gives results based on data, must have an associated data set accessible that backs up the results. This approach supports reproducibility of results in that publishers maintain a repository for the papers they publish, and the data sets that the papers used. Does such a solution that maps one data set (or subset) to one paper fit the needs of the environmental researcher who among other things uses complex models, mines longitudinal data bases, and generates observational results? The second school of thought has emerged out of NSF, NOAA, and NASA funded efforts over time: data sets exist coherent at a location, such as occurs at National Snow and Ice Data Center (NSIDC). But when a collection is coherent, reproducibility of individual results is more challenging. We argue for a third complementary option: the university repository as a location for data sets produced as a result of university-based research. This location for a repository relies on the expertise developing in the university libraries across the country, and leverages tools, such as are being developed in the Sustainable Environments - Actionable Data (SEAD) project, an NSF funded DataNet partner, for reducing the burden of describing, publishing, and sharing research data. We use as example the university institutional repository (IR) and an application taken from climate studies. The application is a storm surge model running as a cloud-based software as a service (SaaS). One of the more immediate and dangerous impacts of climate change could be change in the strength of storms that form over the oceans. There have already been indications that even modest changes in ocean surface temperature can have a disproportionate effect on hurricane strength. In an effort to understand these impacts, modelers turn to predictions generated by hydrodynamic coastal ocean models such as the Sea, Lake and Overland Surges from Hurricanes (SLOSH) model. We step through a use scenario of SLOSH in emergency management. To publish a newly created data set resulting from the ensemble runs on the cloud, one needs tools that minimize the burden of describing the data. SEAD has such tools, and engages the e-Science data curation librarian in the process to aid the data set's ingest into the university IR. We finally bring attention to ongoing effort in the Research Data Alliance (RDA) to make data lifecycle issues easier for environmental researchers so that they invest less time and get more credit for their data sets, giving research wider adoption and impact.

The ATLAS Event Service: A new approach to event processing

NASA Astrophysics Data System (ADS)

Calafiura, P.; De, K.; Guan, W.; Maeno, T.; Nilsson, P.; Oleynik, D.; Panitkin, S.; Tsulaia, V.; Van Gemmeren, P.; Wenaus, T.

2015-12-01

The ATLAS Event Service (ES) implements a new fine grained approach to HEP event processing, designed to be agile and efficient in exploiting transient, short-lived resources such as HPC hole-filling, spot market commercial clouds, and volunteer computing. Input and output control and data flows, bookkeeping, monitoring, and data storage are all managed at the event level in an implementation capable of supporting ATLAS-scale distributed processing throughputs (about 4M CPU-hours/day). Input data flows utilize remote data repositories with no data locality or pre-staging requirements, minimizing the use of costly storage in favor of strongly leveraging powerful networks. Object stores provide a highly scalable means of remotely storing the quasi-continuous, fine grained outputs that give ES based applications a very light data footprint on a processing resource, and ensure negligible losses should the resource suddenly vanish. We will describe the motivations for the ES system, its unique features and capabilities, its architecture and the highly scalable tools and technologies employed in its implementation, and its applications in ATLAS processing on HPCs, commercial cloud resources, volunteer computing, and grid resources. Notice: This manuscript has been authored by employees of Brookhaven Science Associates, LLC under Contract No. DE-AC02-98CH10886 with the U.S. Department of Energy. The publisher by accepting the manuscript for publication acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.

Lightweight scheduling of elastic analysis containers in a competitive cloud environment: a Docked Analysis Facility for ALICE

NASA Astrophysics Data System (ADS)

Berzano, D.; Blomer, J.; Buncic, P.; Charalampidis, I.; Ganis, G.; Meusel, R.

2015-12-01

During the last years, several Grid computing centres chose virtualization as a better way to manage diverse use cases with self-consistent environments on the same bare infrastructure. The maturity of control interfaces (such as OpenNebula and OpenStack) opened the possibility to easily change the amount of resources assigned to each use case by simply turning on and off virtual machines. Some of those private clouds use, in production, copies of the Virtual Analysis Facility, a fully virtualized and self-contained batch analysis cluster capable of expanding and shrinking automatically upon need: however, resources starvation occurs frequently as expansion has to compete with other virtual machines running long-living batch jobs. Such batch nodes cannot relinquish their resources in a timely fashion: the more jobs they run, the longer it takes to drain them and shut off, and making one-job virtual machines introduces a non-negligible virtualization overhead. By improving several components of the Virtual Analysis Facility we have realized an experimental “Docked” Analysis Facility for ALICE, which leverages containers instead of virtual machines for providing performance and security isolation. We will present the techniques we have used to address practical problems, such as software provisioning through CVMFS, as well as our considerations on the maturity of containers for High Performance Computing. As the abstraction layer is thinner, our Docked Analysis Facilities may feature a more fine-grained sizing, down to single-job node containers: we will show how this approach will positively impact automatic cluster resizing by deploying lightweight pilot containers instead of replacing central queue polls.

Introducing Cloud Computing Topics in Curricula

ERIC Educational Resources Information Center

Chen, Ling; Liu, Yang; Gallagher, Marcus; Pailthorpe, Bernard; Sadiq, Shazia; Shen, Heng Tao; Li, Xue

2012-01-01

The demand for graduates with exposure in Cloud Computing is on the rise. For many educational institutions, the challenge is to decide on how to incorporate appropriate cloud-based technologies into their curricula. In this paper, we describe our design and experiences of integrating Cloud Computing components into seven third/fourth-year…

Bootstrapping and Maintaining Trust in the Cloud

DTIC Science & Technology

2016-12-01

simultaneous cloud nodes. 1. INTRODUCTION The proliferation and popularity of infrastructure-as-a- service (IaaS) cloud computing services such as...Amazon Web Services and Google Compute Engine means more cloud tenants are hosting sensitive, private, and business critical data and applications in the...thousands of IaaS resources as they are elastically instantiated and terminated. Prior cloud trusted computing solutions address a subset of these features

Study on the application of mobile internet cloud computing platform

NASA Astrophysics Data System (ADS)

Gong, Songchun; Fu, Songyin; Chen, Zheng

2012-04-01

The innovative development of computer technology promotes the application of the cloud computing platform, which actually is the substitution and exchange of a sort of resource service models and meets the needs of users on the utilization of different resources after changes and adjustments of multiple aspects. "Cloud computing" owns advantages in many aspects which not merely reduce the difficulties to apply the operating system and also make it easy for users to search, acquire and process the resources. In accordance with this point, the author takes the management of digital libraries as the research focus in this paper, and analyzes the key technologies of the mobile internet cloud computing platform in the operation process. The popularization and promotion of computer technology drive people to create the digital library models, and its core idea is to strengthen the optimal management of the library resource information through computers and construct an inquiry and search platform with high performance, allowing the users to access to the necessary information resources at any time. However, the cloud computing is able to promote the computations within the computers to distribute in a large number of distributed computers, and hence implement the connection service of multiple computers. The digital libraries, as a typical representative of the applications of the cloud computing, can be used to carry out an analysis on the key technologies of the cloud computing.

Integration of Cloud resources in the LHCb Distributed Computing

NASA Astrophysics Data System (ADS)

Úbeda García, Mario; Méndez Muñoz, Víctor; Stagni, Federico; Cabarrou, Baptiste; Rauschmayr, Nathalie; Charpentier, Philippe; Closier, Joel

2014-06-01

This contribution describes how Cloud resources have been integrated in the LHCb Distributed Computing. LHCb is using its specific Dirac extension (LHCbDirac) as an interware for its Distributed Computing. So far, it was seamlessly integrating Grid resources and Computer clusters. The cloud extension of DIRAC (VMDIRAC) allows the integration of Cloud computing infrastructures. It is able to interact with multiple types of infrastructures in commercial and institutional clouds, supported by multiple interfaces (Amazon EC2, OpenNebula, OpenStack and CloudStack) - instantiates, monitors and manages Virtual Machines running on this aggregation of Cloud resources. Moreover, specifications for institutional Cloud resources proposed by Worldwide LHC Computing Grid (WLCG), mainly by the High Energy Physics Unix Information Exchange (HEPiX) group, have been taken into account. Several initiatives and computing resource providers in the eScience environment have already deployed IaaS in production during 2013. Keeping this on mind, pros and cons of a cloud based infrasctructure have been studied in contrast with the current setup. As a result, this work addresses four different use cases which represent a major improvement on several levels of our infrastructure. We describe the solution implemented by LHCb for the contextualisation of the VMs based on the idea of Cloud Site. We report on operational experience of using in production several institutional Cloud resources that are thus becoming integral part of the LHCb Distributed Computing resources. Furthermore, we describe as well the gradual migration of our Service Infrastructure towards a fully distributed architecture following the Service as a Service (SaaS) model.

SPARCCS - Smartphone-Assisted Readiness, Command and Control System

DTIC Science & Technology

2012-06-01

and database needs. By doing this SPARCCS takes advantage of all the capabilities cloud computing has to offer, especially that of disbursed data...40092829/ Microsoft. (2011). Cloud Computing . Retrieved September 24, 2011, http ://www.microsoft.com/industry/government/guides/cloud_computing/2...Command, and Control System) to address these issues. We use smartphones in conjunction with cloud computing to extend the benefits of collaborative

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

The application of cloud computing to scientific workflows: a study of cost and performance.

PubMed

Berriman, G Bruce; Deelman, Ewa; Juve, Gideon; Rynge, Mats; Vöckler, Jens-S

2013-01-28

The current model of transferring data from data centres to desktops for analysis will soon be rendered impractical by the accelerating growth in the volume of science datasets. Processing will instead often take place on high-performance servers co-located with data. Evaluations of how new technologies such as cloud computing would support such a new distributed computing model are urgently needed. Cloud computing is a new way of purchasing computing and storage resources on demand through virtualization technologies. We report here the results of investigations of the applicability of commercial cloud computing to scientific computing, with an emphasis on astronomy, including investigations of what types of applications can be run cheaply and efficiently on the cloud, and an example of an application well suited to the cloud: processing a large dataset to create a new science product.

Use of cloud computing in biomedicine.

PubMed

Sobeslav, Vladimir; Maresova, Petra; Krejcar, Ondrej; Franca, Tanos C C; Kuca, Kamil

2016-12-01

Nowadays, biomedicine is characterised by a growing need for processing of large amounts of data in real time. This leads to new requirements for information and communication technologies (ICT). Cloud computing offers a solution to these requirements and provides many advantages, such as cost savings, elasticity and scalability of using ICT. The aim of this paper is to explore the concept of cloud computing and the related use of this concept in the area of biomedicine. Authors offer a comprehensive analysis of the implementation of the cloud computing approach in biomedical research, decomposed into infrastructure, platform and service layer, and a recommendation for processing large amounts of data in biomedicine. Firstly, the paper describes the appropriate forms and technological solutions of cloud computing. Secondly, the high-end computing paradigm of cloud computing aspects is analysed. Finally, the potential and current use of applications in scientific research of this technology in biomedicine is discussed.

A resource management architecture based on complex network theory in cloud computing federation

NASA Astrophysics Data System (ADS)

Zhang, Zehua; Zhang, Xuejie

2011-10-01

Cloud Computing Federation is a main trend of Cloud Computing. Resource Management has significant effect on the design, realization, and efficiency of Cloud Computing Federation. Cloud Computing Federation has the typical characteristic of the Complex System, therefore, we propose a resource management architecture based on complex network theory for Cloud Computing Federation (abbreviated as RMABC) in this paper, with the detailed design of the resource discovery and resource announcement mechanisms. Compare with the existing resource management mechanisms in distributed computing systems, a Task Manager in RMABC can use the historical information and current state data get from other Task Managers for the evolution of the complex network which is composed of Task Managers, thus has the advantages in resource discovery speed, fault tolerance and adaptive ability. The result of the model experiment confirmed the advantage of RMABC in resource discovery performance.

Evaluating the Efficacy of the Cloud for Cluster Computation

NASA Technical Reports Server (NTRS)

Knight, David; Shams, Khawaja; Chang, George; Soderstrom, Tom

2012-01-01

Computing requirements vary by industry, and it follows that NASA and other research organizations have computing demands that fall outside the mainstream. While cloud computing made rapid inroads for tasks such as powering web applications, performance issues on highly distributed tasks hindered early adoption for scientific computation. One venture to address this problem is Nebula, NASA's homegrown cloud project tasked with delivering science-quality cloud computing resources. However, another industry development is Amazon's high-performance computing (HPC) instances on Elastic Cloud Compute (EC2) that promises improved performance for cluster computation. This paper presents results from a series of benchmarks run on Amazon EC2 and discusses the efficacy of current commercial cloud technology for running scientific applications across a cluster. In particular, a 240-core cluster of cloud instances achieved 2 TFLOPS on High-Performance Linpack (HPL) at 70% of theoretical computational performance. The cluster's local network also demonstrated sub-100 ?s inter-process latency with sustained inter-node throughput in excess of 8 Gbps. Beyond HPL, a real-world Hadoop image processing task from NASA's Lunar Mapping and Modeling Project (LMMP) was run on a 29 instance cluster to process lunar and Martian surface images with sizes on the order of tens of gigapixels. These results demonstrate that while not a rival of dedicated supercomputing clusters, commercial cloud technology is now a feasible option for moderately demanding scientific workloads.

CSNS computing environment Based on OpenStack

NASA Astrophysics Data System (ADS)

Li, Yakang; Qi, Fazhi; Chen, Gang; Wang, Yanming; Hong, Jianshu

2017-10-01

Cloud computing can allow for more flexible configuration of IT resources and optimized hardware utilization, it also can provide computing service according to the real need. We are applying this computing mode to the China Spallation Neutron Source(CSNS) computing environment. So, firstly, CSNS experiment and its computing scenarios and requirements are introduced in this paper. Secondly, the design and practice of cloud computing platform based on OpenStack are mainly demonstrated from the aspects of cloud computing system framework, network, storage and so on. Thirdly, some improvments to openstack we made are discussed further. Finally, current status of CSNS cloud computing environment are summarized in the ending of this paper.

COMBAT: mobile-Cloud-based cOmpute/coMmunications infrastructure for BATtlefield applications

NASA Astrophysics Data System (ADS)

Soyata, Tolga; Muraleedharan, Rajani; Langdon, Jonathan; Funai, Colin; Ames, Scott; Kwon, Minseok; Heinzelman, Wendi

2012-05-01

The amount of data processed annually over the Internet has crossed the zetabyte boundary, yet this Big Data cannot be efficiently processed or stored using today's mobile devices. Parallel to this explosive growth in data, a substantial increase in mobile compute-capability and the advances in cloud computing have brought the state-of-the- art in mobile-cloud computing to an inflection point, where the right architecture may allow mobile devices to run applications utilizing Big Data and intensive computing. In this paper, we propose the MObile Cloud-based Hybrid Architecture (MOCHA), which formulates a solution to permit mobile-cloud computing applications such as object recognition in the battlefield by introducing a mid-stage compute- and storage-layer, called the cloudlet. MOCHA is built on the key observation that many mobile-cloud applications have the following characteristics: 1) they are compute-intensive, requiring the compute-power of a supercomputer, and 2) they use Big Data, requiring a communications link to cloud-based database sources in near-real-time. In this paper, we describe the operation of MOCHA in battlefield applications, by formulating the aforementioned mobile and cloudlet to be housed within a soldier's vest and inside a military vehicle, respectively, and enabling access to the cloud through high latency satellite links. We provide simulations using the traditional mobile-cloud approach as well as utilizing MOCHA with a mid-stage cloudlet to quantify the utility of this architecture. We show that the MOCHA platform for mobile-cloud computing promises a future for critical battlefield applications that access Big Data, which is currently not possible using existing technology.

Hybrid cloud: bridging of private and public cloud computing

NASA Astrophysics Data System (ADS)

Aryotejo, Guruh; Kristiyanto, Daniel Y.; Mufadhol

2018-05-01

Cloud Computing is quickly emerging as a promising paradigm in the recent years especially for the business sector. In addition, through cloud service providers, cloud computing is widely used by Information Technology (IT) based startup company to grow their business. However, the level of most businesses awareness on data security issues is low, since some Cloud Service Provider (CSP) could decrypt their data. Hybrid Cloud Deployment Model (HCDM) has characteristic as open source, which is one of secure cloud computing model, thus HCDM may solve data security issues. The objective of this study is to design, deploy and evaluate a HCDM as Infrastructure as a Service (IaaS). In the implementation process, Metal as a Service (MAAS) engine was used as a base to build an actual server and node. Followed by installing the vsftpd application, which serves as FTP server. In comparison with HCDM, public cloud was adopted through public cloud interface. As a result, the design and deployment of HCDM was conducted successfully, instead of having good security, HCDM able to transfer data faster than public cloud significantly. To the best of our knowledge, Hybrid Cloud Deployment model is one of secure cloud computing model due to its characteristic as open source. Furthermore, this study will serve as a base for future studies about Hybrid Cloud Deployment model which may relevant for solving big security issues of IT-based startup companies especially in Indonesia.

Cloud Computing

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

Pete Beckman and Ian Foster

Chicago Matters: Beyond Burnham (WTTW). Chicago has become a world center of "cloud computing." Argonne experts Pete Beckman and Ian Foster explain what "cloud computing" is and how you probably already use it on a daily basis.

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.

Aerosol Direct Radiative Effects and Heating in the New Era of Active Satellite Observations

NASA Astrophysics Data System (ADS)

Matus, Alexander V.

Atmospheric aerosols impact the global energy budget by scattering and absorbing solar radiation. Despite their impacts, aerosols remain a significant source of uncertainty in our ability to predict future climate. Multi-sensor observations from the A-Train satellite constellation provide valuable observational constraints necessary to reduce uncertainties in model simulations of aerosol direct effects. This study will discuss recent efforts to quantify aerosol direct effects globally and regionally using CloudSat's radiative fluxes and heating rates product. Improving upon previous techniques, this approach leverages the capability of CloudSat and CALIPSO to retrieve vertically resolved estimates of cloud and aerosol properties critical for accurately evaluating the radiative impacts of aerosols. We estimate the global annual mean aerosol direct effect to be -1.9 +/- 0.6 W/m2, which is in better agreement with previously published estimates from global models than previous satellite-based estimates. Detailed comparisons against a fully coupled simulation of the Community Earth System Model, however, reveal that this agreement on the global annual mean masks large regional discrepancies between modeled and observed estimates of aerosol direct effects related to model biases in cloud cover. A low bias in stratocumulus cloud cover over the southeastern Pacific Ocean, for example, leads to an overestimate of the radiative effects of marine aerosols. Stratocumulus clouds over the southeastern Atlantic Ocean can enhance aerosol absorption by 50% allowing aerosol layers to remain self-lofted in an area of subsidence. Aerosol heating is found to peak at 0.6 +/- 0.3 K/day an altitude of 4 km in September when biomass burning reaches a maximum. Finally, the contributions of observed aerosols components are evaluated to estimate the direct radiative forcing of anthropogenic aerosols. Aerosol forcing is computed using satellite-based radiative kernels that describe the sensitivity of shortwave fluxes in response to aerosol optical depth. The direct radiative forcing is estimated to be -0.21 W/m2 with the largest contributions from pollution that is partially offset by a positive forcing from smoke aerosols. The results from these analyses provide new benchmarks on the global radiative effects of aerosols and offer new insights for improving future assessments.

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.

Galaxy CloudMan: delivering cloud compute clusters.

PubMed

Afgan, Enis; Baker, Dannon; Coraor, Nate; Chapman, Brad; Nekrutenko, Anton; Taylor, James

2010-12-21

Widespread adoption of high-throughput sequencing has greatly increased the scale and sophistication of computational infrastructure needed to perform genomic research. An alternative to building and maintaining local infrastructure is "cloud computing", which, in principle, offers on demand access to flexible computational infrastructure. However, cloud computing resources are not yet suitable for immediate "as is" use by experimental biologists. We present a cloud resource management system that makes it possible for individual researchers to compose and control an arbitrarily sized compute cluster on Amazon's EC2 cloud infrastructure without any informatics requirements. Within this system, an entire suite of biological tools packaged by the NERC Bio-Linux team (http://nebc.nerc.ac.uk/tools/bio-linux) is available for immediate consumption. The provided solution makes it possible, using only a web browser, to create a completely configured compute cluster ready to perform analysis in less than five minutes. Moreover, we provide an automated method for building custom deployments of cloud resources. This approach promotes reproducibility of results and, if desired, allows individuals and labs to add or customize an otherwise available cloud system to better meet their needs. The expected knowledge and associated effort with deploying a compute cluster in the Amazon EC2 cloud is not trivial. The solution presented in this paper eliminates these barriers, making it possible for researchers to deploy exactly the amount of computing power they need, combined with a wealth of existing analysis software, to handle the ongoing data deluge.

Dynamic electronic institutions in agent oriented cloud robotic systems.

PubMed

Nagrath, Vineet; Morel, Olivier; Malik, Aamir; Saad, Naufal; Meriaudeau, Fabrice

2015-01-01

The dot-com bubble bursted in the year 2000 followed by a swift movement towards resource virtualization and cloud computing business model. Cloud computing emerged not as new form of computing or network technology but a mere remoulding of existing technologies to suit a new business model. Cloud robotics is understood as adaptation of cloud computing ideas for robotic applications. Current efforts in cloud robotics stress upon developing robots that utilize computing and service infrastructure of the cloud, without debating on the underlying business model. HTM5 is an OMG's MDA based Meta-model for agent oriented development of cloud robotic systems. The trade-view of HTM5 promotes peer-to-peer trade amongst software agents. HTM5 agents represent various cloud entities and implement their business logic on cloud interactions. Trade in a peer-to-peer cloud robotic system is based on relationships and contracts amongst several agent subsets. Electronic Institutions are associations of heterogeneous intelligent agents which interact with each other following predefined norms. In Dynamic Electronic Institutions, the process of formation, reformation and dissolution of institutions is automated leading to run time adaptations in groups of agents. DEIs in agent oriented cloud robotic ecosystems bring order and group intellect. This article presents DEI implementations through HTM5 methodology.

In the Clouds: The Implications of Cloud Computing for Higher Education Information Technology Governance and Decision Making

ERIC Educational Resources Information Center

Dulaney, Malik H.

2013-01-01

Emerging technologies challenge the management of information technology in organizations. Paradigm changing technologies, such as cloud computing, have the ability to reverse the norms in organizational management, decision making, and information technology governance. This study explores the effects of cloud computing on information technology…

Factors Influencing the Adoption of Cloud Computing by Decision Making Managers

ERIC Educational Resources Information Center

Ross, Virginia Watson

2010-01-01

Cloud computing is a growing field, addressing the market need for access to computing resources to meet organizational computing requirements. The purpose of this research is to evaluate the factors that influence an organization in their decision whether to adopt cloud computing as a part of their strategic information technology planning.…

A General Cross-Layer Cloud Scheduling Framework for Multiple IoT Computer Tasks.

PubMed

Wu, Guanlin; Bao, Weidong; Zhu, Xiaomin; Zhang, Xiongtao

2018-05-23

The diversity of IoT services and applications brings enormous challenges to improving the performance of multiple computer tasks' scheduling in cross-layer cloud computing systems. Unfortunately, the commonly-employed frameworks fail to adapt to the new patterns on the cross-layer cloud. To solve this issue, we design a new computer task scheduling framework for multiple IoT services in cross-layer cloud computing systems. Specifically, we first analyze the features of the cross-layer cloud and computer tasks. Then, we design the scheduling framework based on the analysis and present detailed models to illustrate the procedures of using the framework. With the proposed framework, the IoT services deployed in cross-layer cloud computing systems can dynamically select suitable algorithms and use resources more effectively to finish computer tasks with different objectives. Finally, the algorithms are given based on the framework, and extensive experiments are also given to validate its effectiveness, as well as its superiority.

Design for Run-Time Monitor on Cloud Computing

NASA Astrophysics Data System (ADS)

Kang, Mikyung; Kang, Dong-In; Yun, Mira; Park, Gyung-Leen; Lee, Junghoon

Cloud computing is a new information technology trend that moves computing and data away from desktops and portable PCs into large data centers. The basic principle of cloud computing is to deliver applications as services over the Internet as well as infrastructure. A cloud is the type of a parallel and distributed system consisting of a collection of inter-connected and virtualized computers that are dynamically provisioned and presented as one or more unified computing resources. The large-scale distributed applications on a cloud require adaptive service-based software, which has the capability of monitoring the system status change, analyzing the monitored information, and adapting its service configuration while considering tradeoffs among multiple QoS features simultaneously. In this paper, we design Run-Time Monitor (RTM) which is a system software to monitor the application behavior at run-time, analyze the collected information, and optimize resources on cloud computing. RTM monitors application software through library instrumentation as well as underlying hardware through performance counter optimizing its computing configuration based on the analyzed data.

Research on phone contacts online status based on mobile cloud computing

NASA Astrophysics Data System (ADS)

Wang, Wen-jinga; Ge, Weib

2013-03-01

Because the limited ability of storage space, CPU processing on mobile phone, it is difficult to realize complex applications on mobile phones, but along with the development of cloud computing, we can place the computing and storage in the clouds, provide users with rich cloud services, helping users complete various function through the browser has become the trend for future mobile communication. This article is taking the mobile phone contacts online status as an example to analysis the development and application of mobile cloud computing.

Bootstrapping and Maintaining Trust in the Cloud

DTIC Science & Technology

2016-12-01

proliferation and popularity of infrastructure-as-a- service (IaaS) cloud computing services such as Amazon Web Services and Google Compute Engine means...IaaS trusted computing system: • Secure Bootstrapping – the system should enable the tenant to securely install an initial root secret into each cloud ...elastically instantiated and terminated. Prior cloud trusted computing solutions address a subset of these features, but none achieve all. Excalibur [31] sup

Fragment assignment in the cloud with eXpress-D

PubMed Central

2013-01-01

Background Probabilistic assignment of ambiguously mapped fragments produced by high-throughput sequencing experiments has been demonstrated to greatly improve accuracy in the analysis of RNA-Seq and ChIP-Seq, and is an essential step in many other sequence census experiments. A maximum likelihood method using the expectation-maximization (EM) algorithm for optimization is commonly used to solve this problem. However, batch EM-based approaches do not scale well with the size of sequencing datasets, which have been increasing dramatically over the past few years. Thus, current approaches to fragment assignment rely on heuristics or approximations for tractability. Results We present an implementation of a distributed EM solution to the fragment assignment problem using Spark, a data analytics framework that can scale by leveraging compute clusters within datacenters–“the cloud”. We demonstrate that our implementation easily scales to billions of sequenced fragments, while providing the exact maximum likelihood assignment of ambiguous fragments. The accuracy of the method is shown to be an improvement over the most widely used tools available and can be run in a constant amount of time when cluster resources are scaled linearly with the amount of input data. Conclusions The cloud offers one solution for the difficulties faced in the analysis of massive high-thoughput sequencing data, which continue to grow rapidly. Researchers in bioinformatics must follow developments in distributed systems–such as new frameworks like Spark–for ways to port existing methods to the cloud and help them scale to the datasets of the future. Our software, eXpress-D, is freely available at: http://github.com/adarob/express-d. PMID:24314033

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.

Evaluating open-source cloud computing solutions for geosciences

NASA Astrophysics Data System (ADS)

Huang, Qunying; Yang, Chaowei; Liu, Kai; Xia, Jizhe; Xu, Chen; Li, Jing; Gui, Zhipeng; Sun, Min; Li, Zhenglong

2013-09-01

Many organizations start to adopt cloud computing for better utilizing computing resources by taking advantage of its scalability, cost reduction, and easy to access characteristics. Many private or community cloud computing platforms are being built using open-source cloud solutions. However, little has been done to systematically compare and evaluate the features and performance of open-source solutions in supporting Geosciences. This paper provides a comprehensive study of three open-source cloud solutions, including OpenNebula, Eucalyptus, and CloudStack. We compared a variety of features, capabilities, technologies and performances including: (1) general features and supported services for cloud resource creation and management, (2) advanced capabilities for networking and security, and (3) the performance of the cloud solutions in provisioning and operating the cloud resources as well as the performance of virtual machines initiated and managed by the cloud solutions in supporting selected geoscience applications. Our study found that: (1) no significant performance differences in central processing unit (CPU), memory and I/O of virtual machines created and managed by different solutions, (2) OpenNebula has the fastest internal network while both Eucalyptus and CloudStack have better virtual machine isolation and security strategies, (3) Cloudstack has the fastest operations in handling virtual machines, images, snapshots, volumes and networking, followed by OpenNebula, and (4) the selected cloud computing solutions are capable for supporting concurrent intensive web applications, computing intensive applications, and small-scale model simulations without intensive data communication.

Cloud Collaboration: Cloud-Based Instruction for Business Writing Class

ERIC Educational Resources Information Center

Lin, Charlie; Yu, Wei-Chieh Wayne; Wang, Jenny

2014-01-01

Cloud computing technologies, such as Google Docs, Adobe Creative Cloud, Dropbox, and Microsoft Windows Live, have become increasingly appreciated to the next generation digital learning tools. Cloud computing technologies encourage students' active engagement, collaboration, and participation in their learning, facilitate group work, and support…

RAPPORT: running scientific high-performance computing applications on the cloud.

PubMed

Cohen, Jeremy; Filippis, Ioannis; Woodbridge, Mark; Bauer, Daniela; Hong, Neil Chue; Jackson, Mike; Butcher, Sarah; Colling, David; Darlington, John; Fuchs, Brian; Harvey, Matt

2013-01-28

Cloud computing infrastructure is now widely used in many domains, but one area where there has been more limited adoption is research computing, in particular for running scientific high-performance computing (HPC) software. The Robust Application Porting for HPC in the Cloud (RAPPORT) project took advantage of existing links between computing researchers and application scientists in the fields of bioinformatics, high-energy physics (HEP) and digital humanities, to investigate running a set of scientific HPC applications from these domains on cloud infrastructure. In this paper, we focus on the bioinformatics and HEP domains, describing the applications and target cloud platforms. We conclude that, while there are many factors that need consideration, there is no fundamental impediment to the use of cloud infrastructure for running many types of HPC applications and, in some cases, there is potential for researchers to benefit significantly from the flexibility offered by cloud platforms.

Security model for VM in cloud

NASA Astrophysics Data System (ADS)

Kanaparti, Venkataramana; Naveen K., R.; Rajani, S.; Padmvathamma, M.; Anitha, C.

2013-03-01

Cloud computing is a new approach emerged to meet ever-increasing demand for computing resources and to reduce operational costs and Capital Expenditure for IT services. As this new way of computation allows data and applications to be stored away from own corporate server, it brings more issues in security such as virtualization security, distributed computing, application security, identity management, access control and authentication. Even though Virtualization forms the basis for cloud computing it poses many threats in securing cloud. As most of Security threats lies at Virtualization layer in cloud we proposed this new Security Model for Virtual Machine in Cloud (SMVC) in which every process is authenticated by Trusted-Agent (TA) in Hypervisor as well as in VM. Our proposed model is designed to with-stand attacks by unauthorized process that pose threat to applications related to Data Mining, OLAP systems, Image processing which requires huge resources in cloud deployed on one or more VM's.

The Geo Data Portal an Example Physical and Application Architecture Demonstrating the Power of the "Cloud" Concept.

NASA Astrophysics Data System (ADS)

Blodgett, D. L.; Booth, N.; Walker, J.; Kunicki, T.

2012-12-01

The U.S. Geological Survey Center for Integrated Data Analytics (CIDA), in holding with the President's Digital Government Strategy and the Department of Interior's IT Transformation initiative, has evolved its data center and application architecture toward the "cloud" paradigm. In this case, "cloud" refers to a goal of developing services that may be distributed to infrastructure anywhere on the Internet. This transition has taken place across the entire data management spectrum from data center location to physical hardware configuration to software design and implementation. In CIDA's case, physical hardware resides in Madison at the Wisconsin Water Science Center, in South Dakota at the Earth Resources Observation and Science Center (EROS), and in the near future at a DOI approved commercial vendor. Tasks normally conducted on desktop-based GIS software with local copies of data in proprietary formats are now done using browser-based interfaces to web processing services drawing on a network of standard data-source web services. Organizations are gaining economies of scale through data center consolidation and the creation of private cloud services as well as taking advantage of the commoditization of data processing services. Leveraging open standards for data and data management take advantage of this commoditization and provide the means to reliably build distributed service based systems. This presentation will use CIDA's experience as an illustration of the benefits and hurdles of moving to the cloud. Replicating, reformatting, and processing large data sets, such as downscaled climate projections, traditionally present a substantial challenge to environmental science researchers who need access to data subsets and derived products. The USGS Geo Data Portal (GDP) project uses cloud concepts to help earth system scientists' access subsets, spatial summaries, and derivatives of commonly needed very large data. The GDP project has developed a reusable architecture and advanced processing services that currently accesses archives hosted at Lawrence Livermore National Lab, Oregon State University, the University Corporation for Atmospheric Research, and the U.S. Geological Survey, among others. Several examples of how the GDP project uses cloud concepts will be highlighted in this presentation: 1) The high bandwidth network connectivity of large data centers reduces the need for data replication and storage local to processing services. 2) Standard data serving web services, like OPeNDAP, Web Coverage Services, and Web Feature Services allow GDP services to remotely access custom subsets of data in a variety of formats, further reducing the need for data replication and reformatting. 3) The GDP services use standard web service APIs to allow browser-based user interfaces to run complex and compute-intensive processes for users from any computer with an Internet connection. The combination of physical infrastructure and application architecture implemented for the Geo Data Portal project offer an operational example of how distributed data and processing on the cloud can be used to aid earth system science.

A Quantitative Risk Analysis Framework for Evaluating and Monitoring Operational Reliability of Cloud Computing

ERIC Educational Resources Information Center

Islam, Muhammad Faysal

2013-01-01

Cloud computing offers the advantage of on-demand, reliable and cost efficient computing solutions without the capital investment and management resources to build and maintain in-house data centers and network infrastructures. Scalability of cloud solutions enable consumers to upgrade or downsize their services as needed. In a cloud environment,…

Cloud Computing for Pharmacometrics: Using AWS, NONMEM, PsN, Grid Engine, and Sonic

PubMed Central

Sanduja, S; Jewell, P; Aron, E; Pharai, N

2015-01-01

Cloud computing allows pharmacometricians to access advanced hardware, network, and security resources available to expedite analysis and reporting. Cloud-based computing environments are available at a fraction of the time and effort when compared to traditional local datacenter-based solutions. This tutorial explains how to get started with building your own personal cloud computer cluster using Amazon Web Services (AWS), NONMEM, PsN, Grid Engine, and Sonic. PMID:26451333

Cloud Computing for Pharmacometrics: Using AWS, NONMEM, PsN, Grid Engine, and Sonic.

PubMed

Sanduja, S; Jewell, P; Aron, E; Pharai, N

2015-09-01

Cloud computing allows pharmacometricians to access advanced hardware, network, and security resources available to expedite analysis and reporting. Cloud-based computing environments are available at a fraction of the time and effort when compared to traditional local datacenter-based solutions. This tutorial explains how to get started with building your own personal cloud computer cluster using Amazon Web Services (AWS), NONMEM, PsN, Grid Engine, and Sonic.

Secure data sharing in public cloud

NASA Astrophysics Data System (ADS)

Venkataramana, Kanaparti; Naveen Kumar, R.; Tatekalva, Sandhya; Padmavathamma, M.

2012-04-01

Secure multi-party protocols have been proposed for entities (organizations or individuals) that don't fully trust each other to share sensitive information. Many types of entities need to collect, analyze, and disseminate data rapidly and accurately, without exposing sensitive information to unauthorized or untrusted parties. Solutions based on secure multiparty computation guarantee privacy and correctness, at an extra communication (too costly in communication to be practical) and computation cost. The high overhead motivates us to extend this SMC to cloud environment which provides large computation and communication capacity which makes SMC to be used between multiple clouds (i.e., it may between private or public or hybrid clouds).Cloud may encompass many high capacity servers which acts as a hosts which participate in computation (IaaS and PaaS) for final result, which is controlled by Cloud Trusted Authority (CTA) for secret sharing within the cloud. The communication between two clouds is controlled by High Level Trusted Authority (HLTA) which is one of the hosts in a cloud which provides MgaaS (Management as a Service). Due to high risk for security in clouds, HLTA generates and distributes public keys and private keys by using Carmichael-R-Prime- RSA algorithm for exchange of private data in SMC between itself and clouds. In cloud, CTA creates Group key for Secure communication between the hosts in cloud based on keys sent by HLTA for exchange of Intermediate values and shares for computation of final result. Since this scheme is extended to be used in clouds( due to high availability and scalability to increase computation power) it is possible to implement SMC practically for privacy preserving in data mining at low cost for the clients.

Applications integration in a hybrid cloud computing environment: modelling and platform

NASA Astrophysics Data System (ADS)

Li, Qing; Wang, Ze-yuan; Li, Wei-hua; Li, Jun; Wang, Cheng; Du, Rui-yang

2013-08-01

With the development of application services providers and cloud computing, more and more small- and medium-sized business enterprises use software services and even infrastructure services provided by professional information service companies to replace all or part of their information systems (ISs). These information service companies provide applications, such as data storage, computing processes, document sharing and even management information system services as public resources to support the business process management of their customers. However, no cloud computing service vendor can satisfy the full functional IS requirements of an enterprise. As a result, enterprises often have to simultaneously use systems distributed in different clouds and their intra enterprise ISs. Thus, this article presents a framework to integrate applications deployed in public clouds and intra ISs. A run-time platform is developed and a cross-computing environment process modelling technique is also developed to improve the feasibility of ISs under hybrid cloud computing environments.

Lost in Cloud

NASA Technical Reports Server (NTRS)

Maluf, David A.; Shetye, Sandeep D.; Chilukuri, Sri; Sturken, Ian

2012-01-01

Cloud computing can reduce cost significantly because businesses can share computing resources. In recent years Small and Medium Businesses (SMB) have used Cloud effectively for cost saving and for sharing IT expenses. With the success of SMBs, many perceive that the larger enterprises ought to move into Cloud environment as well. Government agency s stove-piped environments are being considered as candidates for potential use of Cloud either as an enterprise entity or pockets of small communities. Cloud Computing is the delivery of computing as a service rather than as a product, whereby shared resources, software, and information are provided to computers and other devices as a utility over a network. Underneath the offered services, there exists a modern infrastructure cost of which is often spread across its services or its investors. As NASA is considered as an Enterprise class organization, like other enterprises, a shift has been occurring in perceiving its IT services as candidates for Cloud services. This paper discusses market trends in cloud computing from an enterprise angle and then addresses the topic of Cloud Computing for NASA in two possible forms. First, in the form of a public Cloud to support it as an enterprise, as well as to share it with the commercial and public at large. Second, as a private Cloud wherein the infrastructure is operated solely for NASA, whether managed internally or by a third-party and hosted internally or externally. The paper addresses the strengths and weaknesses of both paradigms of public and private Clouds, in both internally and externally operated settings. The content of the paper is from a NASA perspective but is applicable to any large enterprise with thousands of employees and contractors.

Securing the Data Storage and Processing in Cloud Computing Environment

ERIC Educational Resources Information Center

Owens, Rodney

2013-01-01

Organizations increasingly utilize cloud computing architectures to reduce costs and energy consumption both in the data warehouse and on mobile devices by better utilizing the computing resources available. However, the security and privacy issues with publicly available cloud computing infrastructures have not been studied to a sufficient depth…

A Comprehensive Toolset for General-Purpose Private Computing and Outsourcing

DTIC Science & Technology

2016-12-08

project and scientific advances made towards each of the research thrusts throughout the project duration. 1 Project Objectives Cloud computing enables...possibilities that the cloud enables is computation outsourcing, when the client can utilize any necessary computing resources for its computational task...Security considerations, however, stand on the way of harnessing the full benefits of cloud computing to the fullest extent and prevent clients from

Galaxy CloudMan: delivering cloud compute clusters

PubMed Central

2010-01-01

Background Widespread adoption of high-throughput sequencing has greatly increased the scale and sophistication of computational infrastructure needed to perform genomic research. An alternative to building and maintaining local infrastructure is “cloud computing”, which, in principle, offers on demand access to flexible computational infrastructure. However, cloud computing resources are not yet suitable for immediate “as is” use by experimental biologists. Results We present a cloud resource management system that makes it possible for individual researchers to compose and control an arbitrarily sized compute cluster on Amazon’s EC2 cloud infrastructure without any informatics requirements. Within this system, an entire suite of biological tools packaged by the NERC Bio-Linux team (http://nebc.nerc.ac.uk/tools/bio-linux) is available for immediate consumption. The provided solution makes it possible, using only a web browser, to create a completely configured compute cluster ready to perform analysis in less than five minutes. Moreover, we provide an automated method for building custom deployments of cloud resources. This approach promotes reproducibility of results and, if desired, allows individuals and labs to add or customize an otherwise available cloud system to better meet their needs. Conclusions The expected knowledge and associated effort with deploying a compute cluster in the Amazon EC2 cloud is not trivial. The solution presented in this paper eliminates these barriers, making it possible for researchers to deploy exactly the amount of computing power they need, combined with a wealth of existing analysis software, to handle the ongoing data deluge. PMID:21210983

Leveraging Existing Heritage Documentation for Animations: Senate Virtual Tour

NASA Astrophysics Data System (ADS)

Dhanda, A.; Fai, S.; Graham, K.; Walczak, G.

2017-08-01

The use of digital documentation techniques has led to an increase in opportunities for using documentation data for valorization purposes, in addition to technical purposes. Likewise, building information models (BIMs) made from these data sets hold valuable information that can be as effective for public education as it is for rehabilitation. A BIM can reveal the elements of a building, as well as the different stages of a building over time. Valorizing this information increases the possibility for public engagement and interest in a heritage place. Digital data sets were leveraged by the Carleton Immersive Media Studio (CIMS) for parts of a virtual tour of the Senate of Canada. For the tour, workflows involving four different programs were explored to determine an efficient and effective way to leverage the existing documentation data to create informative and visually enticing animations for public dissemination: Autodesk Revit, Enscape, Autodesk 3ds Max, and Bentley Pointools. The explored workflows involve animations of point clouds, BIMs, and a combination of the two.

Developing cloud applications using the e-Science Central platform.

PubMed

Hiden, Hugo; Woodman, Simon; Watson, Paul; Cala, Jacek

2013-01-28

This paper describes the e-Science Central (e-SC) cloud data processing system and its application to a number of e-Science projects. e-SC provides both software as a service (SaaS) and platform as a service for scientific data management, analysis and collaboration. It is a portable system and can be deployed on both private (e.g. Eucalyptus) and public clouds (Amazon AWS and Microsoft Windows Azure). The SaaS application allows scientists to upload data, edit and run workflows and share results in the cloud, using only a Web browser. It is underpinned by a scalable cloud platform consisting of a set of components designed to support the needs of scientists. The platform is exposed to developers so that they can easily upload their own analysis services into the system and make these available to other users. A representational state transfer-based application programming interface (API) is also provided so that external applications can leverage the platform's functionality, making it easier to build scalable, secure cloud-based applications. This paper describes the design of e-SC, its API and its use in three different case studies: spectral data visualization, medical data capture and analysis, and chemical property prediction.

Developing cloud applications using the e-Science Central platform

PubMed Central

Hiden, Hugo; Woodman, Simon; Watson, Paul; Cala, Jacek

2013-01-01

This paper describes the e-Science Central (e-SC) cloud data processing system and its application to a number of e-Science projects. e-SC provides both software as a service (SaaS) and platform as a service for scientific data management, analysis and collaboration. It is a portable system and can be deployed on both private (e.g. Eucalyptus) and public clouds (Amazon AWS and Microsoft Windows Azure). The SaaS application allows scientists to upload data, edit and run workflows and share results in the cloud, using only a Web browser. It is underpinned by a scalable cloud platform consisting of a set of components designed to support the needs of scientists. The platform is exposed to developers so that they can easily upload their own analysis services into the system and make these available to other users. A representational state transfer-based application programming interface (API) is also provided so that external applications can leverage the platform's functionality, making it easier to build scalable, secure cloud-based applications. This paper describes the design of e-SC, its API and its use in three different case studies: spectral data visualization, medical data capture and analysis, and chemical property prediction. PMID:23230161

Security Risks of Cloud Computing and Its Emergence as 5th Utility Service

NASA Astrophysics Data System (ADS)

Ahmad, Mushtaq

Cloud Computing is being projected by the major cloud services provider IT companies such as IBM, Google, Yahoo, Amazon and others as fifth utility where clients will have access for processing those applications and or software projects which need very high processing speed for compute intensive and huge data capacity for scientific, engineering research problems and also e- business and data content network applications. These services for different types of clients are provided under DASM-Direct Access Service Management based on virtualization of hardware, software and very high bandwidth Internet (Web 2.0) communication. The paper reviews these developments for Cloud Computing and Hardware/Software configuration of the cloud paradigm. The paper also examines the vital aspects of security risks projected by IT Industry experts, cloud clients. The paper also highlights the cloud provider's response to cloud security risks.

CloVR: a virtual machine for automated and portable sequence analysis from the desktop using cloud computing.

PubMed

Angiuoli, Samuel V; Matalka, Malcolm; Gussman, Aaron; Galens, Kevin; Vangala, Mahesh; Riley, David R; Arze, Cesar; White, James R; White, Owen; Fricke, W Florian

2011-08-30

Next-generation sequencing technologies have decentralized sequence acquisition, increasing the demand for new bioinformatics tools that are easy to use, portable across multiple platforms, and scalable for high-throughput applications. Cloud computing platforms provide on-demand access to computing infrastructure over the Internet and can be used in combination with custom built virtual machines to distribute pre-packaged with pre-configured software. We describe the Cloud Virtual Resource, CloVR, a new desktop application for push-button automated sequence analysis that can utilize cloud computing resources. CloVR is implemented as a single portable virtual machine (VM) that provides several automated analysis pipelines for microbial genomics, including 16S, whole genome and metagenome sequence analysis. The CloVR VM runs on a personal computer, utilizes local computer resources and requires minimal installation, addressing key challenges in deploying bioinformatics workflows. In addition CloVR supports use of remote cloud computing resources to improve performance for large-scale sequence processing. In a case study, we demonstrate the use of CloVR to automatically process next-generation sequencing data on multiple cloud computing platforms. The CloVR VM and associated architecture lowers the barrier of entry for utilizing complex analysis protocols on both local single- and multi-core computers and cloud systems for high throughput data processing.

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.

Application verification research of cloud computing technology in the field of real time aerospace experiment

NASA Astrophysics Data System (ADS)

Wan, Junwei; Chen, Hongyan; Zhao, Jing

2017-08-01

According to the requirements of real-time, reliability and safety for aerospace experiment, the single center cloud computing technology application verification platform is constructed. At the IAAS level, the feasibility of the cloud computing technology be applied to the field of aerospace experiment is tested and verified. Based on the analysis of the test results, a preliminary conclusion is obtained: Cloud computing platform can be applied to the aerospace experiment computing intensive business. For I/O intensive business, it is recommended to use the traditional physical machine.

Formal Specification and Analysis of Cloud Computing Management

DTIC Science & Technology

2012-01-24

te r Cloud Computing in a Nutshell We begin this introduction to Cloud Computing with a famous quote by Larry Ellison: “The interesting thing about...the wording of some of our ads.” — Larry Ellison, Oracle CEO [106] In view of this statement, we summarize the essential aspects of Cloud Computing...1] M. Abadi, M. Burrows , M. Manasse, and T. Wobber. Moderately hard, memory-bound functions. ACM Transactions on Internet Technology, 5(2):299–327

A Test-Bed of Secure Mobile Cloud Computing for Military Applications

DTIC Science & Technology

2016-09-13

searching databases. This kind of applications is a typical example of mobile cloud computing (MCC). MCC has lots of applications in the military...Release; Distribution Unlimited UU UU UU UU 13-09-2016 1-Aug-2014 31-Jul-2016 Final Report: A Test-bed of Secure Mobile Cloud Computing for Military...Army Research Office P.O. Box 12211 Research Triangle Park, NC 27709-2211 Test-bed, Mobile Cloud Computing , Security, Military Applications REPORT

Cloud computing can simplify HIT infrastructure management.

PubMed

Glaser, John

2011-08-01

Software as a Service (SaaS), built on cloud computing technology, is emerging as the forerunner in IT infrastructure because it helps healthcare providers reduce capital investments. Cloud computing leads to predictable, monthly, fixed operating expenses for hospital IT staff. Outsourced cloud computing facilities are state-of-the-art data centers boasting some of the most sophisticated networking equipment on the market. The SaaS model helps hospitals safeguard against technology obsolescence, minimizes maintenance requirements, and simplifies management.

A Weibull distribution accrual failure detector for cloud computing.

PubMed

Liu, Jiaxi; Wu, Zhibo; Wu, Jin; Dong, Jian; Zhao, Yao; Wen, Dongxin

2017-01-01

Failure detectors are used to build high availability distributed systems as the fundamental component. To meet the requirement of a complicated large-scale distributed system, accrual failure detectors that can adapt to multiple applications have been studied extensively. However, several implementations of accrual failure detectors do not adapt well to the cloud service environment. To solve this problem, a new accrual failure detector based on Weibull Distribution, called the Weibull Distribution Failure Detector, has been proposed specifically for cloud computing. It can adapt to the dynamic and unexpected network conditions in cloud computing. The performance of the Weibull Distribution Failure Detector is evaluated and compared based on public classical experiment data and cloud computing experiment data. The results show that the Weibull Distribution Failure Detector has better performance in terms of speed and accuracy in unstable scenarios, especially in cloud computing.

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…

Design and Development of a Run-Time Monitor for Multi-Core Architectures in Cloud Computing

PubMed Central

Kang, Mikyung; Kang, Dong-In; Crago, Stephen P.; Park, Gyung-Leen; Lee, Junghoon

2011-01-01

Cloud computing is a new information technology trend that moves computing and data away from desktops and portable PCs into large data centers. The basic principle of cloud computing is to deliver applications as services over the Internet as well as infrastructure. A cloud is a type of parallel and distributed system consisting of a collection of inter-connected and virtualized computers that are dynamically provisioned and presented as one or more unified computing resources. The large-scale distributed applications on a cloud require adaptive service-based software, which has the capability of monitoring system status changes, analyzing the monitored information, and adapting its service configuration while considering tradeoffs among multiple QoS features simultaneously. In this paper, we design and develop a Run-Time Monitor (RTM) which is a system software to monitor the application behavior at run-time, analyze the collected information, and optimize cloud computing resources for multi-core architectures. RTM monitors application software through library instrumentation as well as underlying hardware through a performance counter optimizing its computing configuration based on the analyzed data. PMID:22163811

Design and development of a run-time monitor for multi-core architectures in cloud computing.

PubMed

Kang, Mikyung; Kang, Dong-In; Crago, Stephen P; Park, Gyung-Leen; Lee, Junghoon

2011-01-01

Cloud computing is a new information technology trend that moves computing and data away from desktops and portable PCs into large data centers. The basic principle of cloud computing is to deliver applications as services over the Internet as well as infrastructure. A cloud is a type of parallel and distributed system consisting of a collection of inter-connected and virtualized computers that are dynamically provisioned and presented as one or more unified computing resources. The large-scale distributed applications on a cloud require adaptive service-based software, which has the capability of monitoring system status changes, analyzing the monitored information, and adapting its service configuration while considering tradeoffs among multiple QoS features simultaneously. In this paper, we design and develop a Run-Time Monitor (RTM) which is a system software to monitor the application behavior at run-time, analyze the collected information, and optimize cloud computing resources for multi-core architectures. RTM monitors application software through library instrumentation as well as underlying hardware through a performance counter optimizing its computing configuration based on the analyzed data.

Challenges and opportunities of cloud computing for atmospheric sciences

NASA Astrophysics Data System (ADS)

Pérez Montes, Diego A.; Añel, Juan A.; Pena, Tomás F.; Wallom, David C. H.

2016-04-01

Cloud computing is an emerging technological solution widely used in many fields. Initially developed as a flexible way of managing peak demand it has began to make its way in scientific research. One of the greatest advantages of cloud computing for scientific research is independence of having access to a large cyberinfrastructure to fund or perform a research project. Cloud computing can avoid maintenance expenses for large supercomputers and has the potential to 'democratize' the access to high-performance computing, giving flexibility to funding bodies for allocating budgets for the computational costs associated with a project. Two of the most challenging problems in atmospheric sciences are computational cost and uncertainty in meteorological forecasting and climate projections. Both problems are closely related. Usually uncertainty can be reduced with the availability of computational resources to better reproduce a phenomenon or to perform a larger number of experiments. Here we expose results of the application of cloud computing resources for climate modeling using cloud computing infrastructures of three major vendors and two climate models. We show how the cloud infrastructure compares in performance to traditional supercomputers and how it provides the capability to complete experiments in shorter periods of time. The monetary cost associated is also analyzed. Finally we discuss the future potential of this technology for meteorological and climatological applications, both from the point of view of operational use and research.

Cloud computing for comparative genomics

PubMed Central

2010-01-01

Background Large comparative genomics studies and tools are becoming increasingly more compute-expensive as the number of available genome sequences continues to rise. The capacity and cost of local computing infrastructures are likely to become prohibitive with the increase, especially as the breadth of questions continues to rise. Alternative computing architectures, in particular cloud computing environments, may help alleviate this increasing pressure and enable fast, large-scale, and cost-effective comparative genomics strategies going forward. To test this, we redesigned a typical comparative genomics algorithm, the reciprocal smallest distance algorithm (RSD), to run within Amazon's Elastic Computing Cloud (EC2). We then employed the RSD-cloud for ortholog calculations across a wide selection of fully sequenced genomes. Results We ran more than 300,000 RSD-cloud processes within the EC2. These jobs were farmed simultaneously to 100 high capacity compute nodes using the Amazon Web Service Elastic Map Reduce and included a wide mix of large and small genomes. The total computation time took just under 70 hours and cost a total of $6,302 USD. Conclusions The effort to transform existing comparative genomics algorithms from local compute infrastructures is not trivial. However, the speed and flexibility of cloud computing environments provides a substantial boost with manageable cost. The procedure designed to transform the RSD algorithm into a cloud-ready application is readily adaptable to similar comparative genomics problems. PMID:20482786

Application of microarray analysis on computer cluster and cloud platforms.

PubMed

Bernau, C; Boulesteix, A-L; Knaus, J

2013-01-01

Analysis of recent high-dimensional biological data tends to be computationally intensive as many common approaches such as resampling or permutation tests require the basic statistical analysis to be repeated many times. A crucial advantage of these methods is that they can be easily parallelized due to the computational independence of the resampling or permutation iterations, which has induced many statistics departments to establish their own computer clusters. An alternative is to rent computing resources in the cloud, e.g. at Amazon Web Services. In this article we analyze whether a selection of statistical projects, recently implemented at our department, can be efficiently realized on these cloud resources. Moreover, we illustrate an opportunity to combine computer cluster and cloud resources. In order to compare the efficiency of computer cluster and cloud implementations and their respective parallelizations we use microarray analysis procedures and compare their runtimes on the different platforms. Amazon Web Services provide various instance types which meet the particular needs of the different statistical projects we analyzed in this paper. Moreover, the network capacity is sufficient and the parallelization is comparable in efficiency to standard computer cluster implementations. Our results suggest that many statistical projects can be efficiently realized on cloud resources. It is important to mention, however, that workflows can change substantially as a result of a shift from computer cluster to cloud computing.

Cloud computing for comparative genomics.

PubMed

Wall, Dennis P; Kudtarkar, Parul; Fusaro, Vincent A; Pivovarov, Rimma; Patil, Prasad; Tonellato, Peter J

2010-05-18

Large comparative genomics studies and tools are becoming increasingly more compute-expensive as the number of available genome sequences continues to rise. The capacity and cost of local computing infrastructures are likely to become prohibitive with the increase, especially as the breadth of questions continues to rise. Alternative computing architectures, in particular cloud computing environments, may help alleviate this increasing pressure and enable fast, large-scale, and cost-effective comparative genomics strategies going forward. To test this, we redesigned a typical comparative genomics algorithm, the reciprocal smallest distance algorithm (RSD), to run within Amazon's Elastic Computing Cloud (EC2). We then employed the RSD-cloud for ortholog calculations across a wide selection of fully sequenced genomes. We ran more than 300,000 RSD-cloud processes within the EC2. These jobs were farmed simultaneously to 100 high capacity compute nodes using the Amazon Web Service Elastic Map Reduce and included a wide mix of large and small genomes. The total computation time took just under 70 hours and cost a total of $6,302 USD. The effort to transform existing comparative genomics algorithms from local compute infrastructures is not trivial. However, the speed and flexibility of cloud computing environments provides a substantial boost with manageable cost. The procedure designed to transform the RSD algorithm into a cloud-ready application is readily adaptable to similar comparative genomics problems.

Leveraging human oversight and intervention in large-scale parallel processing of open-source data

NASA Astrophysics Data System (ADS)

Casini, Enrico; Suri, Niranjan; Bradshaw, Jeffrey M.

2015-05-01

The popularity of cloud computing along with the increased availability of cheap storage have led to the necessity of elaboration and transformation of large volumes of open-source data, all in parallel. One way to handle such extensive volumes of information properly is to take advantage of distributed computing frameworks like Map-Reduce. Unfortunately, an entirely automated approach that excludes human intervention is often unpredictable and error prone. Highly accurate data processing and decision-making can be achieved by supporting an automatic process through human collaboration, in a variety of environments such as warfare, cyber security and threat monitoring. Although this mutual participation seems easily exploitable, human-machine collaboration in the field of data analysis presents several challenges. First, due to the asynchronous nature of human intervention, it is necessary to verify that once a correction is made, all the necessary reprocessing is done in chain. Second, it is often needed to minimize the amount of reprocessing in order to optimize the usage of resources due to limited availability. In order to improve on these strict requirements, this paper introduces improvements to an innovative approach for human-machine collaboration in the processing of large amounts of open-source data in parallel.

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.

Consolidation of cloud computing in ATLAS

NASA Astrophysics Data System (ADS)

Taylor, Ryan P.; Domingues Cordeiro, Cristovao Jose; Giordano, Domenico; Hover, John; Kouba, Tomas; Love, Peter; McNab, Andrew; Schovancova, Jaroslava; Sobie, Randall; ATLAS Collaboration

2017-10-01

Throughout the first half of LHC Run 2, ATLAS cloud computing has undergone a period of consolidation, characterized by building upon previously established systems, with the aim of reducing operational effort, improving robustness, and reaching higher scale. This paper describes the current state of ATLAS cloud computing. Cloud activities are converging on a common contextualization approach for virtual machines, and cloud resources are sharing monitoring and service discovery components. We describe the integration of Vacuum resources, streamlined usage of the Simulation at Point 1 cloud for offline processing, extreme scaling on Amazon compute resources, and procurement of commercial cloud capacity in Europe. Finally, building on the previously established monitoring infrastructure, we have deployed a real-time monitoring and alerting platform which coalesces data from multiple sources, provides flexible visualization via customizable dashboards, and issues alerts and carries out corrective actions in response to problems.

A Novel Method for Estimating Shortwave Direct Radiative Effect of Above-cloud Aerosols over Ocean Using CALIOP and MODIS Data

NASA Technical Reports Server (NTRS)

Zhang, Z.; Meyer, K.; Platnick, S.; Oreopoulos, L.; Lee, D.; Yu, H.

2013-01-01

This paper describes an efficient and unique method for computing the shortwave direct radiative effect (DRE) of aerosol residing above low-level liquid-phase clouds using CALIOP and MODIS data. It accounts for the overlapping of aerosol and cloud rigorously by utilizing the joint histogram of cloud optical depth and cloud top pressure. Effects of sub-grid scale cloud and aerosol variations on DRE are accounted for. It is computationally efficient through using grid-level cloud and aerosol statistics, instead of pixel-level products, and a pre-computed look-up table in radiative transfer calculations. We verified that for smoke over the southeast Atlantic Ocean the method yields a seasonal mean instantaneous shortwave DRE that generally agrees with more rigorous pixel-level computation within 4%. We have also computed the annual mean instantaneous shortwave DRE of light-absorbing aerosols (i.e., smoke and polluted dust) over global ocean based on 4 yr of CALIOP and MODIS data. We found that the variability of the annual mean shortwave DRE of above-cloud light-absorbing aerosol is mainly driven by the optical depth of the underlying clouds.

A Novel Method for Estimating Shortwave Direct Radiative Effect of Above-Cloud Aerosols Using CALIOP and MODIS Data

NASA Technical Reports Server (NTRS)

Zhang, Z.; Meyer, K.; Platnick, S.; Oreopoulos, L.; Lee, D.; Yu, H.

2014-01-01

This paper describes an efficient and unique method for computing the shortwave direct radiative effect (DRE) of aerosol residing above low-level liquid-phase clouds using CALIOP and MODIS data. It accounts for the overlapping of aerosol and cloud rigorously by utilizing the joint histogram of cloud optical depth and cloud top pressure. Effects of sub-grid scale cloud and aerosol variations on DRE are accounted for. It is computationally efficient through using grid-level cloud and aerosol statistics, instead of pixel-level products, and a pre-computed look-up table in radiative transfer calculations. We verified that for smoke over the southeast Atlantic Ocean the method yields a seasonal mean instantaneous shortwave DRE that generally agrees with more rigorous pixel-level computation within 4. We have also computed the annual mean instantaneous shortwave DRE of light-absorbing aerosols (i.e., smoke and polluted dust) over global ocean based on 4 yr of CALIOP and MODIS data. We found that the variability of the annual mean shortwave DRE of above-cloud light-absorbing aerosol is mainly driven by the optical depth of the underlying clouds.

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.

cloudPEST - A python module for cloud-computing deployment of PEST, a program for parameter estimation

USGS Publications Warehouse

Fienen, Michael N.; Kunicki, Thomas C.; Kester, Daniel E.

2011-01-01

This report documents cloudPEST-a Python module with functions to facilitate deployment of the model-independent parameter estimation code PEST on a cloud-computing environment. cloudPEST makes use of low-level, freely available command-line tools that interface with the Amazon Elastic Compute Cloud (EC2(TradeMark)) that are unlikely to change dramatically. This report describes the preliminary setup for both Python and EC2 tools and subsequently describes the functions themselves. The code and guidelines have been tested primarily on the Windows(Registered) operating system but are extensible to Linux(Registered).

Cloud Computing in Support of Synchronized Disaster Response Operations

DTIC Science & Technology

2010-09-01

scalable, Web application based on cloud computing technologies to facilitate communication between a broad range of public and private entities without...requiring them to compromise security or competitive advantage. The proposed design applies the unique benefits of cloud computing architectures such as

Architectural Implications of Cloud Computing

DTIC Science & Technology

2011-10-24

Public Cloud Infrastructure-as-a- Service (IaaS) Software -as-a- Service ( SaaS ) Cloud Computing Types Platform-as-a- Service (PaaS) Based on Type of...Twitter #SEIVirtualForum © 2011 Carnegie Mellon University Software -as-a- Service ( SaaS ) Model of software deployment in which a third-party...and System Solutions (RTSS) Program. Her current interests and projects are in service -oriented architecture (SOA), cloud computing, and context

Integrating Cloud-Computing-Specific Model into Aircraft Design

NASA Astrophysics Data System (ADS)

Zhimin, Tian; Qi, Lin; Guangwen, Yang

Cloud Computing is becoming increasingly relevant, as it will enable companies involved in spreading this technology to open the door to Web 3.0. In the paper, the new categories of services introduced will slowly replace many types of computational resources currently used. In this perspective, grid computing, the basic element for the large scale supply of cloud services, will play a fundamental role in defining how those services will be provided. The paper tries to integrate cloud computing specific model into aircraft design. This work has acquired good results in sharing licenses of large scale and expensive software, such as CFD (Computational Fluid Dynamics), UG, CATIA, and so on.

Cognitive Approaches for Medicine in Cloud Computing.

PubMed

Ogiela, Urszula; Takizawa, Makoto; Ogiela, Lidia

2018-03-03

This paper will present the application potential of the cognitive approach to data interpretation, with special reference to medical areas. The possibilities of using the meaning approach to data description and analysis will be proposed for data analysis tasks in Cloud Computing. The methods of cognitive data management in Cloud Computing are aimed to support the processes of protecting data against unauthorised takeover and they serve to enhance the data management processes. The accomplishment of the proposed tasks will be the definition of algorithms for the execution of meaning data interpretation processes in safe Cloud Computing. • We proposed a cognitive methods for data description. • Proposed a techniques for secure data in Cloud Computing. • Application of cognitive approaches for medicine was described.

Towards an Approach of Semantic Access Control for Cloud Computing

NASA Astrophysics Data System (ADS)

Hu, Luokai; Ying, Shi; Jia, Xiangyang; Zhao, Kai

With the development of cloud computing, the mutual understandability among distributed Access Control Policies (ACPs) has become an important issue in the security field of cloud computing. Semantic Web technology provides the solution to semantic interoperability of heterogeneous applications. In this paper, we analysis existing access control methods and present a new Semantic Access Control Policy Language (SACPL) for describing ACPs in cloud computing environment. Access Control Oriented Ontology System (ACOOS) is designed as the semantic basis of SACPL. Ontology-based SACPL language can effectively solve the interoperability issue of distributed ACPs. This study enriches the research that the semantic web technology is applied in the field of security, and provides a new way of thinking of access control in cloud computing.

Easy, Collaborative and Engaging--The Use of Cloud Computing in the Design of Management Classrooms

ERIC Educational Resources Information Center

Schneckenberg, Dirk

2014-01-01

Background: Cloud computing has recently received interest in information systems research and practice as a new way to organise information with the help of an increasingly ubiquitous computer infrastructure. However, the use of cloud computing in higher education institutions and business schools, as well as its potential to create novel…

CloVR: A virtual machine for automated and portable sequence analysis from the desktop using cloud computing

PubMed Central

2011-01-01

Background Next-generation sequencing technologies have decentralized sequence acquisition, increasing the demand for new bioinformatics tools that are easy to use, portable across multiple platforms, and scalable for high-throughput applications. Cloud computing platforms provide on-demand access to computing infrastructure over the Internet and can be used in combination with custom built virtual machines to distribute pre-packaged with pre-configured software. Results We describe the Cloud Virtual Resource, CloVR, a new desktop application for push-button automated sequence analysis that can utilize cloud computing resources. CloVR is implemented as a single portable virtual machine (VM) that provides several automated analysis pipelines for microbial genomics, including 16S, whole genome and metagenome sequence analysis. The CloVR VM runs on a personal computer, utilizes local computer resources and requires minimal installation, addressing key challenges in deploying bioinformatics workflows. In addition CloVR supports use of remote cloud computing resources to improve performance for large-scale sequence processing. In a case study, we demonstrate the use of CloVR to automatically process next-generation sequencing data on multiple cloud computing platforms. Conclusion The CloVR VM and associated architecture lowers the barrier of entry for utilizing complex analysis protocols on both local single- and multi-core computers and cloud systems for high throughput data processing. PMID:21878105

Identifying the impact of G-quadruplexes on Affymetrix 3' arrays using cloud computing.

PubMed

Memon, Farhat N; Owen, Anne M; Sanchez-Graillet, Olivia; Upton, Graham J G; Harrison, Andrew P

2010-01-15

A tetramer quadruplex structure is formed by four parallel strands of DNA/ RNA containing runs of guanine. These quadruplexes are able to form because guanine can Hoogsteen hydrogen bond to other guanines, and a tetrad of guanines can form a stable arrangement. Recently we have discovered that probes on Affymetrix GeneChips that contain runs of guanine do not measure gene expression reliably. We associate this finding with the likelihood that quadruplexes are forming on the surface of GeneChips. In order to cope with the rapidly expanding size of GeneChip array datasets in the public domain, we are exploring the use of cloud computing to replicate our experiments on 3' arrays to look at the effect of the location of G-spots (runs of guanines). Cloud computing is a recently introduced high-performance solution that takes advantage of the computational infrastructure of large organisations such as Amazon and Google. We expect that cloud computing will become widely adopted because it enables bioinformaticians to avoid capital expenditure on expensive computing resources and to only pay a cloud computing provider for what is used. Moreover, as well as financial efficiency, cloud computing is an ecologically-friendly technology, it enables efficient data-sharing and we expect it to be faster for development purposes. Here we propose the advantageous use of cloud computing to perform a large data-mining analysis of public domain 3' arrays.

Reconciliation of the cloud computing model with US federal electronic health record regulations

PubMed Central

2011-01-01

Cloud computing refers to subscription-based, fee-for-service utilization of computer hardware and software over the Internet. The model is gaining acceptance for business information technology (IT) applications because it allows capacity and functionality to increase on the fly without major investment in infrastructure, personnel or licensing fees. Large IT investments can be converted to a series of smaller operating expenses. Cloud architectures could potentially be superior to traditional electronic health record (EHR) designs in terms of economy, efficiency and utility. A central issue for EHR developers in the US is that these systems are constrained by federal regulatory legislation and oversight. These laws focus on security and privacy, which are well-recognized challenges for cloud computing systems in general. EHRs built with the cloud computing model can achieve acceptable privacy and security through business associate contracts with cloud providers that specify compliance requirements, performance metrics and liability sharing. PMID:21727204

Evaluating the Influence of the Client Behavior in Cloud Computing.

PubMed

Souza Pardo, Mário Henrique; Centurion, Adriana Molina; Franco Eustáquio, Paulo Sérgio; Carlucci Santana, Regina Helena; Bruschi, Sarita Mazzini; Santana, Marcos José

2016-01-01

This paper proposes a novel approach for the implementation of simulation scenarios, providing a client entity for cloud computing systems. The client entity allows the creation of scenarios in which the client behavior has an influence on the simulation, making the results more realistic. The proposed client entity is based on several characteristics that affect the performance of a cloud computing system, including different modes of submission and their behavior when the waiting time between requests (think time) is considered. The proposed characterization of the client enables the sending of either individual requests or group of Web services to scenarios where the workload takes the form of bursts. The client entity is included in the CloudSim, a framework for modelling and simulation of cloud computing. Experimental results show the influence of the client behavior on the performance of the services executed in a cloud computing system.

Evaluating the Influence of the Client Behavior in Cloud Computing

PubMed Central

Centurion, Adriana Molina; Franco Eustáquio, Paulo Sérgio; Carlucci Santana, Regina Helena; Bruschi, Sarita Mazzini; Santana, Marcos José

2016-01-01

This paper proposes a novel approach for the implementation of simulation scenarios, providing a client entity for cloud computing systems. The client entity allows the creation of scenarios in which the client behavior has an influence on the simulation, making the results more realistic. The proposed client entity is based on several characteristics that affect the performance of a cloud computing system, including different modes of submission and their behavior when the waiting time between requests (think time) is considered. The proposed characterization of the client enables the sending of either individual requests or group of Web services to scenarios where the workload takes the form of bursts. The client entity is included in the CloudSim, a framework for modelling and simulation of cloud computing. Experimental results show the influence of the client behavior on the performance of the services executed in a cloud computing system. PMID:27441559

A Weibull distribution accrual failure detector for cloud computing

PubMed Central

Wu, Zhibo; Wu, Jin; Zhao, Yao; Wen, Dongxin

2017-01-01

Failure detectors are used to build high availability distributed systems as the fundamental component. To meet the requirement of a complicated large-scale distributed system, accrual failure detectors that can adapt to multiple applications have been studied extensively. However, several implementations of accrual failure detectors do not adapt well to the cloud service environment. To solve this problem, a new accrual failure detector based on Weibull Distribution, called the Weibull Distribution Failure Detector, has been proposed specifically for cloud computing. It can adapt to the dynamic and unexpected network conditions in cloud computing. The performance of the Weibull Distribution Failure Detector is evaluated and compared based on public classical experiment data and cloud computing experiment data. The results show that the Weibull Distribution Failure Detector has better performance in terms of speed and accuracy in unstable scenarios, especially in cloud computing. PMID:28278229

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.

Reconciliation of the cloud computing model with US federal electronic health record regulations.

PubMed

Schweitzer, Eugene J

2012-01-01

Cloud computing refers to subscription-based, fee-for-service utilization of computer hardware and software over the Internet. The model is gaining acceptance for business information technology (IT) applications because it allows capacity and functionality to increase on the fly without major investment in infrastructure, personnel or licensing fees. Large IT investments can be converted to a series of smaller operating expenses. Cloud architectures could potentially be superior to traditional electronic health record (EHR) designs in terms of economy, efficiency and utility. A central issue for EHR developers in the US is that these systems are constrained by federal regulatory legislation and oversight. These laws focus on security and privacy, which are well-recognized challenges for cloud computing systems in general. EHRs built with the cloud computing model can achieve acceptable privacy and security through business associate contracts with cloud providers that specify compliance requirements, performance metrics and liability sharing.

OpenID connect as a security service in Cloud-based diagnostic imaging systems

NASA Astrophysics Data System (ADS)

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

2015-03-01

The evolution of cloud computing is driving the next generation of diagnostic imaging (DI) systems. Cloud-based DI systems are able to deliver better services to patients without constraining to their own physical facilities. However, privacy and security concerns have been consistently regarded as the major obstacle for adoption of cloud computing by healthcare domains. Furthermore, traditional computing models and interfaces employed by DI systems are not ready for accessing diagnostic images through mobile devices. RESTful is an ideal technology for provisioning both mobile services and cloud computing. OpenID Connect, combining OpenID and OAuth together, is an emerging REST-based federated identity solution. It is one of the most perspective open standards to potentially become the de-facto standard for securing cloud computing and mobile applications, which has ever been regarded as "Kerberos of Cloud". We introduce OpenID Connect as an identity and authentication service in cloud-based DI systems and propose enhancements that allow for incorporating this technology within distributed enterprise environment. The objective of this study is to offer solutions for secure radiology image sharing among DI-r (Diagnostic Imaging Repository) and heterogeneous PACS (Picture Archiving and Communication Systems) as well as mobile clients in the cloud ecosystem. Through using OpenID Connect as an open-source identity and authentication service, deploying DI-r and PACS to private or community clouds should obtain equivalent security level to traditional computing model.

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.

Development of a SaaS application probe to the physical properties of the Earth's interior: An attempt at moving HPC to the cloud

NASA Astrophysics Data System (ADS)

Huang, Qian

2014-09-01

Scientific computing often requires the availability of a massive number of computers for performing large-scale simulations, and computing in mineral physics is no exception. In order to investigate physical properties of minerals at extreme conditions in computational mineral physics, parallel computing technology is used to speed up the performance by utilizing multiple computer resources to process a computational task simultaneously thereby greatly reducing computation time. Traditionally, parallel computing has been addressed by using High Performance Computing (HPC) solutions and installed facilities such as clusters and super computers. Today, it has been seen that there is a tremendous growth in cloud computing. Infrastructure as a Service (IaaS), the on-demand and pay-as-you-go model, creates a flexible and cost-effective mean to access computing resources. In this paper, a feasibility report of HPC on a cloud infrastructure is presented. It is found that current cloud services in IaaS layer still need to improve performance to be useful to research projects. On the other hand, Software as a Service (SaaS), another type of cloud computing, is introduced into an HPC system for computing in mineral physics, and an application of which is developed. In this paper, an overall description of this SaaS application is presented. This contribution can promote cloud application development in computational mineral physics, and cross-disciplinary studies.

Adopting Cloud Computing in the Pakistan Navy

DTIC Science & Technology

2015-06-01

administrative aspect is required to operate optimally, provide synchronized delivery of cloud services, and integrate multi-provider cloud environment...AND ABBREVIATIONS ANSI American National Standards Institute AWS Amazon web services CIA Confidentiality Integrity Availability CIO Chief...also adopted cloud computing as an integral component of military operations conducted either locally or remotely. With the use of 2 cloud services

ADVANCED COMPUTATIONAL METHODS IN DOSE MODELING: APPLICATION OF COMPUTATIONAL BIOPHYSICAL TRANSPORT, COMPUTATIONAL CHEMISTRY, AND COMPUTATIONAL BIOLOGY

EPA Science Inventory

Computational toxicology (CompTox) leverages the significant gains in computing power and computational techniques (e.g., numerical approaches, structure-activity relationships, bioinformatics) realized over the last few years, thereby reducing costs and increasing efficiency i...

Translational bioinformatics in the cloud: an affordable alternative

PubMed Central

2010-01-01

With the continued exponential expansion of publicly available genomic data and access to low-cost, high-throughput molecular technologies for profiling patient populations, computational technologies and informatics are becoming vital considerations in genomic medicine. Although cloud computing technology is being heralded as a key enabling technology for the future of genomic research, available case studies are limited to applications in the domain of high-throughput sequence data analysis. The goal of this study was to evaluate the computational and economic characteristics of cloud computing in performing a large-scale data integration and analysis representative of research problems in genomic medicine. We find that the cloud-based analysis compares favorably in both performance and cost in comparison to a local computational cluster, suggesting that cloud computing technologies might be a viable resource for facilitating large-scale translational research in genomic medicine. PMID:20691073

Cloud Computing Explained

ERIC Educational Resources Information Center

Metz, Rosalyn

2010-01-01

While many talk about the cloud, few actually understand it. Three organizations' definitions come to the forefront when defining the cloud: Gartner, Forrester, and the National Institutes of Standards and Technology (NIST). Although both Gartner and Forrester provide definitions of cloud computing, the NIST definition is concise and uses…

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.

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.

An Assessment of Security Vulnerabilities Comprehension of Cloud Computing Environments: A Quantitative Study Using the Unified Theory of Acceptance and Use

ERIC Educational Resources Information Center

Venkatesh, Vijay P.

2013-01-01

The current computing landscape owes its roots to the birth of hardware and software technologies from the 1940s and 1950s. Since then, the advent of mainframes, miniaturized computing, and internetworking has given rise to the now prevalent cloud computing era. In the past few months just after 2010, cloud computing adoption has picked up pace…

Cloud Computing at the Tactical Edge

DTIC Science & Technology

2012-10-01

Cloud Computing (CloudCom ’09). Bejing , China , December 2009. Springer-Verlag, 2009. [Marinelli 2009] Marinelli, E. Hyrax: Cloud Computing on Mobile...offloading is appropriate. Each applica- tion overlay is generated from the same Base VM Image that resides in the cloudlet. In an opera - tional setting...overlay, the following opera - tions execute: 1. The overlay is decompressed using the tools listed in Section 4.2. 2. VM synthesis is performed through

Near Real-Time Flood Monitoring and Impact Assessment Systems. Chapter 6; [Case Study: 2011 Flooding in Southeast Asia

NASA Technical Reports Server (NTRS)

Ahamed, Aakash; Bolten, John; Doyle, Colin; Fayne, Jessica

2016-01-01

Floods are the costliest natural disaster, causing approximately 6.8 million deaths in the twentieth century alone. Worldwide economic flood damage estimates in 2012 exceed $19 Billion USD. Extended duration floods also pose longer term threats to food security, water, sanitation, hygiene, and community livelihoods, particularly in developing countries. Projections by the Intergovernmental Panel on Climate Change (IPCC) suggest that precipitation extremes, rainfall intensity, storm intensity, and variability are increasing due to climate change. Increasing hydrologic uncertainty will likely lead to unprecedented extreme flood events. As such, there is a vital need to enhance and further develop traditional techniques used to rapidly assess flooding and extend analytical methods to estimate impacted population and infrastructure. Measuring flood extent in situ is generally impractical, time consuming, and can be inaccurate. Remotely sensed imagery acquired from space-borne and airborne sensors provides a viable platform for consistent and rapid wall-to-wall monitoring of large flood events through time. Terabytes of freely available satellite imagery are made available online each day by NASA, ESA, and other international space research institutions. Advances in cloud computing and data storage technologies allow researchers to leverage these satellite data and apply analytical methods at scale. Repeat-survey earth observations help provide insight about how natural phenomena change through time, including the progression and recession of floodwaters. In recent years, cloud-penetrating radar remote sensing techniques (e.g., Synthetic Aperture Radar) and high temporal resolution imagery platforms (e.g., MODIS and its 1-day return period), along with high performance computing infrastructure, have enabled significant advances in software systems that provide flood warning, assessments, and hazard reduction potential. By incorporating social and economic data, researchers can develop systems that automatically quantify the socioeconomic impacts resulting from flood disaster events.

Reassessing the effect of cloud type on Earth's energy balance

NASA Astrophysics Data System (ADS)

Hang, A.; L'Ecuyer, T.

2017-12-01

Cloud feedbacks depend critically on the characteristics of the clouds that change, their location and their environment. As a result, accurately predicting the impact of clouds on future climate requires a better understanding of individual cloud types and their spatial and temporal variability. This work revisits the problem of documenting the effects of distinct cloud regimes on Earth's radiation budget distinguishing cloud types according to their signatures in spaceborne active observations. Using CloudSat's multi-sensor radiative fluxes product that leverages high-resolution vertical cloud information from CloudSat, CALIPSO, and MODIS observations to provide the most accurate estimates of vertically-resolved radiative fluxes available to date, we estimate the global annual mean net cloud radiative effect at the top of the atmosphere to be -17.1 W m-2 (-44.2 W m-2 in the shortwave and 27.1 W m-2 in the longwave), slightly weaker than previous estimates from passive sensor observations. Multi-layered cloud systems, that are often misclassified using passive techniques but are ubiquitous in both hemispheres, contribute about -6.2 W m-2 of the net cooling effect, particularly at ITCZ and higher latitudes. Another unique aspect of this work is the ability of CloudSat and CALIPSO to detect cloud boundary information providing an improved capability to accurately discern the impact of cloud-type variations on surface radiation balance, a critical factor in modulating the disposition of excess energy in the climate system. The global annual net cloud radiative effect at the surface is estimated to be -24.8 W m-2 (-51.1 W m-2 in the shortwave and 26.3 W m-2 in the longwave), dominated by shortwave heating in multi-layered and stratocumulus clouds. Corresponding estimates of the effects of clouds on atmospheric heating suggest that clouds redistribute heat from poles to equator enhancing the general circulation.

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).

Teaching, Learning, and Collaborating in the Cloud: Applications of Cloud Computing for Educators in Post-Secondary Institutions

ERIC Educational Resources Information Center

Aaron, Lynn S.; Roche, Catherine M.

2012-01-01

"Cloud computing" refers to the use of computing resources on the Internet instead of on individual personal computers. The field is expanding and has significant potential value for educators. This is discussed with a focus on four main functions: file storage, file synchronization, document creation, and collaboration--each of which has…

Leveraging the Cloud for Integrated Network Experimentation

DTIC Science & Technology

2014-03-01

kernel settings, or any of the low-level subcomponents. 3. Scalable Solutions: Businesses can build scalable solutions for their clients , ranging from...values. These values 13 can assume several distributions that include normal, Pareto , uniform, exponential and Poisson, among others [21]. Additionally, D...communication, the web client establishes a connection to the server before traffic begins to flow. Web servers do not initiate connections to clients in

A cloud-compatible bioinformatics pipeline for ultrarapid pathogen identification from next-generation sequencing of clinical samples

PubMed Central

Naccache, Samia N.; Federman, Scot; Veeraraghavan, Narayanan; Zaharia, Matei; Lee, Deanna; Samayoa, Erik; Bouquet, Jerome; Greninger, Alexander L.; Luk, Ka-Cheung; Enge, Barryett; Wadford, Debra A.; Messenger, Sharon L.; Genrich, Gillian L.; Pellegrino, Kristen; Grard, Gilda; Leroy, Eric; Schneider, Bradley S.; Fair, Joseph N.; Martínez, Miguel A.; Isa, Pavel; Crump, John A.; DeRisi, Joseph L.; Sittler, Taylor; Hackett, John; Miller, Steve; Chiu, Charles Y.

2014-01-01

Unbiased next-generation sequencing (NGS) approaches enable comprehensive pathogen detection in the clinical microbiology laboratory and have numerous applications for public health surveillance, outbreak investigation, and the diagnosis of infectious diseases. However, practical deployment of the technology is hindered by the bioinformatics challenge of analyzing results accurately and in a clinically relevant timeframe. Here we describe SURPI (“sequence-based ultrarapid pathogen identification”), a computational pipeline for pathogen identification from complex metagenomic NGS data generated from clinical samples, and demonstrate use of the pipeline in the analysis of 237 clinical samples comprising more than 1.1 billion sequences. Deployable on both cloud-based and standalone servers, SURPI leverages two state-of-the-art aligners for accelerated analyses, SNAP and RAPSearch, which are as accurate as existing bioinformatics tools but orders of magnitude faster in performance. In fast mode, SURPI detects viruses and bacteria by scanning data sets of 7–500 million reads in 11 min to 5 h, while in comprehensive mode, all known microorganisms are identified, followed by de novo assembly and protein homology searches for divergent viruses in 50 min to 16 h. SURPI has also directly contributed to real-time microbial diagnosis in acutely ill patients, underscoring its potential key role in the development of unbiased NGS-based clinical assays in infectious diseases that demand rapid turnaround times. PMID:24899342

BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience

PubMed Central

Van Geit, Werner; Gevaert, Michael; Chindemi, Giuseppe; Rössert, Christian; Courcol, Jean-Denis; Muller, Eilif B.; Schürmann, Felix; Segev, Idan; Markram, Henry

2016-01-01

At many scales in neuroscience, appropriate mathematical models take the form of complex dynamical systems. Parameterizing such models to conform to the multitude of available experimental constraints is a global non-linear optimisation problem with a complex fitness landscape, requiring numerical techniques to find suitable approximate solutions. Stochastic optimisation approaches, such as evolutionary algorithms, have been shown to be effective, but often the setting up of such optimisations and the choice of a specific search algorithm and its parameters is non-trivial, requiring domain-specific expertise. Here we describe BluePyOpt, a Python package targeted at the broad neuroscience community to simplify this task. BluePyOpt is an extensible framework for data-driven model parameter optimisation that wraps and standardizes several existing open-source tools. It simplifies the task of creating and sharing these optimisations, and the associated techniques and knowledge. This is achieved by abstracting the optimisation and evaluation tasks into various reusable and flexible discrete elements according to established best-practices. Further, BluePyOpt provides methods for setting up both small- and large-scale optimisations on a variety of platforms, ranging from laptops to Linux clusters and cloud-based compute infrastructures. The versatility of the BluePyOpt framework is demonstrated by working through three representative neuroscience specific use cases. PMID:27375471

The Development of an Educational Cloud for IS Curriculum through a Student-Run Data Center

ERIC Educational Resources Information Center

Hwang, Drew; Pike, Ron; Manson, Dan

2016-01-01

The industry-wide emphasis on cloud computing has created a new focus in Information Systems (IS) education. As the demand for graduates with adequate knowledge and skills in cloud computing is on the rise, IS educators are facing a challenge to integrate cloud technology into their curricula. Although public cloud tools and services are available…

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.

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

78 FR 2919 - Proposed Priority-National Institute on Disability and Rehabilitation Research-Disability and...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-01-15

... Rehabilitation Research--Disability and Rehabilitation Research Project--Inclusive Cloud and Web Computing CFDA... inclusive Cloud and Web computing. The Assistant Secretary may use this priority for competitions in fiscal... Priority for Inclusive Cloud and Web Computing'' in the subject line of your electronic message. FOR...

Cloud Computing for Teaching Practice: A New Design?

ERIC Educational Resources Information Center

Saadatdoost, Robab; Sim, Alex Tze Hiang; Jafarkarimi, Hosein; Hee, Jee Mei; Saadatdoost, Leila

2014-01-01

Recently researchers have shown an increased interest in cloud computing technology. It is becoming increasingly difficult to ignore cloud computing technology in education context. However rapid changes in information technology are having a serious effect on teaching framework designs. So far, however, there has been little discussion about…

78 FR 26626 - Applications for New Awards; National Institute on Disability and Rehabilitation Research...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-07

... Rehabilitation Research--Disability and Rehabilitation Research Projects--Inclusive Cloud and Web Computing... Rehabilitation Research Projects (DRRPs)--Inclusive Cloud and Web Computing Notice inviting applications for new...#DRRP . Priorities: Priority 1--DRRP on Inclusive Cloud and Web Computing-- is from the notice of final...

Navigating the Challenges of the Cloud

ERIC Educational Resources Information Center

Ovadia, Steven

2010-01-01

Cloud computing is increasingly popular in education. Cloud computing is "the delivery of computer services from vast warehouses of shared machines that enables companies and individuals to cut costs by handing over the running of their email, customer databases or accounting software to someone else, and then accessing it over the internet."…

A study on strategic provisioning of cloud computing services.

PubMed

Whaiduzzaman, Md; Haque, Mohammad Nazmul; Rejaul Karim Chowdhury, Md; Gani, Abdullah

2014-01-01

Cloud computing is currently emerging as an ever-changing, growing paradigm that models "everything-as-a-service." Virtualised physical resources, infrastructure, and applications are supplied by service provisioning in the cloud. The evolution in the adoption of cloud computing is driven by clear and distinct promising features for both cloud users and cloud providers. However, the increasing number of cloud providers and the variety of service offerings have made it difficult for the customers to choose the best services. By employing successful service provisioning, the essential services required by customers, such as agility and availability, pricing, security and trust, and user metrics can be guaranteed by service provisioning. Hence, continuous service provisioning that satisfies the user requirements is a mandatory feature for the cloud user and vitally important in cloud computing service offerings. Therefore, we aim to review the state-of-the-art service provisioning objectives, essential services, topologies, user requirements, necessary metrics, and pricing mechanisms. We synthesize and summarize different provision techniques, approaches, and models through a comprehensive literature review. A thematic taxonomy of cloud service provisioning is presented after the systematic review. Finally, future research directions and open research issues are identified.

A Study on Strategic Provisioning of Cloud Computing Services

PubMed Central

Rejaul Karim Chowdhury, Md

2014-01-01

Cloud computing is currently emerging as an ever-changing, growing paradigm that models “everything-as-a-service.” Virtualised physical resources, infrastructure, and applications are supplied by service provisioning in the cloud. The evolution in the adoption of cloud computing is driven by clear and distinct promising features for both cloud users and cloud providers. However, the increasing number of cloud providers and the variety of service offerings have made it difficult for the customers to choose the best services. By employing successful service provisioning, the essential services required by customers, such as agility and availability, pricing, security and trust, and user metrics can be guaranteed by service provisioning. Hence, continuous service provisioning that satisfies the user requirements is a mandatory feature for the cloud user and vitally important in cloud computing service offerings. Therefore, we aim to review the state-of-the-art service provisioning objectives, essential services, topologies, user requirements, necessary metrics, and pricing mechanisms. We synthesize and summarize different provision techniques, approaches, and models through a comprehensive literature review. A thematic taxonomy of cloud service provisioning is presented after the systematic review. Finally, future research directions and open research issues are identified. PMID:25032243

Scaling Agile Infrastructure to People

NASA Astrophysics Data System (ADS)

Jones, B.; McCance, G.; Traylen, S.; Barrientos Arias, N.

2015-12-01

When CERN migrated its infrastructure away from homegrown fabric management tools to emerging industry-standard open-source solutions, the immediate technical challenges and motivation were clear. The move to a multi-site Cloud Computing model meant that the tool chains that were growing around this ecosystem would be a good choice, the challenge was to leverage them. The use of open-source tools brings challenges other than merely how to deploy them. Homegrown software, for all the deficiencies identified at the outset of the project, has the benefit of growing with the organization. This paper will examine what challenges there were in adapting open-source tools to the needs of the organization, particularly in the areas of multi-group development and security. Additionally, the increase in scale of the plant required changes to how Change Management was organized and managed. Continuous Integration techniques are used in order to manage the rate of change across multiple groups, and the tools and workflow for this will be examined.

Development of the ARISTOTLE webware for cloud-based rarefied gas flow modeling

NASA Astrophysics Data System (ADS)

Deschenes, Timothy R.; Grot, Jonathan; Cline, Jason A.

2016-11-01

Rarefied gas dynamics are important for a wide variety of applications. An improvement in the ability of general users to predict these gas flows will enable optimization of current, and discovery of future processes. Despite this potential, most rarefied simulation software is designed by and for experts in the community. This has resulted in low adoption of the methods outside of the immediate RGD community. This paper outlines an ongoing effort to create a rarefied gas dynamics simulation tool that can be used by a general audience. The tool leverages a direct simulation Monte Carlo (DSMC) library that is available to the entire community and a web-based simulation process that will enable all users to take advantage of high performance computing capabilities. First, the DSMC library and simulation architecture are described. Then the DSMC library is used to predict a number of representative transient gas flows that are applicable to the rarefied gas dynamics community. The paper closes with a summary and future direction.

Region-Based Prediction for Image Compression in the Cloud.

PubMed

Begaint, Jean; Thoreau, Dominique; Guillotel, Philippe; Guillemot, Christine

2018-04-01

Thanks to the increasing number of images stored in the cloud, external image similarities can be leveraged to efficiently compress images by exploiting inter-images correlations. In this paper, we propose a novel image prediction scheme for cloud storage. Unlike current state-of-the-art methods, we use a semi-local approach to exploit inter-image correlation. The reference image is first segmented into multiple planar regions determined from matched local features and super-pixels. The geometric and photometric disparities between the matched regions of the reference image and the current image are then compensated. Finally, multiple references are generated from the estimated compensation models and organized in a pseudo-sequence to differentially encode the input image using classical video coding tools. Experimental results demonstrate that the proposed approach yields significant rate-distortion performance improvements compared with the current image inter-coding solutions such as high efficiency video coding.

H-Ransac a Hybrid Point Cloud Segmentation Combining 2d and 3d Data

NASA Astrophysics Data System (ADS)

Adam, A.; Chatzilari, E.; Nikolopoulos, S.; Kompatsiaris, I.

2018-05-01

In this paper, we present a novel 3D segmentation approach operating on point clouds generated from overlapping images. The aim of the proposed hybrid approach is to effectively segment co-planar objects, by leveraging the structural information originating from the 3D point cloud and the visual information from the 2D images, without resorting to learning based procedures. More specifically, the proposed hybrid approach, H-RANSAC, is an extension of the well-known RANSAC plane-fitting algorithm, incorporating an additional consistency criterion based on the results of 2D segmentation. Our expectation that the integration of 2D data into 3D segmentation will achieve more accurate results, is validated experimentally in the domain of 3D city models. Results show that HRANSAC can successfully delineate building components like main facades and windows, and provide more accurate segmentation results compared to the typical RANSAC plane-fitting algorithm.

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

McCaskey, Alexander J.

There is a lack of state-of-the-art quantum computing simulation software that scales on heterogeneous systems like Titan. Tensor Network Quantum Virtual Machine (TNQVM) provides a quantum simulator that leverages a distributed network of GPUs to simulate quantum circuits in a manner that leverages recent results from tensor network theory.

How to Cloud for Earth Scientists: An Introduction

NASA Technical Reports Server (NTRS)

Lynnes, Chris

2018-01-01

This presentation is a tutorial on getting started with cloud computing for the purposes of Earth Observation datasets. We first discuss some of the main advantages that cloud computing can provide for the Earth scientist: copious processing power, immense and affordable data storage, and rapid startup time. We also talk about some of the challenges of getting the most out of cloud computing: re-organizing the way data are analyzed, handling node failures and attending.

Evaluating the Usage of Cloud-Based Collaboration Services through Teamwork

ERIC Educational Resources Information Center

Qin, Li; Hsu, Jeffrey; Stern, Mel

2016-01-01

With the proliferation of cloud computing for both organizational and educational use, cloud-based collaboration services are transforming how people work in teams. The authors investigated the determinants of the usage of cloud-based collaboration services including teamwork quality, computer self-efficacy, and prior experience, as well as its…

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.

Understanding the Performance and Potential of Cloud Computing for Scientific Applications

DOE PAGES

Sadooghi, Iman; Martin, Jesus Hernandez; Li, Tonglin; ...

2015-02-19

In this paper, commercial clouds bring a great opportunity to the scientific computing area. Scientific applications usually require significant resources, however not all scientists have access to sufficient high-end computing systems, may of which can be found in the Top500 list. Cloud Computing has gained the attention of scientists as a competitive resource to run HPC applications at a potentially lower cost. But as a different infrastructure, it is unclear whether clouds are capable of running scientific applications with a reasonable performance per money spent. This work studies the performance of public clouds and places this performance in context tomore » price. We evaluate the raw performance of different services of AWS cloud in terms of the basic resources, such as compute, memory, network and I/O. We also evaluate the performance of the scientific applications running in the cloud. This paper aims to assess the ability of the cloud to perform well, as well as to evaluate the cost of the cloud running scientific applications. We developed a full set of metrics and conducted a comprehensive performance evlauation over the Amazon cloud. We evaluated EC2, S3, EBS and DynamoDB among the many Amazon AWS services. We evaluated the memory sub-system performance with CacheBench, the network performance with iperf, processor and network performance with the HPL benchmark application, and shared storage with NFS and PVFS in addition to S3. We also evaluated a real scientific computing application through the Swift parallel scripting system at scale. Armed with both detailed benchmarks to gauge expected performance and a detailed monetary cost analysis, we expect this paper will be a recipe cookbook for scientists to help them decide where to deploy and run their scientific applications between public clouds, private clouds, or hybrid clouds.« less

Understanding the Performance and Potential of Cloud Computing for Scientific Applications

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

Sadooghi, Iman; Martin, Jesus Hernandez; Li, Tonglin

In this paper, commercial clouds bring a great opportunity to the scientific computing area. Scientific applications usually require significant resources, however not all scientists have access to sufficient high-end computing systems, may of which can be found in the Top500 list. Cloud Computing has gained the attention of scientists as a competitive resource to run HPC applications at a potentially lower cost. But as a different infrastructure, it is unclear whether clouds are capable of running scientific applications with a reasonable performance per money spent. This work studies the performance of public clouds and places this performance in context tomore » price. We evaluate the raw performance of different services of AWS cloud in terms of the basic resources, such as compute, memory, network and I/O. We also evaluate the performance of the scientific applications running in the cloud. This paper aims to assess the ability of the cloud to perform well, as well as to evaluate the cost of the cloud running scientific applications. We developed a full set of metrics and conducted a comprehensive performance evlauation over the Amazon cloud. We evaluated EC2, S3, EBS and DynamoDB among the many Amazon AWS services. We evaluated the memory sub-system performance with CacheBench, the network performance with iperf, processor and network performance with the HPL benchmark application, and shared storage with NFS and PVFS in addition to S3. We also evaluated a real scientific computing application through the Swift parallel scripting system at scale. Armed with both detailed benchmarks to gauge expected performance and a detailed monetary cost analysis, we expect this paper will be a recipe cookbook for scientists to help them decide where to deploy and run their scientific applications between public clouds, private clouds, or hybrid clouds.« 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.

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.

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

Secure Cloud Computing Implementation Study For Singapore Military Operations

DTIC Science & Technology

2016-09-01

COMPUTING IMPLEMENTATION STUDY FOR SINGAPORE MILITARY OPERATIONS by Lai Guoquan September 2016 Thesis Advisor: John D. Fulp Co-Advisor...DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE SECURE CLOUD COMPUTING IMPLEMENTATION STUDY FOR SINGAPORE MILITARY OPERATIONS 5. FUNDING NUMBERS...addition, from the military perspective, the benefits of cloud computing were analyzed from a study of the U.S. Department of Defense. Then, using

Operating Dedicated Data Centers - Is It Cost-Effective?

NASA Astrophysics Data System (ADS)

Ernst, M.; Hogue, R.; Hollowell, C.; Strecker-Kellog, W.; Wong, A.; Zaytsev, A.

2014-06-01

The advent of cloud computing centres such as Amazon's EC2 and Google's Computing Engine has elicited comparisons with dedicated computing clusters. Discussions on appropriate usage of cloud resources (both academic and commercial) and costs have ensued. This presentation discusses a detailed analysis of the costs of operating and maintaining the RACF (RHIC and ATLAS Computing Facility) compute cluster at Brookhaven National Lab and compares them with the cost of cloud computing resources under various usage scenarios. An extrapolation of likely future cost effectiveness of dedicated computing resources is also presented.

Cloud Technology May Widen Genomic Bottleneck - TCGA

Cancer.gov

Computational biologist Dr. Ilya Shmulevich suggests that renting cloud computing power might widen the bottleneck for analyzing genomic data. Learn more about his experience with the Cloud in this TCGA in Action Case Study.

Platform for High-Assurance Cloud Computing

DTIC Science & Technology

2016-06-01

to create today’s standard cloud computing applications and services. Additionally , our SuperCloud (a related but distinct project under the same... Additionally , our SuperCloud (a related but distinct project under the same MRC funding) reduces vendor lock-in and permits application to migrate, to follow...managing key- value storage with strong assurance properties. This first accomplishment allows us to climb the cloud technical stack, by offering

MCloud: Secure Provenance for Mobile Cloud Users

DTIC Science & Technology

2016-10-03

Feasibility of Smartphone Clouds , 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). 04-MAY- 15, Shenzhen, China...final decision. MCloud: Secure Provenance for Mobile Cloud Users Final Report Bogdan Carbunar Florida International University Computing and...Release; Distribution Unlimited UU UU UU UU 03-10-2016 31-May-2013 30-May-2016 Final Report: MCloud: Secure Provenance for Mobile Cloud Users The views

HEPCloud, a New Paradigm for HEP Facilities: CMS Amazon Web Services Investigation

DOE PAGES

Holzman, Burt; Bauerdick, Lothar A. T.; Bockelman, Brian; ...

2017-09-29

Historically, high energy physics computing has been performed on large purpose-built computing systems. These began as single-site compute facilities, but have evolved into the distributed computing grids used today. Recently, there has been an exponential increase in the capacity and capability of commercial clouds. Cloud resources are highly virtualized and intended to be able to be flexibly deployed for a variety of computing tasks. There is a growing interest among the cloud providers to demonstrate the capability to perform large-scale scientific computing. In this paper, we discuss results from the CMS experiment using the Fermilab HEPCloud facility, which utilized bothmore » local Fermilab resources and virtual machines in the Amazon Web Services Elastic Compute Cloud. We discuss the planning, technical challenges, and lessons learned involved in performing physics workflows on a large-scale set of virtualized resources. Additionally, we will discuss the economics and operational efficiencies when executing workflows both in the cloud and on dedicated resources.« less

HEPCloud, a New Paradigm for HEP Facilities: CMS Amazon Web Services Investigation

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

Holzman, Burt; Bauerdick, Lothar A. T.; Bockelman, Brian

Historically, high energy physics computing has been performed on large purpose-built computing systems. These began as single-site compute facilities, but have evolved into the distributed computing grids used today. Recently, there has been an exponential increase in the capacity and capability of commercial clouds. Cloud resources are highly virtualized and intended to be able to be flexibly deployed for a variety of computing tasks. There is a growing interest among the cloud providers to demonstrate the capability to perform large-scale scientific computing. In this paper, we discuss results from the CMS experiment using the Fermilab HEPCloud facility, which utilized bothmore » local Fermilab resources and virtual machines in the Amazon Web Services Elastic Compute Cloud. We discuss the planning, technical challenges, and lessons learned involved in performing physics workflows on a large-scale set of virtualized resources. Additionally, we will discuss the economics and operational efficiencies when executing workflows both in the cloud and on dedicated resources.« less

Information Security in the Age of Cloud Computing

ERIC Educational Resources Information Center

Sims, J. Eric

2012-01-01

Information security has been a particularly hot topic since the enhanced internal control requirements of Sarbanes-Oxley (SOX) were introduced in 2002. At about this same time, cloud computing started its explosive growth. Outsourcing of mission-critical functions has always been a gamble for managers, but the advantages of cloud computing are…

Cloud Computing in the Curricula of Schools of Computer Science and Information Systems

ERIC Educational Resources Information Center

Lawler, James P.

2011-01-01

The cloud continues to be a developing area of information systems. Evangelistic literature in the practitioner field indicates benefit for business firms but disruption for technology departments of the firms. Though the cloud currently is immature in methodology, this study defines a model program by which computer science and information…

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…

A Semantic Based Policy Management Framework for Cloud Computing Environments

ERIC Educational Resources Information Center

Takabi, Hassan

2013-01-01

Cloud computing paradigm has gained tremendous momentum and generated intensive interest. Although security issues are delaying its fast adoption, cloud computing is an unstoppable force and we need to provide security mechanisms to ensure its secure adoption. In this dissertation, we mainly focus on issues related to policy management and access…

CANFAR+Skytree: A Cloud Computing and Data Mining System for Astronomy

NASA Astrophysics Data System (ADS)

Ball, N. M.

2013-10-01

This is a companion Focus Demonstration article to the CANFAR+Skytree poster (Ball 2013, this volume), demonstrating the usage of the Skytree machine learning software on the Canadian Advanced Network for Astronomical Research (CANFAR) cloud computing system. CANFAR+Skytree is the world's first cloud computing system for data mining in astronomy.

ASSURED CLOUD COMPUTING UNIVERSITY CENTER OFEXCELLENCE (ACC UCOE)

DTIC Science & Technology

2018-01-18

average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed...infrastructure security -Design of algorithms and techniques for real- time assuredness in cloud computing -Map-reduce task assignment with data locality...46 DESIGN OF ALGORITHMS AND TECHNIQUES FOR REAL- TIME ASSUREDNESS IN CLOUD COMPUTING

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

A Secure Alignment Algorithm for Mapping Short Reads to Human Genome.

PubMed

Zhao, Yongan; Wang, Xiaofeng; Tang, Haixu

2018-05-09

The elastic and inexpensive computing resources such as clouds have been recognized as a useful solution to analyzing massive human genomic data (e.g., acquired by using next-generation sequencers) in biomedical researches. However, outsourcing human genome computation to public or commercial clouds was hindered due to privacy concerns: even a small number of human genome sequences contain sufficient information for identifying the donor of the genomic data. This issue cannot be directly addressed by existing security and cryptographic techniques (such as homomorphic encryption), because they are too heavyweight to carry out practical genome computation tasks on massive data. In this article, we present a secure algorithm to accomplish the read mapping, one of the most basic tasks in human genomic data analysis based on a hybrid cloud computing model. Comparing with the existing approaches, our algorithm delegates most computation to the public cloud, while only performing encryption and decryption on the private cloud, and thus makes the maximum use of the computing resource of the public cloud. Furthermore, our algorithm reports similar results as the nonsecure read mapping algorithms, including the alignment between reads and the reference genome, which can be directly used in the downstream analysis such as the inference of genomic variations. We implemented the algorithm in C++ and Python on a hybrid cloud system, in which the public cloud uses an Apache Spark system.

Cloud Computing for Protein-Ligand Binding Site Comparison

PubMed Central

2013-01-01

The proteome-wide analysis of protein-ligand binding sites and their interactions with ligands is important in structure-based drug design and in understanding ligand cross reactivity and toxicity. The well-known and commonly used software, SMAP, has been designed for 3D ligand binding site comparison and similarity searching of a structural proteome. SMAP can also predict drug side effects and reassign existing drugs to new indications. However, the computing scale of SMAP is limited. We have developed a high availability, high performance system that expands the comparison scale of SMAP. This cloud computing service, called Cloud-PLBS, combines the SMAP and Hadoop frameworks and is deployed on a virtual cloud computing platform. To handle the vast amount of experimental data on protein-ligand binding site pairs, Cloud-PLBS exploits the MapReduce paradigm as a management and parallelizing tool. Cloud-PLBS provides a web portal and scalability through which biologists can address a wide range of computer-intensive questions in biology and drug discovery. PMID:23762824

Cloud computing for protein-ligand binding site comparison.

PubMed

Hung, Che-Lun; Hua, Guan-Jie

2013-01-01

The proteome-wide analysis of protein-ligand binding sites and their interactions with ligands is important in structure-based drug design and in understanding ligand cross reactivity and toxicity. The well-known and commonly used software, SMAP, has been designed for 3D ligand binding site comparison and similarity searching of a structural proteome. SMAP can also predict drug side effects and reassign existing drugs to new indications. However, the computing scale of SMAP is limited. We have developed a high availability, high performance system that expands the comparison scale of SMAP. This cloud computing service, called Cloud-PLBS, combines the SMAP and Hadoop frameworks and is deployed on a virtual cloud computing platform. To handle the vast amount of experimental data on protein-ligand binding site pairs, Cloud-PLBS exploits the MapReduce paradigm as a management and parallelizing tool. Cloud-PLBS provides a web portal and scalability through which biologists can address a wide range of computer-intensive questions in biology and drug discovery.

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.

A new data collaboration service based on cloud computing security

NASA Astrophysics Data System (ADS)

Ying, Ren; Li, Hua-Wei; Wang, Li na

2017-09-01

With the rapid development of cloud computing, the storage and usage of data have undergone revolutionary changes. Data owners can store data in the cloud. While bringing convenience, it also brings many new challenges to cloud data security. A key issue is how to support a secure data collaboration service that supports access and updates to cloud data. This paper proposes a secure, efficient and extensible data collaboration service, which prevents data leaks in cloud storage, supports one to many encryption mechanisms, and also enables cloud data writing and fine-grained access control.

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.

CloudDOE: a user-friendly tool for deploying Hadoop clouds and analyzing high-throughput sequencing data with MapReduce.

PubMed

Chung, Wei-Chun; Chen, Chien-Chih; Ho, Jan-Ming; Lin, Chung-Yen; Hsu, Wen-Lian; Wang, Yu-Chun; Lee, D T; Lai, Feipei; Huang, Chih-Wei; Chang, Yu-Jung

2014-01-01

Explosive growth of next-generation sequencing data has resulted in ultra-large-scale data sets and ensuing computational problems. Cloud computing provides an on-demand and scalable environment for large-scale data analysis. Using a MapReduce framework, data and workload can be distributed via a network to computers in the cloud to substantially reduce computational latency. Hadoop/MapReduce has been successfully adopted in bioinformatics for genome assembly, mapping reads to genomes, and finding single nucleotide polymorphisms. Major cloud providers offer Hadoop cloud services to their users. However, it remains technically challenging to deploy a Hadoop cloud for those who prefer to run MapReduce programs in a cluster without built-in Hadoop/MapReduce. We present CloudDOE, a platform-independent software package implemented in Java. CloudDOE encapsulates technical details behind a user-friendly graphical interface, thus liberating scientists from having to perform complicated operational procedures. Users are guided through the user interface to deploy a Hadoop cloud within in-house computing environments and to run applications specifically targeted for bioinformatics, including CloudBurst, CloudBrush, and CloudRS. One may also use CloudDOE on top of a public cloud. CloudDOE consists of three wizards, i.e., Deploy, Operate, and Extend wizards. Deploy wizard is designed to aid the system administrator to deploy a Hadoop cloud. It installs Java runtime environment version 1.6 and Hadoop version 0.20.203, and initiates the service automatically. Operate wizard allows the user to run a MapReduce application on the dashboard list. To extend the dashboard list, the administrator may install a new MapReduce application using Extend wizard. CloudDOE is a user-friendly tool for deploying a Hadoop cloud. Its smart wizards substantially reduce the complexity and costs of deployment, execution, enhancement, and management. Interested users may collaborate to improve the source code of CloudDOE to further incorporate more MapReduce bioinformatics tools into CloudDOE and support next-generation big data open source tools, e.g., Hadoop BigTop and Spark. CloudDOE is distributed under Apache License 2.0 and is freely available at http://clouddoe.iis.sinica.edu.tw/.

CloudDOE: A User-Friendly Tool for Deploying Hadoop Clouds and Analyzing High-Throughput Sequencing Data with MapReduce

PubMed Central

Chung, Wei-Chun; Chen, Chien-Chih; Ho, Jan-Ming; Lin, Chung-Yen; Hsu, Wen-Lian; Wang, Yu-Chun; Lee, D. T.; Lai, Feipei; Huang, Chih-Wei; Chang, Yu-Jung

2014-01-01

Background Explosive growth of next-generation sequencing data has resulted in ultra-large-scale data sets and ensuing computational problems. Cloud computing provides an on-demand and scalable environment for large-scale data analysis. Using a MapReduce framework, data and workload can be distributed via a network to computers in the cloud to substantially reduce computational latency. Hadoop/MapReduce has been successfully adopted in bioinformatics for genome assembly, mapping reads to genomes, and finding single nucleotide polymorphisms. Major cloud providers offer Hadoop cloud services to their users. However, it remains technically challenging to deploy a Hadoop cloud for those who prefer to run MapReduce programs in a cluster without built-in Hadoop/MapReduce. Results We present CloudDOE, a platform-independent software package implemented in Java. CloudDOE encapsulates technical details behind a user-friendly graphical interface, thus liberating scientists from having to perform complicated operational procedures. Users are guided through the user interface to deploy a Hadoop cloud within in-house computing environments and to run applications specifically targeted for bioinformatics, including CloudBurst, CloudBrush, and CloudRS. One may also use CloudDOE on top of a public cloud. CloudDOE consists of three wizards, i.e., Deploy, Operate, and Extend wizards. Deploy wizard is designed to aid the system administrator to deploy a Hadoop cloud. It installs Java runtime environment version 1.6 and Hadoop version 0.20.203, and initiates the service automatically. Operate wizard allows the user to run a MapReduce application on the dashboard list. To extend the dashboard list, the administrator may install a new MapReduce application using Extend wizard. Conclusions CloudDOE is a user-friendly tool for deploying a Hadoop cloud. Its smart wizards substantially reduce the complexity and costs of deployment, execution, enhancement, and management. Interested users may collaborate to improve the source code of CloudDOE to further incorporate more MapReduce bioinformatics tools into CloudDOE and support next-generation big data open source tools, e.g., Hadoop BigTop and Spark. Availability: CloudDOE is distributed under Apache License 2.0 and is freely available at http://clouddoe.iis.sinica.edu.tw/. PMID:24897343

Cloud computing in medical imaging.

PubMed

Kagadis, George C; Kloukinas, Christos; Moore, Kevin; Philbin, Jim; Papadimitroulas, Panagiotis; Alexakos, Christos; Nagy, Paul G; Visvikis, Dimitris; Hendee, William R

2013-07-01

Over the past century technology has played a decisive role in defining, driving, and reinventing procedures, devices, and pharmaceuticals in healthcare. Cloud computing has been introduced only recently but is already one of the major topics of discussion in research and clinical settings. The provision of extensive, easily accessible, and reconfigurable resources such as virtual systems, platforms, and applications with low service cost has caught the attention of many researchers and clinicians. Healthcare researchers are moving their efforts to the cloud, because they need adequate resources to process, store, exchange, and use large quantities of medical data. This Vision 20/20 paper addresses major questions related to the applicability of advanced cloud computing in medical imaging. The paper also considers security and ethical issues that accompany cloud computing.

Facilitating NASA Earth Science Data Processing Using Nebula Cloud Computing

NASA Technical Reports Server (NTRS)

Pham, Long; Chen, Aijun; Kempler, Steven; Lynnes, Christopher; Theobald, Michael; Asghar, Esfandiari; Campino, Jane; Vollmer, Bruce

2011-01-01

Cloud Computing has been implemented in several commercial arenas. The NASA Nebula Cloud Computing platform is an Infrastructure as a Service (IaaS) built in 2008 at NASA Ames Research Center and 2010 at GSFC. Nebula is an open source Cloud platform intended to: a) Make NASA realize significant cost savings through efficient resource utilization, reduced energy consumption, and reduced labor costs. b) Provide an easier way for NASA scientists and researchers to efficiently explore and share large and complex data sets. c) Allow customers to provision, manage, and decommission computing capabilities on an as-needed bases

On Study of Building Smart Campus under Conditions of Cloud Computing and Internet of Things

NASA Astrophysics Data System (ADS)

Huang, Chao

2017-12-01

two new concepts in the information era are cloud computing and internet of things, although they are defined differently, they share close relationship. It is a new measure to realize leap-forward development of campus by virtue of cloud computing, internet of things and other internet technologies to build smart campus. This paper, centering on the construction of smart campus, analyzes and compares differences between network in traditional campus and that in smart campus, and makes proposals on how to build smart campus finally from the perspectives of cloud computing and internet of things.

Design and Implement of Astronomical Cloud Computing Environment In China-VO

NASA Astrophysics Data System (ADS)

Li, Changhua; Cui, Chenzhou; Mi, Linying; He, Boliang; Fan, Dongwei; Li, Shanshan; Yang, Sisi; Xu, Yunfei; Han, Jun; Chen, Junyi; Zhang, Hailong; Yu, Ce; Xiao, Jian; Wang, Chuanjun; Cao, Zihuang; Fan, Yufeng; Liu, Liang; Chen, Xiao; Song, Wenming; Du, Kangyu

2017-06-01

Astronomy cloud computing environment 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 virtualization technology, astronomy cloud computing environment was designed and implemented by China-VO team. It consists of five distributed nodes across the mainland of China. Astronomer can get compuitng and storage resource in this cloud computing environment. Through this environments, astronomer can easily search and analyze astronomical data collected by different telescopes and data centers , and avoid the large scale dataset transportation.

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.

Toward real-time Monte Carlo simulation using a commercial cloud computing infrastructure.

PubMed

Wang, Henry; Ma, Yunzhi; Pratx, Guillem; Xing, Lei

2011-09-07

Monte Carlo (MC) methods are the gold standard for modeling photon and electron transport in a heterogeneous medium; however, their computational cost prohibits their routine use in the clinic. Cloud computing, wherein computing resources are allocated on-demand from a third party, is a new approach for high performance computing and is implemented to perform ultra-fast MC calculation in radiation therapy. We deployed the EGS5 MC package in a commercial cloud environment. Launched from a single local computer with Internet access, a Python script allocates a remote virtual cluster. A handshaking protocol designates master and worker nodes. The EGS5 binaries and the simulation data are initially loaded onto the master node. The simulation is then distributed among independent worker nodes via the message passing interface, and the results aggregated on the local computer for display and data analysis. The described approach is evaluated for pencil beams and broad beams of high-energy electrons and photons. The output of cloud-based MC simulation is identical to that produced by single-threaded implementation. For 1 million electrons, a simulation that takes 2.58 h on a local computer can be executed in 3.3 min on the cloud with 100 nodes, a 47× speed-up. Simulation time scales inversely with the number of parallel nodes. The parallelization overhead is also negligible for large simulations. Cloud computing represents one of the most important recent advances in supercomputing technology and provides a promising platform for substantially improved MC simulation. In addition to the significant speed up, cloud computing builds a layer of abstraction for high performance parallel computing, which may change the way dose calculations are performed and radiation treatment plans are completed.

Using Google Applications as Part of Cloud Computing to Improve Knowledge and Teaching Skills of Faculty Members at the University of Bisha, Bisha, Saudi Arabia

ERIC Educational Resources Information Center

Alshihri, Bandar A.

2017-01-01

Cloud computing is a recent computing paradigm that has been integrated into the educational system. It provides numerous opportunities for delivering a variety of computing services in a way that has not been experienced before. The Google Company is among the top business companies that afford their cloud services by launching a number of…

CloudMC: a cloud computing application for Monte Carlo simulation.

PubMed

Miras, H; Jiménez, R; Miras, C; Gomà, C

2013-04-21

This work presents CloudMC, a cloud computing application-developed in Windows Azure®, the platform of the Microsoft® cloud-for the parallelization of Monte Carlo simulations in a dynamic virtual cluster. CloudMC is a web application designed to be independent of the Monte Carlo code in which the simulations are based-the simulations just need to be of the form: input files → executable → output files. To study the performance of CloudMC in Windows Azure®, Monte Carlo simulations with penelope were performed on different instance (virtual machine) sizes, and for different number of instances. The instance size was found to have no effect on the simulation runtime. It was also found that the decrease in time with the number of instances followed Amdahl's law, with a slight deviation due to the increase in the fraction of non-parallelizable time with increasing number of instances. A simulation that would have required 30 h of CPU on a single instance was completed in 48.6 min when executed on 64 instances in parallel (speedup of 37 ×). Furthermore, the use of cloud computing for parallel computing offers some advantages over conventional clusters: high accessibility, scalability and pay per usage. Therefore, it is strongly believed that cloud computing will play an important role in making Monte Carlo dose calculation a reality in future clinical practice.

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).

Applying analytic hierarchy process to assess healthcare-oriented cloud computing service systems.

PubMed

Liao, Wen-Hwa; Qiu, Wan-Li

2016-01-01

Numerous differences exist between the healthcare industry and other industries. Difficulties in the business operation of the healthcare industry have continually increased because of the volatility and importance of health care, changes to and requirements of health insurance policies, and the statuses of healthcare providers, which are typically considered not-for-profit organizations. Moreover, because of the financial risks associated with constant changes in healthcare payment methods and constantly evolving information technology, healthcare organizations must continually adjust their business operation objectives; therefore, cloud computing presents both a challenge and an opportunity. As a response to aging populations and the prevalence of the Internet in fast-paced contemporary societies, cloud computing can be used to facilitate the task of balancing the quality and costs of health care. To evaluate cloud computing service systems for use in health care, providing decision makers with a comprehensive assessment method for prioritizing decision-making factors is highly beneficial. Hence, this study applied the analytic hierarchy process, compared items related to cloud computing and health care, executed a questionnaire survey, and then classified the critical factors influencing healthcare cloud computing service systems on the basis of statistical analyses of the questionnaire results. The results indicate that the primary factor affecting the design or implementation of optimal cloud computing healthcare service systems is cost effectiveness, with the secondary factors being practical considerations such as software design and system architecture.

Capabilities and Advantages of Cloud Computing in the Implementation of Electronic Health Record.

PubMed

Ahmadi, Maryam; Aslani, Nasim

2018-01-01

With regard to the high cost of the Electronic Health Record (EHR), in recent years the use of new technologies, in particular cloud computing, has increased. The purpose of this study was to review systematically the studies conducted in the field of cloud computing. The present study was a systematic review conducted in 2017. Search was performed in the Scopus, Web of Sciences, IEEE, Pub Med and Google Scholar databases by combination keywords. From the 431 article that selected at the first, after applying the inclusion and exclusion criteria, 27 articles were selected for surveyed. Data gathering was done by a self-made check list and was analyzed by content analysis method. The finding of this study showed that cloud computing is a very widespread technology. It includes domains such as cost, security and privacy, scalability, mutual performance and interoperability, implementation platform and independence of Cloud Computing, ability to search and exploration, reducing errors and improving the quality, structure, flexibility and sharing ability. It will be effective for electronic health record. According to the findings of the present study, higher capabilities of cloud computing are useful in implementing EHR in a variety of contexts. It also provides wide opportunities for managers, analysts and providers of health information systems. Considering the advantages and domains of cloud computing in the establishment of HER, it is recommended to use this technology.

Capabilities and Advantages of Cloud Computing in the Implementation of Electronic Health Record

PubMed Central

Ahmadi, Maryam; Aslani, Nasim

2018-01-01

Background: With regard to the high cost of the Electronic Health Record (EHR), in recent years the use of new technologies, in particular cloud computing, has increased. The purpose of this study was to review systematically the studies conducted in the field of cloud computing. Methods: The present study was a systematic review conducted in 2017. Search was performed in the Scopus, Web of Sciences, IEEE, Pub Med and Google Scholar databases by combination keywords. From the 431 article that selected at the first, after applying the inclusion and exclusion criteria, 27 articles were selected for surveyed. Data gathering was done by a self-made check list and was analyzed by content analysis method. Results: The finding of this study showed that cloud computing is a very widespread technology. It includes domains such as cost, security and privacy, scalability, mutual performance and interoperability, implementation platform and independence of Cloud Computing, ability to search and exploration, reducing errors and improving the quality, structure, flexibility and sharing ability. It will be effective for electronic health record. Conclusion: According to the findings of the present study, higher capabilities of cloud computing are useful in implementing EHR in a variety of contexts. It also provides wide opportunities for managers, analysts and providers of health information systems. Considering the advantages and domains of cloud computing in the establishment of HER, it is recommended to use this technology. PMID:29719309

The Ethics of Cloud Computing.

PubMed

de Bruin, Boudewijn; Floridi, Luciano

2017-02-01

Cloud computing is rapidly gaining traction in business. It offers businesses online services on demand (such as Gmail, iCloud and Salesforce) and allows them to cut costs on hardware and IT support. This is the first paper in business ethics dealing with this new technology. It analyzes the informational duties of hosting companies that own and operate cloud computing datacentres (e.g., Amazon). It considers the cloud services providers leasing 'space in the cloud' from hosting companies (e.g., Dropbox, Salesforce). And it examines the business and private 'clouders' using these services. The first part of the paper argues that hosting companies, services providers and clouders have mutual informational (epistemic) obligations to provide and seek information about relevant issues such as consumer privacy, reliability of services, data mining and data ownership. The concept of interlucency is developed as an epistemic virtue governing ethically effective communication. The second part considers potential forms of government restrictions on or proscriptions against the development and use of cloud computing technology. Referring to the concept of technology neutrality, it argues that interference with hosting companies and cloud services providers is hardly ever necessary or justified. It is argued, too, however, that businesses using cloud services (e.g., banks, law firms, hospitals etc. storing client data in the cloud) will have to follow rather more stringent regulations.

Opportunities and challenges of cloud computing to improve health care services.

PubMed

Kuo, Alex Mu-Hsing

2011-09-21

Cloud computing is a new way of delivering computing resources and services. Many managers and experts believe that it can improve health care services, benefit health care research, and change the face of health information technology. However, as with any innovation, cloud computing should be rigorously evaluated before its widespread adoption. This paper discusses the concept and its current place in health care, and uses 4 aspects (management, technology, security, and legal) to evaluate the opportunities and challenges of this computing model. Strategic planning that could be used by a health organization to determine its direction, strategy, and resource allocation when it has decided to migrate from traditional to cloud-based health services is also discussed.

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

Structural Technology Evaluation and Analysis Program (STEAP). Delivery Order 0035: Dynamics and Control and Computational Design of Flapping Wing Micro Air Vehicles

DTIC Science & Technology

2012-10-01

library as a principal Requestor. The M3CT requestor is written in Java , leveraging the cross platform deployment capabilities needed for a broadly...each application to the Java programming language, the independently generated sources are wrapped with JNA or Groovy. The Java wrapping process...unlimited. Figure 13. Leveraging Languages Once the underlying product is available to the Java source as a library, the application leverages

Exploiting GPUs in Virtual Machine for BioCloud

PubMed Central

Jo, Heeseung; Jeong, Jinkyu; Lee, Myoungho; Choi, Dong Hoon

2013-01-01

Recently, biological applications start to be reimplemented into the applications which exploit many cores of GPUs for better computation performance. Therefore, by providing virtualized GPUs to VMs in cloud computing environment, many biological applications will willingly move into cloud environment to enhance their computation performance and utilize infinite cloud computing resource while reducing expenses for computations. In this paper, we propose a BioCloud system architecture that enables VMs to use GPUs in cloud environment. Because much of the previous research has focused on the sharing mechanism of GPUs among VMs, they cannot achieve enough performance for biological applications of which computation throughput is more crucial rather than sharing. The proposed system exploits the pass-through mode of PCI express (PCI-E) channel. By making each VM be able to access underlying GPUs directly, applications can show almost the same performance as when those are in native environment. In addition, our scheme multiplexes GPUs by using hot plug-in/out device features of PCI-E channel. By adding or removing GPUs in each VM in on-demand manner, VMs in the same physical host can time-share their GPUs. We implemented the proposed system using the Xen VMM and NVIDIA GPUs and showed that our prototype is highly effective for biological GPU applications in cloud environment. PMID:23710465

Exploiting GPUs in virtual machine for BioCloud.

PubMed

Jo, Heeseung; Jeong, Jinkyu; Lee, Myoungho; Choi, Dong Hoon

2013-01-01

Recently, biological applications start to be reimplemented into the applications which exploit many cores of GPUs for better computation performance. Therefore, by providing virtualized GPUs to VMs in cloud computing environment, many biological applications will willingly move into cloud environment to enhance their computation performance and utilize infinite cloud computing resource while reducing expenses for computations. In this paper, we propose a BioCloud system architecture that enables VMs to use GPUs in cloud environment. Because much of the previous research has focused on the sharing mechanism of GPUs among VMs, they cannot achieve enough performance for biological applications of which computation throughput is more crucial rather than sharing. The proposed system exploits the pass-through mode of PCI express (PCI-E) channel. By making each VM be able to access underlying GPUs directly, applications can show almost the same performance as when those are in native environment. In addition, our scheme multiplexes GPUs by using hot plug-in/out device features of PCI-E channel. By adding or removing GPUs in each VM in on-demand manner, VMs in the same physical host can time-share their GPUs. We implemented the proposed system using the Xen VMM and NVIDIA GPUs and showed that our prototype is highly effective for biological GPU applications in cloud environment.

A Strategic Approach to Network Defense: Framing the Cloud

DTIC Science & Technology

2011-03-10

accepted network defensive principles, to reduce risks associated with emerging virtualization capabilities and scalability of cloud computing . This expanded...defensive framework can assist enterprise networking and cloud computing architects to better design more secure systems.

Trusted computing strengthens cloud authentication.

PubMed

Ghazizadeh, Eghbal; Zamani, Mazdak; Ab Manan, Jamalul-lail; Alizadeh, Mojtaba

2014-01-01

Cloud computing is a new generation of technology which is designed to provide the commercial necessities, solve the IT management issues, and run the appropriate applications. Another entry on the list of cloud functions which has been handled internally is Identity Access Management (IAM). Companies encounter IAM as security challenges while adopting more technologies became apparent. Trust Multi-tenancy and trusted computing based on a Trusted Platform Module (TPM) are great technologies for solving the trust and security concerns in the cloud identity environment. Single sign-on (SSO) and OpenID have been released to solve security and privacy problems for cloud identity. This paper proposes the use of trusted computing, Federated Identity Management, and OpenID Web SSO to solve identity theft in the cloud. Besides, this proposed model has been simulated in .Net environment. Security analyzing, simulation, and BLP confidential model are three ways to evaluate and analyze our proposed model.

Trusted Computing Strengthens Cloud Authentication

PubMed Central

2014-01-01

Cloud computing is a new generation of technology which is designed to provide the commercial necessities, solve the IT management issues, and run the appropriate applications. Another entry on the list of cloud functions which has been handled internally is Identity Access Management (IAM). Companies encounter IAM as security challenges while adopting more technologies became apparent. Trust Multi-tenancy and trusted computing based on a Trusted Platform Module (TPM) are great technologies for solving the trust and security concerns in the cloud identity environment. Single sign-on (SSO) and OpenID have been released to solve security and privacy problems for cloud identity. This paper proposes the use of trusted computing, Federated Identity Management, and OpenID Web SSO to solve identity theft in the cloud. Besides, this proposed model has been simulated in .Net environment. Security analyzing, simulation, and BLP confidential model are three ways to evaluate and analyze our proposed model. PMID:24701149

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

Cloud Computing for Geosciences--GeoCloud for standardized geospatial service platforms (Invited)

NASA Astrophysics Data System (ADS)

Nebert, D. D.; Huang, Q.; Yang, C.

2013-12-01

The 21st century geoscience faces challenges of Big Data, spike computing requirements (e.g., when natural disaster happens), and sharing resources through cyberinfrastructure across different organizations (Yang et al., 2011). With flexibility and cost-efficiency of computing resources a primary concern, cloud computing emerges as a promising solution to provide core capabilities to address these challenges. Many governmental and federal agencies are adopting cloud technologies to cut costs and to make federal IT operations more efficient (Huang et al., 2010). However, it is still difficult for geoscientists to take advantage of the benefits of cloud computing to facilitate the scientific research and discoveries. This presentation reports using GeoCloud to illustrate the process and strategies used in building a common platform for geoscience communities to enable the sharing, integration of geospatial data, information and knowledge across different domains. GeoCloud is an annual incubator project coordinated by the Federal Geographic Data Committee (FGDC) in collaboration with the U.S. General Services Administration (GSA) and the Department of Health and Human Services. It is designed as a staging environment to test and document the deployment of a common GeoCloud community platform that can be implemented by multiple agencies. With these standardized virtual geospatial servers, a variety of government geospatial applications can be quickly migrated to the cloud. In order to achieve this objective, multiple projects are nominated each year by federal agencies as existing public-facing geospatial data services. From the initial candidate projects, a set of common operating system and software requirements was identified as the baseline for platform as a service (PaaS) packages. Based on these developed common platform packages, each project deploys and monitors its web application, develops best practices, and documents cost and performance information. This paper presents the background, architectural design, and activities of GeoCloud in support of the Geospatial Platform Initiative. System security strategies and approval processes for migrating federal geospatial data, information, and applications into cloud, and cost estimation for cloud operations are covered. Finally, some lessons learned from the GeoCloud project are discussed as reference for geoscientists to consider in the adoption of cloud computing.

Infrastructures for Distributed Computing: the case of BESIII

NASA Astrophysics Data System (ADS)

Pellegrino, J.

2018-05-01

The BESIII is an electron-positron collision experiment hosted at BEPCII in Beijing and aimed to investigate Tau-Charm physics. Now BESIII has been running for several years and gathered more than 1PB raw data. In order to analyze these data and perform massive Monte Carlo simulations, a large amount of computing and storage resources is needed. The distributed computing system is based up on DIRAC and it is in production since 2012. It integrates computing and storage resources from different institutes and a variety of resource types such as cluster, grid, cloud or volunteer computing. About 15 sites from BESIII Collaboration from all over the world joined this distributed computing infrastructure, giving a significant contribution to the IHEP computing facility. Nowadays cloud computing is playing a key role in the HEP computing field, due to its scalability and elasticity. Cloud infrastructures take advantages of several tools, such as VMDirac, to manage virtual machines through cloud managers according to the job requirements. With the virtually unlimited resources from commercial clouds, the computing capacity could scale accordingly in order to deal with any burst demands. General computing models have been discussed in the talk and are addressed herewith, with particular focus on the BESIII infrastructure. Moreover new computing tools and upcoming infrastructures will be addressed.

Examining Effects of Virtual Machine Settings on Voice over Internet Protocol in a Private Cloud Environment

ERIC Educational Resources Information Center

Liao, Yuan

2011-01-01

The virtualization of computing resources, as represented by the sustained growth of cloud computing, continues to thrive. Information Technology departments are building their private clouds due to the perception of significant cost savings by managing all physical computing resources from a single point and assigning them to applications or…

Bootstrapping Development of a Cloud-Based Spoken Dialog System in the Educational Domain from Scratch Using Crowdsourced Data. Research Report. ETS RR-16-16

ERIC Educational Resources Information Center

Ramanarayanan, Vikram; Suendermann-Oeft, David; Lange, Patrick; Ivanov, Alexei V.; Evanini, Keelan; Yu, Zhou; Tsuprun, Eugene; Qian, Yao

2016-01-01

We propose a crowdsourcing-based framework to iteratively and rapidly bootstrap a dialog system from scratch for a new domain. We leverage the open-source modular HALEF dialog system to deploy dialog applications. We illustrate the usefulness of this framework using four different prototype dialog items with applications in the educational domain…

Cloud Library for Directed Probabilistic Graphical Models

DTIC Science & Technology

2014-10-01

integrated into popular big - data analytical software like Hive to lower developer friction, leveraging both the data access primitives in Hive and BayesDB...Government drawings, specifications, or other data included in this document for any purpose other than Government procurement does not in any way...obligate the U.S. Government. The fact that the Government formulated or supplied the drawings, specifications, or other data does not license the holder

SDTCP: Towards Datacenter TCP Congestion Control with SDN for IoT Applications.

PubMed

Lu, Yifei; Ling, Zhen; Zhu, Shuhong; Tang, Ling

2017-01-08

The Internet of Things (IoT) has gained popularity in recent years. Today's IoT applications are now increasingly deployed in cloud platforms to perform Big Data analytics. In cloud data center networks (DCN), TCP incast usually happens when multiple senders simultaneously communicate with a single receiver. However, when TCP incast happens, DCN may suffer from both throughput collapse for TCP burst flows and temporary starvation for TCP background flows. In this paper, we propose a software defined network (SDN)-based TCP congestion control mechanism, referred to as SDTCP, to leverage the features, e.g., centralized control methods and the global view of the network, in order to solve the TCP incast problems. When we detect network congestion on an OpenFlow switch, our controller can select the background flows and reduce their bandwidth by adjusting the advertised window of TCP ACK packets of the corresponding background flows so as to reserve more bandwidth for burst flows. SDTCP is transparent to the end systems and can accurately decelerate the rate of background flows by leveraging the global view of the network gained via SDN. The experiments demonstrate that our SDTCP can provide high tolerance for burst flows and achieve better flow completion time for short flows. Therefore, SDTCP is an effective and scalable solution for the TCP incast problem.

Monitoring of IaaS and scientific applications on the Cloud using the Elasticsearch ecosystem

NASA Astrophysics Data System (ADS)

Bagnasco, S.; Berzano, D.; Guarise, A.; Lusso, S.; Masera, M.; Vallero, S.

2015-05-01

The private Cloud at the Torino INFN computing centre offers IaaS services to different scientific computing applications. The infrastructure is managed with the OpenNebula cloud controller. The main stakeholders of the facility are a grid Tier-2 site for the ALICE collaboration at LHC, an interactive analysis facility for the same experiment and a grid Tier-2 site for the BES-III collaboration, plus an increasing number of other small tenants. Besides keeping track of the usage, the automation of dynamic allocation of resources to tenants requires detailed monitoring and accounting of the resource usage. As a first investigation towards this, we set up a monitoring system to inspect the site activities both in terms of IaaS and applications running on the hosted virtual instances. For this purpose we used the Elasticsearch, Logstash and Kibana stack. In the current implementation, the heterogeneous accounting information is fed to different MySQL databases and sent to Elasticsearch via a custom Logstash plugin. For the IaaS metering, we developed sensors for the OpenNebula API. The IaaS level information gathered through the API is sent to the MySQL database through an ad-hoc developed RESTful web service, which is also used for other accounting purposes. Concerning the application level, we used the Root plugin TProofMonSenderSQL to collect accounting data from the interactive analysis facility. The BES-III virtual instances used to be monitored with Zabbix, as a proof of concept we also retrieve the information contained in the Zabbix database. Each of these three cases is indexed separately in Elasticsearch. We are now starting to consider dismissing the intermediate level provided by the SQL database and evaluating a NoSQL option as a unique central database for all the monitoring information. We setup a set of Kibana dashboards with pre-defined queries in order to monitor the relevant information in each case. In this way we have achieved a uniform monitoring interface for both the IaaS and the scientific applications, mostly leveraging off-the-shelf tools.

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.

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.

Exploration of cloud computing late start LDRD #149630 : Raincoat. v. 2.1.

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

Echeverria, Victor T.; Metral, Michael David; Leger, Michelle A.

This report contains documentation from an interoperability study conducted under the Late Start LDRD 149630, Exploration of Cloud Computing. A small late-start LDRD from last year resulted in a study (Raincoat) on using Virtual Private Networks (VPNs) to enhance security in a hybrid cloud environment. Raincoat initially explored the use of OpenVPN on IPv4 and demonstrates that it is possible to secure the communication channel between two small 'test' clouds (a few nodes each) at New Mexico Tech and Sandia. We extended the Raincoat study to add IPSec support via Vyatta routers, to interface with a public cloud (Amazon Elasticmore » Compute Cloud (EC2)), and to be significantly more scalable than the previous iteration. The study contributed to our understanding of interoperability in a hybrid cloud.« less

Dynamic partitioning as a way to exploit new computing paradigms: the cloud use case.

NASA Astrophysics Data System (ADS)

Ciaschini, Vincenzo; Dal Pra, Stefano; dell'Agnello, Luca

2015-12-01

The WLCG community and many groups in the HEP community have based their computing strategy on the Grid paradigm, which proved successful and still ensures its goals. However, Grid technology has not spread much over other communities; in the commercial world, the cloud paradigm is the emerging way to provide computing services. WLCG experiments aim to achieve integration of their existing current computing model with cloud deployments and take advantage of the so-called opportunistic resources (including HPC facilities) which are usually not Grid compliant. One missing feature in the most common cloud frameworks, is the concept of job scheduler, which plays a key role in a traditional computing centre, by enabling a fairshare based access at the resources to the experiments in a scenario where demand greatly outstrips availability. At CNAF we are investigating the possibility to access the Tier-1 computing resources as an OpenStack based cloud service. The system, exploiting the dynamic partitioning mechanism already being used to enable Multicore computing, allowed us to avoid a static splitting of the computing resources in the Tier-1 farm, while permitting a share friendly approach. The hosts in a dynamically partitioned farm may be moved to or from the partition, according to suitable policies for request and release of computing resources. Nodes being requested in the partition switch their role and become available to play a different one. In the cloud use case hosts may switch from acting as Worker Node in the Batch system farm to cloud compute node member, made available to tenants. In this paper we describe the dynamic partitioning concept, its implementation and integration with our current batch system, LSF.

GATE Monte Carlo simulation in a cloud computing environment

NASA Astrophysics Data System (ADS)

Rowedder, Blake Austin

The GEANT4-based GATE is a unique and powerful Monte Carlo (MC) platform, which provides a single code library allowing the simulation of specific medical physics applications, e.g. PET, SPECT, CT, radiotherapy, and hadron therapy. However, this rigorous yet flexible platform is used only sparingly in the clinic due to its lengthy calculation time. By accessing the powerful computational resources of a cloud computing environment, GATE's runtime can be significantly reduced to clinically feasible levels without the sizable investment of a local high performance cluster. This study investigated a reliable and efficient execution of GATE MC simulations using a commercial cloud computing services. Amazon's Elastic Compute Cloud was used to launch several nodes equipped with GATE. Job data was initially broken up on the local computer, then uploaded to the worker nodes on the cloud. The results were automatically downloaded and aggregated on the local computer for display and analysis. Five simulations were repeated for every cluster size between 1 and 20 nodes. Ultimately, increasing cluster size resulted in a decrease in calculation time that could be expressed with an inverse power model. Comparing the benchmark results to the published values and error margins indicated that the simulation results were not affected by the cluster size and thus that integrity of a calculation is preserved in a cloud computing environment. The runtime of a 53 minute long simulation was decreased to 3.11 minutes when run on a 20-node cluster. The ability to improve the speed of simulation suggests that fast MC simulations are viable for imaging and radiotherapy applications. With high power computing continuing to lower in price and accessibility, implementing Monte Carlo techniques with cloud computing for clinical applications will continue to become more attractive.

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.

Cloud Computing for the Grid: GridControl: A Software Platform to Support the Smart Grid

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

None

GENI Project: Cornell University is creating a new software platform for grid operators called GridControl that will utilize cloud computing to more efficiently control the grid. In a cloud computing system, there are minimal hardware and software demands on users. The user can tap into a network of computers that is housed elsewhere (the cloud) and the network runs computer applications for the user. The user only needs interface software to access all of the cloud’s data resources, which can be as simple as a web browser. Cloud computing can reduce costs, facilitate innovation through sharing, empower users, and improvemore » the overall reliability of a dispersed system. Cornell’s GridControl will focus on 4 elements: delivering the state of the grid to users quickly and reliably; building networked, scalable grid-control software; tailoring services to emerging smart grid uses; and simulating smart grid behavior under various conditions.« less

Bioinformatics clouds for big data manipulation.

PubMed

Dai, Lin; Gao, Xin; Guo, Yan; Xiao, Jingfa; Zhang, Zhang

2012-11-28

As advances in life sciences and information technology bring profound influences on bioinformatics due to its interdisciplinary nature, bioinformatics is experiencing a new leap-forward from in-house computing infrastructure into utility-supplied cloud computing delivered over the Internet, in order to handle the vast quantities of biological data generated by high-throughput experimental technologies. Albeit relatively new, cloud computing promises to address big data storage and analysis issues in the bioinformatics field. Here we review extant cloud-based services in bioinformatics, classify them into Data as a Service (DaaS), Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS), and present our perspectives on the adoption of cloud computing in bioinformatics. This article was reviewed by Frank Eisenhaber, Igor Zhulin, and Sandor Pongor.

The Deep South Clouds & Aerosols project: Improving the modelling of clouds in the Southern Ocean region

NASA Astrophysics Data System (ADS)

Morgenstern, Olaf; McDonald, Adrian; Harvey, Mike; Davies, Roger; Katurji, Marwan; Varma, Vidya; Williams, Jonny

2016-04-01

Southern-Hemisphere climate projections are subject to persistent climate model biases affecting the large majority of contemporary climate models, which degrade the reliability of these projections, particularly at the regional scale. Southern-Hemisphere specific problems include the fact that satellite-based observations comparisons with model output indicate that cloud occurrence above the Southern Ocean is substantially underestimated, with consequences for the radiation balance, sea surface temperatures, sea ice, and the position of storm tracks. The Southern-Ocean and Antarctic region is generally characterized by an acute paucity of surface-based and airborne observations, further complicating the situation. In recognition of this and other Southern-Hemisphere specific problems with climate modelling, the New Zealand Government has launched the Deep South National Science Challenge, whose purpose is to develop a new Earth System Model which reduces these very large radiative forcing problems associated with erroneous clouds. The plan is to conduct a campaign of targeted observations in the Southern Ocean region, leveraging off international measurement campaigns in this area, and using these and existing measurements of cloud and aerosol properties to improve the representation of clouds in the nascent New Zealand Earth System Model. Observations and model development will target aerosol physics and chemistry, particularly sulphate, sea salt, and non-sulphate organic aerosol, its interactions with clouds, and cloud microphysics. The hypothesis is that the cloud schemes in most GCMs are trained on Northern-Hemisphere data characterized by substantial anthropogenic or terrestrial aerosol-related influences which are almost completely absent in the Deep South.

SU-E-T-222: Computational Optimization of Monte Carlo Simulation On 4D Treatment Planning Using the Cloud Computing Technology

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

Chow, J

Purpose: This study evaluated the efficiency of 4D lung radiation treatment planning using Monte Carlo simulation on the cloud. The EGSnrc Monte Carlo code was used in dose calculation on the 4D-CT image set. Methods: 4D lung radiation treatment plan was created by the DOSCTP linked to the cloud, based on the Amazon elastic compute cloud platform. Dose calculation was carried out by Monte Carlo simulation on the 4D-CT image set on the cloud, and results were sent to the FFD4D image deformation program for dose reconstruction. The dependence of computing time for treatment plan on the number of computemore » node was optimized with variations of the number of CT image set in the breathing cycle and dose reconstruction time of the FFD4D. Results: It is found that the dependence of computing time on the number of compute node was affected by the diminishing return of the number of node used in Monte Carlo simulation. Moreover, the performance of the 4D treatment planning could be optimized by using smaller than 10 compute nodes on the cloud. The effects of the number of image set and dose reconstruction time on the dependence of computing time on the number of node were not significant, as more than 15 compute nodes were used in Monte Carlo simulations. Conclusion: The issue of long computing time in 4D treatment plan, requiring Monte Carlo dose calculations in all CT image sets in the breathing cycle, can be solved using the cloud computing technology. It is concluded that the optimized number of compute node selected in simulation should be between 5 and 15, as the dependence of computing time on the number of node is significant.« less

Privacy and Data Security under Cloud Computing Arrangements: The Legal Framework and Practical Do's and Don'ts

ERIC Educational Resources Information Center

Buckman, Joel; Gold, Stephanie

2012-01-01

This article outlines privacy and data security compliance issues facing postsecondary education institutions when they utilize cloud computing and concludes with a practical list of do's and dont's. Cloud computing does not change an institution's privacy and data security obligations. It does involve reliance on a third party, which requires an…

Cloud-Based versus Local-Based Web Development Education: An Experimental Study in Learning Experience

ERIC Educational Resources Information Center

Pike, Ronald E.; Pittman, Jason M.; Hwang, Drew

2017-01-01

This paper investigates the use of a cloud computing environment to facilitate the teaching of web development at a university in the Southwestern United States. A between-subjects study of students in a web development course was conducted to assess the merits of a cloud computing environment instead of personal computers for developing websites.…

Assessing Affordances of Selected Cloud Computing Tools for Language Teacher Education in Nigeria

ERIC Educational Resources Information Center

Ofemile, Abdulmalik Yusuf

2015-01-01

This paper reports part of a study that hoped to understand Teacher Educators' (TE) assessment of the affordances of selected cloud computing tools ranked among the top 100 for the year 2010. Research has shown that ICT and by extension cloud computing has positive impacts on daily life and this informed the Nigerian government's policy to…

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.

An Agro-Climatological Early Warning Tool Based on the Google Earth Engine to Support Regional Food Security Analysis

NASA Astrophysics Data System (ADS)

Landsfeld, M. F.; Daudert, B.; Friedrichs, M.; Morton, C.; Hegewisch, K.; Husak, G. J.; Funk, C. C.; Peterson, P.; Huntington, J. L.; Abatzoglou, J. T.; Verdin, J. P.; Williams, E. L.

2015-12-01

The Famine Early Warning Systems Network (FEWS NET) focuses on food insecurity in developing nations and provides objective, evidence based analysis to help government decision-makers and relief agencies plan for and respond to humanitarian emergencies. The Google Earth Engine (GEE) is a platform provided by Google Inc. to support scientific research and analysis of environmental data in their cloud environment. The intent is to allow scientists and independent researchers to mine massive collections of environmental data and leverage Google's vast computational resources to detect changes and monitor the Earth's surface and climate. GEE hosts an enormous amount of satellite imagery and climate archives, one of which is the Climate Hazards Group Infrared Precipitation with Stations dataset (CHIRPS). The CHIRPS dataset is land based, quasi-global (latitude 50N-50S), 0.05 degree resolution, and has a relatively long term period of record (1981-present). CHIRPS is on a continuous monthly feed into the GEE as new data fields are generated each month. This precipitation dataset is a key input for FEWS NET monitoring and forecasting efforts. FEWS NET intends to leverage the GEE in order to provide analysts and scientists with flexible, interactive tools to aid in their monitoring and research efforts. These scientists often work in bandwidth limited regions, so lightweight Internet tools and services that bypass the need for downloading massive datasets to analyze them, are preferred for their work. The GEE provides just this type of service. We present a tool designed specifically for FEWS NET scientists to be utilized interactively for investigating and monitoring for agro-climatological issues. We are able to utilize the enormous GEE computing power to generate on-the-fly statistics to calculate precipitation anomalies, z-scores, percentiles and band ratios, and allow the user to interactively select custom areas for statistical time series comparisons and predictions.

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.

Government Cloud Computing Policies: Potential Opportunities for Advancing Military Biomedical Research.

PubMed

Lebeda, Frank J; Zalatoris, Jeffrey J; Scheerer, Julia B

2018-02-07

This position paper summarizes the development and the present status of Department of Defense (DoD) and other government policies and guidances regarding cloud computing services. Due to the heterogeneous and growing biomedical big datasets, cloud computing services offer an opportunity to mitigate the associated storage and analysis requirements. Having on-demand network access to a shared pool of flexible computing resources creates a consolidated system that should reduce potential duplications of effort in military biomedical research. Interactive, online literature searches were performed with Google, at the Defense Technical Information Center, and at two National Institutes of Health research portfolio information sites. References cited within some of the collected documents also served as literature resources. We gathered, selected, and reviewed DoD and other government cloud computing policies and guidances published from 2009 to 2017. These policies were intended to consolidate computer resources within the government and reduce costs by decreasing the number of federal data centers and by migrating electronic data to cloud systems. Initial White House Office of Management and Budget information technology guidelines were developed for cloud usage, followed by policies and other documents from the DoD, the Defense Health Agency, and the Armed Services. Security standards from the National Institute of Standards and Technology, the Government Services Administration, the DoD, and the Army were also developed. Government Services Administration and DoD Inspectors General monitored cloud usage by the DoD. A 2016 Government Accountability Office report characterized cloud computing as being economical, flexible and fast. A congressionally mandated independent study reported that the DoD was active in offering a wide selection of commercial cloud services in addition to its milCloud system. Our findings from the Department of Health and Human Services indicated that the security infrastructure in cloud services may be more compliant with the Health Insurance Portability and Accountability Act of 1996 regulations than traditional methods. To gauge the DoD's adoption of cloud technologies proposed metrics included cost factors, ease of use, automation, availability, accessibility, security, and policy compliance. Since 2009, plans and policies were developed for the use of cloud technology to help consolidate and reduce the number of data centers which were expected to reduce costs, improve environmental factors, enhance information technology security, and maintain mission support for service members. Cloud technologies were also expected to improve employee efficiency and productivity. Federal cloud computing policies within the last decade also offered increased opportunities to advance military healthcare. It was assumed that these opportunities would benefit consumers of healthcare and health science data by allowing more access to centralized cloud computer facilities to store, analyze, search and share relevant data, to enhance standardization, and to reduce potential duplications of effort. We recommend that cloud computing be considered by DoD biomedical researchers for increasing connectivity, presumably by facilitating communications and data sharing, among the various intra- and extramural laboratories. We also recommend that policies and other guidances be updated to include developing additional metrics that will help stakeholders evaluate the above mentioned assumptions and expectations. Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2018. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Performance, Agility and Cost of Cloud Computing Services for NASA GES DISC Giovanni Application

NASA Astrophysics Data System (ADS)

Pham, L.; Chen, A.; Wharton, S.; Winter, E. L.; Lynnes, C.

2013-12-01

The NASA Goddard Earth Science Data and Information Services Center (GES DISC) is investigating the performance, agility and cost of Cloud computing for GES DISC applications. Giovanni (Geospatial Interactive Online Visualization ANd aNalysis Infrastructure), one of the core applications at the GES DISC for online climate-related Earth science data access, subsetting, analysis, visualization, and downloading, was used to evaluate the feasibility and effort of porting an application to the Amazon Cloud Services platform. The performance and the cost of running Giovanni on the Amazon Cloud were compared to similar parameters for the GES DISC local operational system. A Giovanni Time-Series analysis of aerosol absorption optical depth (388nm) from OMI (Ozone Monitoring Instrument)/Aura was selected for these comparisons. All required data were pre-cached in both the Cloud and local system to avoid data transfer delays. The 3-, 6-, 12-, and 24-month data were used for analysis on the Cloud and local system respectively, and the processing times for the analysis were used to evaluate system performance. To investigate application agility, Giovanni was installed and tested on multiple Cloud platforms. The cost of using a Cloud computing platform mainly consists of: computing, storage, data requests, and data transfer in/out. The Cloud computing cost is calculated based on the hourly rate, and the storage cost is calculated based on the rate of Gigabytes per month. Cost for incoming data transfer is free, and for data transfer out, the cost is based on the rate in Gigabytes. The costs for a local server system consist of buying hardware/software, system maintenance/updating, and operating cost. The results showed that the Cloud platform had a 38% better performance and cost 36% less than the local system. This investigation shows the potential of cloud computing to increase system performance and lower the overall cost of system management.

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..

Opportunities and Challenges of Cloud Computing to Improve Health Care Services

PubMed Central

2011-01-01

Cloud computing is a new way of delivering computing resources and services. Many managers and experts believe that it can improve health care services, benefit health care research, and change the face of health information technology. However, as with any innovation, cloud computing should be rigorously evaluated before its widespread adoption. This paper discusses the concept and its current place in health care, and uses 4 aspects (management, technology, security, and legal) to evaluate the opportunities and challenges of this computing model. Strategic planning that could be used by a health organization to determine its direction, strategy, and resource allocation when it has decided to migrate from traditional to cloud-based health services is also discussed. PMID:21937354

Investigating the Use of Cloudbursts for High-Throughput Medical Image Registration

PubMed Central

Kim, Hyunjoo; Parashar, Manish; Foran, David J.; Yang, Lin

2010-01-01

This paper investigates the use of clouds and autonomic cloudbursting to support a medical image registration. The goal is to enable a virtual computational cloud that integrates local computational environments and public cloud services on-the-fly, and support image registration requests from different distributed researcher groups with varied computational requirements and QoS constraints. The virtual cloud essentially implements shared and coordinated task-spaces, which coordinates the scheduling of jobs submitted by a dynamic set of research groups to their local job queues. A policy-driven scheduling agent uses the QoS constraints along with performance history and the state of the resources to determine the appropriate size and mix of the public and private cloud resource that should be allocated to a specific request. The virtual computational cloud and the medical image registration service have been developed using the CometCloud engine and have been deployed on a combination of private clouds at Rutgers University and the Cancer Institute of New Jersey and Amazon EC2. An experimental evaluation is presented and demonstrates the effectiveness of autonomic cloudbursts and policy-based autonomic scheduling for this application. PMID:20640235

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.

The Metadata Cloud: The Last Piece of a Distributed Data System Model

NASA Astrophysics Data System (ADS)

King, T. A.; Cecconi, B.; Hughes, J. S.; Walker, R. J.; Roberts, D.; Thieman, J. R.; Joy, S. P.; Mafi, J. N.; Gangloff, M.

2012-12-01

Distributed data systems have existed ever since systems were networked together. Over the years the model for distributed data systems have evolved from basic file transfer to client-server to multi-tiered to grid and finally to cloud based systems. Initially metadata was tightly coupled to the data either by embedding the metadata in the same file containing the data or by co-locating the metadata in commonly named files. As the sources of data multiplied, data volumes have increased and services have specialized to improve efficiency; a cloud system model has emerged. In a cloud system computing and storage are provided as services with accessibility emphasized over physical location. Computation and data clouds are common implementations. Effectively using the data and computation capabilities requires metadata. When metadata is stored separately from the data; a metadata cloud is formed. With a metadata cloud information and knowledge about data resources can migrate efficiently from system to system, enabling services and allowing the data to remain efficiently stored until used. This is especially important with "Big Data" where movement of the data is limited by bandwidth. We examine how the metadata cloud completes a general distributed data system model, how standards play a role and relate this to the existing types of cloud computing. We also look at the major science data systems in existence and compare each to the generalized cloud system model.

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.

Scientific Services on the Cloud

NASA Astrophysics Data System (ADS)

Chapman, David; Joshi, Karuna P.; Yesha, Yelena; Halem, Milt; Yesha, Yaacov; Nguyen, Phuong

Scientific Computing was one of the first every applications for parallel and distributed computation. To this date, scientific applications remain some of the most compute intensive, and have inspired creation of petaflop compute infrastructure such as the Oak Ridge Jaguar and Los Alamos RoadRunner. Large dedicated hardware infrastructure has become both a blessing and a curse to the scientific community. Scientists are interested in cloud computing for much the same reason as businesses and other professionals. The hardware is provided, maintained, and administrated by a third party. Software abstraction and virtualization provide reliability, and fault tolerance. Graduated fees allow for multi-scale prototyping and execution. Cloud computing resources are only a few clicks away, and by far the easiest high performance distributed platform to gain access to. There may still be dedicated infrastructure for ultra-scale science, but the cloud can easily play a major part of the scientific computing initiative.

Effects of Atmospheric Water Vapor and Clouds on NOAA (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High Resolution Radiometer) Satellite Data.

DTIC Science & Technology

1984-07-01

aerosols and sub pixel-sized clouds all tend to increase Channel 1 with respect to Channel 2 and reduce the computed VIN. Further, the Guide states that... computation of the VIN. Large scale cloud contamination of pixels, while diffi- cult to correct for, can at least be monitored and affected pixels...techniques have been developed for computer cloud screening. See, for example, Horvath et al. (1982), Gray and McCrary (1981a) and Nixon et al. (1983

Cloud Bursting with GlideinWMS: Means to satisfy ever increasing computing needs for Scientific Workflows

NASA Astrophysics Data System (ADS)

Mhashilkar, Parag; Tiradani, Anthony; Holzman, Burt; Larson, Krista; Sfiligoi, Igor; Rynge, Mats

2014-06-01

Scientific communities have been in the forefront of adopting new technologies and methodologies in the computing. Scientific computing has influenced how science is done today, achieving breakthroughs that were impossible to achieve several decades ago. For the past decade several such communities in the Open Science Grid (OSG) and the European Grid Infrastructure (EGI) have been using GlideinWMS to run complex application workflows to effectively share computational resources over the grid. GlideinWMS is a pilot-based workload management system (WMS) that creates on demand, a dynamically sized overlay HTCondor batch system on grid resources. At present, the computational resources shared over the grid are just adequate to sustain the computing needs. We envision that the complexity of the science driven by "Big Data" will further push the need for computational resources. To fulfill their increasing demands and/or to run specialized workflows, some of the big communities like CMS are investigating the use of cloud computing as Infrastructure-As-A-Service (IAAS) with GlideinWMS as a potential alternative to fill the void. Similarly, communities with no previous access to computing resources can use GlideinWMS to setup up a batch system on the cloud infrastructure. To enable this, the architecture of GlideinWMS has been extended to enable support for interfacing GlideinWMS with different Scientific and commercial cloud providers like HLT, FutureGrid, FermiCloud and Amazon EC2. In this paper, we describe a solution for cloud bursting with GlideinWMS. The paper describes the approach, architectural changes and lessons learned while enabling support for cloud infrastructures in GlideinWMS.

Cloud Bursting with GlideinWMS: Means to satisfy ever increasing computing needs for Scientific Workflows

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

Mhashilkar, Parag; Tiradani, Anthony; Holzman, Burt

Scientific communities have been in the forefront of adopting new technologies and methodologies in the computing. Scientific computing has influenced how science is done today, achieving breakthroughs that were impossible to achieve several decades ago. For the past decade several such communities in the Open Science Grid (OSG) and the European Grid Infrastructure (EGI) have been using GlideinWMS to run complex application workflows to effectively share computational resources over the grid. GlideinWMS is a pilot-based workload management system (WMS) that creates on demand, a dynamically sized overlay HTCondor batch system on grid resources. At present, the computational resources shared overmore » the grid are just adequate to sustain the computing needs. We envision that the complexity of the science driven by 'Big Data' will further push the need for computational resources. To fulfill their increasing demands and/or to run specialized workflows, some of the big communities like CMS are investigating the use of cloud computing as Infrastructure-As-A-Service (IAAS) with GlideinWMS as a potential alternative to fill the void. Similarly, communities with no previous access to computing resources can use GlideinWMS to setup up a batch system on the cloud infrastructure. To enable this, the architecture of GlideinWMS has been extended to enable support for interfacing GlideinWMS with different Scientific and commercial cloud providers like HLT, FutureGrid, FermiCloud and Amazon EC2. In this paper, we describe a solution for cloud bursting with GlideinWMS. The paper describes the approach, architectural changes and lessons learned while enabling support for cloud infrastructures in GlideinWMS.« less

Threshold-based queuing system for performance analysis of cloud computing system with dynamic scaling

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

Shorgin, Sergey Ya.; Pechinkin, Alexander V.; Samouylov, Konstantin E.

Cloud computing is promising technology to manage and improve utilization of computing center resources to deliver various computing and IT services. For the purpose of energy saving there is no need to unnecessarily operate many servers under light loads, and they are switched off. On the other hand, some servers should be switched on in heavy load cases to prevent very long delays. Thus, waiting times and system operating cost can be maintained on acceptable level by dynamically adding or removing servers. One more fact that should be taken into account is significant server setup costs and activation times. Formore » better energy efficiency, cloud computing system should not react on instantaneous increase or instantaneous decrease of load. That is the main motivation for using queuing systems with hysteresis for cloud computing system modelling. In the paper, we provide a model of cloud computing system in terms of multiple server threshold-based infinite capacity queuing system with hysteresis and noninstantanuous server activation. For proposed model, we develop a method for computing steady-state probabilities that allow to estimate a number of performance measures.« less

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.

Progress towards MODIS and VIIRS Cloud Optical Property Data Record Continuity

NASA Astrophysics Data System (ADS)

Meyer, K.; Platnick, S. E.; Wind, G.; Amarasinghe, N.; Holz, R.; Ackerman, S. A.; Heidinger, A. K.

2016-12-01

The launch of Suomi NPP in the fall of 2011 began the next generation of U.S. operational polar orbiting Earth observations, and its VIIRS imager provides an opportunity to extend the 15+ year climate data record of MODIS EOS. Similar to MODIS, VIIRS provides visible through IR observations at moderate spatial resolution with a 1330 LT equatorial crossing consistent with the MODIS on the Aqua platform. However, unlike MODIS, VIIRS lacks key water vapor and CO2 absorbing channels used for high cloud detection and cloud-top property retrievals, and there is a significant change in the spectral location of the 2.1μm shortwave-infrared channel used for cloud optical/microphysical retrievals and cloud thermodynamic phase. Given these instrument differences between MODIS EOS and VIIRS S-NPP/JPSS, we discuss our progress towards merging the MODIS observational record with VIIRS in order to generate cloud optical property climate data record continuity across the observing systems. The MODIS-VIIRS continuity algorithm for cloud optical property retrievals leverages heritage algorithms that produce the existing MODIS cloud optical and microphysical properties product (MOD06); the NOAA AWG/CLAVR-x cloud-top property algorithm and a common MODIS-VIIRS cloud mask feed into the optical property algorithm. To account for the different channel sets of MODIS and VIIRS, each algorithm nominally uses a subset of channels common to both imagers. Data granule and aggregated examples for the current version of the continuity algorithm (MODAWG) will be shown. In addition, efforts to reconcile apparent radiometric biases between analogous channels of the two imagers, a critical consideration for obtaining inter-sensor climate data record continuity, will be discussed.

Reprocessing Multiyear GPS Data from Continuously Operating Reference Stations on Cloud Computing Platform

NASA Astrophysics Data System (ADS)

Yoon, S.

2016-12-01

To define geodetic reference frame using GPS data collected by Continuously Operating Reference Stations (CORS) network, historical GPS data needs to be reprocessed regularly. Reprocessing GPS data collected by upto 2000 CORS sites for the last two decades requires a lot of computational resource. At National Geodetic Survey (NGS), there has been one completed reprocessing in 2011, and currently, the second reprocessing is undergoing. For the first reprocessing effort, in-house computing resource was utilized. In the current second reprocessing effort, outsourced cloud computing platform is being utilized. In this presentation, the outline of data processing strategy at NGS is described as well as the effort to parallelize the data processing procedure in order to maximize the benefit of the cloud computing. The time and cost savings realized by utilizing cloud computing approach will also be discussed.

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

RACORO Extended-Term Aircraft Observations of Boundary-Layer Clouds

NASA Technical Reports Server (NTRS)

Vogelmann, Andrew M.; McFarquhar, Greg M.; Ogren, John A.; Turner, David D.; Comstock, Jennifer M.; Feingold, Graham; Long, Charles N.; Jonsson, Haflidi H.; Bucholtz, Anthony; Collins, Don R.;

Bioinformatics clouds for big data manipulation

PubMed Central

2012-01-01

Abstract As advances in life sciences and information technology bring profound influences on bioinformatics due to its interdisciplinary nature, bioinformatics is experiencing a new leap-forward from in-house computing infrastructure into utility-supplied cloud computing delivered over the Internet, in order to handle the vast quantities of biological data generated by high-throughput experimental technologies. Albeit relatively new, cloud computing promises to address big data storage and analysis issues in the bioinformatics field. Here we review extant cloud-based services in bioinformatics, classify them into Data as a Service (DaaS), Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS), and present our perspectives on the adoption of cloud computing in bioinformatics. Reviewers This article was reviewed by Frank Eisenhaber, Igor Zhulin, and Sandor Pongor. PMID:23190475

A proposed study of multiple scattering through clouds up to 1 THz

NASA Technical Reports Server (NTRS)

Gerace, G. C.; Smith, E. K.

1992-01-01

A rigorous computation of the electromagnetic field scattered from an atmospheric liquid water cloud is proposed. The recent development of a fast recursive algorithm (Chew algorithm) for computing the fields scattered from numerous scatterers now makes a rigorous computation feasible. A method is presented for adapting this algorithm to a general case where there are an extremely large number of scatterers. It is also proposed to extend a new binary PAM channel coding technique (El-Khamy coding) to multiple levels with non-square pulse shapes. The Chew algorithm can be used to compute the transfer function of a cloud channel. Then the transfer function can be used to design an optimum El-Khamy code. In principle, these concepts can be applied directly to the realistic case of a time-varying cloud (adaptive channel coding and adaptive equalization). A brief review is included of some preliminary work on cloud dispersive effects on digital communication signals and on cloud liquid water spectra and correlations.

Prediction based proactive thermal virtual machine scheduling in green clouds.

PubMed

Kinger, Supriya; Kumar, Rajesh; Sharma, Anju

2014-01-01

Cloud computing has rapidly emerged as a widely accepted computing paradigm, but the research on Cloud computing is still at an early stage. Cloud computing provides many advanced features but it still has some shortcomings such as relatively high operating cost and environmental hazards like increasing carbon footprints. These hazards can be reduced up to some extent by efficient scheduling of Cloud resources. Working temperature on which a machine is currently running can be taken as a criterion for Virtual Machine (VM) scheduling. This paper proposes a new proactive technique that considers current and maximum threshold temperature of Server Machines (SMs) before making scheduling decisions with the help of a temperature predictor, so that maximum temperature is never reached. Different workload scenarios have been taken into consideration. The results obtained show that the proposed system is better than existing systems of VM scheduling, which does not consider current temperature of nodes before making scheduling decisions. Thus, a reduction in need of cooling systems for a Cloud environment has been obtained and validated.

Hybrid Cloud Computing Environment for EarthCube and Geoscience Community

NASA Astrophysics Data System (ADS)

Yang, C. P.; Qin, H.

2016-12-01

The NSF EarthCube Integration and Test Environment (ECITE) has built a hybrid cloud computing environment to provides cloud resources from private cloud environments by using cloud system software - OpenStack and Eucalyptus, and also manages public cloud - Amazon Web Service that allow resource synchronizing and bursting between private and public cloud. On ECITE hybrid cloud platform, EarthCube and geoscience community can deploy and manage the applications by using base virtual machine images or customized virtual machines, analyze big datasets by using virtual clusters, and real-time monitor the virtual resource usage on the cloud. Currently, a number of EarthCube projects have deployed or started migrating their projects to this platform, such as CHORDS, BCube, CINERGI, OntoSoft, and some other EarthCube building blocks. To accomplish the deployment or migration, administrator of ECITE hybrid cloud platform prepares the specific needs (e.g. images, port numbers, usable cloud capacity, etc.) of each project in advance base on the communications between ECITE and participant projects, and then the scientists or IT technicians in those projects launch one or multiple virtual machines, access the virtual machine(s) to set up computing environment if need be, and migrate their codes, documents or data without caring about the heterogeneity in structure and operations among different cloud platforms.

Searching for SNPs with cloud computing

PubMed Central

2009-01-01

As DNA sequencing outpaces improvements in computer speed, there is a critical need to accelerate tasks like alignment and SNP calling. Crossbow is a cloud-computing software tool that combines the aligner Bowtie and the SNP caller SOAPsnp. Executing in parallel using Hadoop, Crossbow analyzes data comprising 38-fold coverage of the human genome in three hours using a 320-CPU cluster rented from a cloud computing service for about $85. Crossbow is available from http://bowtie-bio.sourceforge.net/crossbow/. PMID:19930550

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

The Role of Standards in Cloud-Computing Interoperability

DTIC Science & Technology

2012-10-01

services are not shared outside the organization. CloudStack, Eucalyptus, HP, Microsoft, OpenStack , Ubuntu, and VMWare provide tools for building...center requirements • Developing usage models for cloud ven- dors • Independent IT consortium OpenStack http://www.openstack.org • Open-source...software for running private clouds • Currently consists of three core software projects: OpenStack Compute (Nova), OpenStack Object Storage (Swift

Toward real-time Monte Carlo simulation using a commercial cloud computing infrastructure

NASA Astrophysics Data System (ADS)

Wang, Henry; Ma, Yunzhi; Pratx, Guillem; Xing, Lei

2011-09-01

Monte Carlo (MC) methods are the gold standard for modeling photon and electron transport in a heterogeneous medium; however, their computational cost prohibits their routine use in the clinic. Cloud computing, wherein computing resources are allocated on-demand from a third party, is a new approach for high performance computing and is implemented to perform ultra-fast MC calculation in radiation therapy. We deployed the EGS5 MC package in a commercial cloud environment. Launched from a single local computer with Internet access, a Python script allocates a remote virtual cluster. A handshaking protocol designates master and worker nodes. The EGS5 binaries and the simulation data are initially loaded onto the master node. The simulation is then distributed among independent worker nodes via the message passing interface, and the results aggregated on the local computer for display and data analysis. The described approach is evaluated for pencil beams and broad beams of high-energy electrons and photons. The output of cloud-based MC simulation is identical to that produced by single-threaded implementation. For 1 million electrons, a simulation that takes 2.58 h on a local computer can be executed in 3.3 min on the cloud with 100 nodes, a 47× speed-up. Simulation time scales inversely with the number of parallel nodes. The parallelization overhead is also negligible for large simulations. Cloud computing represents one of the most important recent advances in supercomputing technology and provides a promising platform for substantially improved MC simulation. In addition to the significant speed up, cloud computing builds a layer of abstraction for high performance parallel computing, which may change the way dose calculations are performed and radiation treatment plans are completed. This work was presented in part at the 2010 Annual Meeting of the American Association of Physicists in Medicine (AAPM), Philadelphia, PA.

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.

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

PubMed Central

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

2017-01-01

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. PMID:28272305

Construction and application of Red5 cluster based on OpenStack

NASA Astrophysics Data System (ADS)

Wang, Jiaqing; Song, Jianxin

2017-08-01

With the application and development of cloud computing technology in various fields, the resource utilization rate of the data center has been improved obviously, and the system based on cloud computing platform has also improved the expansibility and stability. In the traditional way, Red5 cluster resource utilization is low and the system stability is poor. This paper uses cloud computing to efficiently calculate the resource allocation ability, and builds a Red5 server cluster based on OpenStack. Multimedia applications can be published to the Red5 cloud server cluster. The system achieves the flexible construction of computing resources, but also greatly improves the stability of the cluster and service efficiency.

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.

The Magellan Final Report on Cloud Computing

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

,; Coghlan, Susan; Yelick, Katherine

The goal of Magellan, a project funded through the U.S. Department of Energy (DOE) Office of Advanced Scientific Computing Research (ASCR), was to investigate the potential role of cloud computing in addressing the computing needs for the DOE Office of Science (SC), particularly related to serving the needs of mid- range computing and future data-intensive computing workloads. A set of research questions was formed to probe various aspects of cloud computing from performance, usability, and cost. To address these questions, a distributed testbed infrastructure was deployed at the Argonne Leadership Computing Facility (ALCF) and the National Energy Research Scientific Computingmore » Center (NERSC). The testbed was designed to be flexible and capable enough to explore a variety of computing models and hardware design points in order to understand the impact for various scientific applications. During the project, the testbed also served as a valuable resource to application scientists. Applications from a diverse set of projects such as MG-RAST (a metagenomics analysis server), the Joint Genome Institute, the STAR experiment at the Relativistic Heavy Ion Collider, and the Laser Interferometer Gravitational Wave Observatory (LIGO), were used by the Magellan project for benchmarking within the cloud, but the project teams were also able to accomplish important production science utilizing the Magellan cloud resources.« less

NPOESS Preparatory Project Validation Program for Atmsophere Data Products from VIIRS

NASA Astrophysics Data System (ADS)

Starr, D.; Wong, E.

2009-12-01

The National Polar-orbiting Operational Environmental Satellite Suite (NPOESS) Program, in partnership with National Aeronautical Space Administration (NASA), will launch the NPOESS Preparatory Project (NPP), a risk reduction and data continuity mission, prior to the first operational NPOESS launch. The NPOESS Program, in partnership with Northrop Grumman Aerospace Systems (NGAS), will execute the NPP Validation program to ensure the data products comply with the requirements of the sponsoring agencies. Data from the NPP Visible/Infrared Imager/Radiometer Suite (VIIRS) will be used to produce Environmental Data Records (EDR's) for aerosol and clouds, specifically Aerosol Optical Thickness (AOT), Aerosol Particle Size Parameter (APSP), and Suspended Matter (SM); and Cloud Optical Thickness (COT), Cloud Effective Particle Size (CEPS), Cloud Top Temperature (CTT), Height (CTH) and Pressure (CTP), and Cloud Base Height (CBH). The Aerosol and Cloud EDR Validation Program is a multifaceted effort to characterize and validate these data products. The program involves systematic comparison to heritage data products, e.g., MODIS, and ground-based correlative data, such as AERONET and ARM data products, and potentially airborne field measurements. To the extent possible, the domain is global. The program leverages various investments that have and are continuing to be made by national funding agencies in such resources, as well as the operational user community and the broad Earth science user community. This presentation will provide an overview of the approaches, data and schedule for the validation of the NPP VIIRS Aerosol and Cloud environmental data products.

A Brief Analysis of Development Situations and Trend of Cloud Computing

NASA Astrophysics Data System (ADS)

Yang, Wenyan

2017-12-01

in recent years, the rapid development of Internet technology has radically changed people's work, learning and lifestyles. More and more activities are completed by virtue of computers and networks. The amount of information and data generated is bigger day by day, and people rely more on computer, which makes computing power of computer fail to meet demands of accuracy and rapidity from people. The cloud computing technology has experienced fast development, which is widely applied in the computer industry as a result of advantages of high precision, fast computing and easy usage. Moreover, it has become a focus in information research at present. In this paper, the development situations and trend of cloud computing shall be analyzed and researched.

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.

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.

Research on the digital education resources of sharing pattern in independent colleges based on cloud computing environment

NASA Astrophysics Data System (ADS)

Xiong, Ting; He, Zhiwen

2017-06-01

Cloud computing was first proposed by Google Company in the United States, which was based on the Internet center, providing a standard and open network sharing service approach. With the rapid development of the higher education in China, the educational resources provided by colleges and universities had greatly gap in the actual needs of teaching resources. therefore, Cloud computing of using the Internet technology to provide shared methods liked the timely rain, which had become an important means of the Digital Education on sharing applications in the current higher education. Based on Cloud computing environment, the paper analyzed the existing problems about the sharing of digital educational resources in Jiangxi Province Independent Colleges. According to the sharing characteristics of mass storage, efficient operation and low input about Cloud computing, the author explored and studied the design of the sharing model about the digital educational resources of higher education in Independent College. Finally, the design of the shared model was put into the practical applications.

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.

Adventures in Private Cloud: Balancing Cost and Capability at the CloudSat Data Processing Center

NASA Astrophysics Data System (ADS)

Partain, P.; Finley, S.; Fluke, J.; Haynes, J. M.; Cronk, H. Q.; Miller, S. D.

2016-12-01

Since the beginning of the CloudSat Mission in 2006, The CloudSat Data Processing Center (DPC) at the Cooperative Institute for Research in the Atmosphere (CIRA) has been ingesting data from the satellite and other A-Train sensors, producing data products, and distributing them to researchers around the world. The computing infrastructure was specifically designed to fulfill the requirements as specified at the beginning of what nominally was a two-year mission. The environment consisted of servers dedicated to specific processing tasks in a rigid workflow to generate the required products. To the benefit of science and with credit to the mission engineers, CloudSat has lasted well beyond its planned lifetime and is still collecting data ten years later. Over that period requirements of the data processing system have greatly expanded and opportunities for providing value-added services have presented themselves. But while demands on the system have increased, the initial design allowed for very little expansion in terms of scalability and flexibility. The design did change to include virtual machine processing nodes and distributed workflows but infrastructure management was still a time consuming task when system modification was required to run new tests or implement new processes. To address the scalability, flexibility, and manageability of the system Cloud computing methods and technologies are now being employed. The use of a public cloud like Amazon Elastic Compute Cloud or Google Compute Engine was considered but, among other issues, data transfer and storage cost becomes a problem especially when demand fluctuates as a result of reprocessing and the introduction of new products and services. Instead, the existing system was converted to an on premises private Cloud using the OpenStack computing platform and Ceph software defined storage to reap the benefits of the Cloud computing paradigm. This work details the decisions that were made, the benefits that have been realized, the difficulties that were encountered and issues that still exist.

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.

Position and volume estimation of atmospheric nuclear detonations from video reconstruction

NASA Astrophysics Data System (ADS)

Schmitt, Daniel T.

Recent work in digitizing films of foundational atmospheric nuclear detonations from the 1950s provides an opportunity to perform deeper analysis on these historical tests. This work leverages multi-view geometry and computer vision techniques to provide an automated means to perform three-dimensional analysis of the blasts for several points in time. The accomplishment of this requires careful alignment of the films in time, detection of features in the images, matching of features, and multi-view reconstruction. Sub-explosion features can be detected with a 67% hit rate and 22% false alarm rate. Hotspot features can be detected with a 71.95% hit rate, 86.03% precision and a 0.015% false positive rate. Detected hotspots are matched across 57-109 degree viewpoints with 76.63% average correct matching by defining their location relative to the center of the explosion, rotating them to the alternative viewpoint, and matching them collectively. When 3D reconstruction is applied to the hotspot matching it completes an automated process that has been used to create 168 3D point clouds with 31.6 points per reconstruction with each point having an accuracy of 0.62 meters with 0.35, 0.24, and 0.34 meters of accuracy in the x-, y- and z-direction respectively. As a demonstration of using the point clouds for analysis, volumes are estimated and shown to be consistent with radius-based models and in some cases improve on the level of uncertainty in the yield calculation.

A cloud-compatible bioinformatics pipeline for ultrarapid pathogen identification from next-generation sequencing of clinical samples.

PubMed

Naccache, Samia N; Federman, Scot; Veeraraghavan, Narayanan; Zaharia, Matei; Lee, Deanna; Samayoa, Erik; Bouquet, Jerome; Greninger, Alexander L; Luk, Ka-Cheung; Enge, Barryett; Wadford, Debra A; Messenger, Sharon L; Genrich, Gillian L; Pellegrino, Kristen; Grard, Gilda; Leroy, Eric; Schneider, Bradley S; Fair, Joseph N; Martínez, Miguel A; Isa, Pavel; Crump, John A; DeRisi, Joseph L; Sittler, Taylor; Hackett, John; Miller, Steve; Chiu, Charles Y

2014-07-01

Unbiased next-generation sequencing (NGS) approaches enable comprehensive pathogen detection in the clinical microbiology laboratory and have numerous applications for public health surveillance, outbreak investigation, and the diagnosis of infectious diseases. However, practical deployment of the technology is hindered by the bioinformatics challenge of analyzing results accurately and in a clinically relevant timeframe. Here we describe SURPI ("sequence-based ultrarapid pathogen identification"), a computational pipeline for pathogen identification from complex metagenomic NGS data generated from clinical samples, and demonstrate use of the pipeline in the analysis of 237 clinical samples comprising more than 1.1 billion sequences. Deployable on both cloud-based and standalone servers, SURPI leverages two state-of-the-art aligners for accelerated analyses, SNAP and RAPSearch, which are as accurate as existing bioinformatics tools but orders of magnitude faster in performance. In fast mode, SURPI detects viruses and bacteria by scanning data sets of 7-500 million reads in 11 min to 5 h, while in comprehensive mode, all known microorganisms are identified, followed by de novo assembly and protein homology searches for divergent viruses in 50 min to 16 h. SURPI has also directly contributed to real-time microbial diagnosis in acutely ill patients, underscoring its potential key role in the development of unbiased NGS-based clinical assays in infectious diseases that demand rapid turnaround times. © 2014 Naccache et al.; Published by Cold Spring Harbor Laboratory Press.

Temporal Variations in Jupiter's Atmosphere

NASA Technical Reports Server (NTRS)

Simon-Miller, Amy A.; Chanover, N. J.; Yanamandra-Fisher, P.; Hammel, H. B.; dePater, I.; Noll, K.; Wong, M.; Clarke, J.; Sanchez-Levega, A.; Orton, G. S.;

Citizen Science in Libraries: Results and Insights from a Unique NASA Collaboration

NASA Astrophysics Data System (ADS)

Janney, D. W.; Schwerin, T. G.; Riebeek Kohl, H.; Dusenbery, P.; LaConte, K.; Taylor, J.; Weaver, K. L. K.

2017-12-01

Libraries are local community centers and hubs for learning, with more and more libraries responding to the need to increase science literacy and support 21st century skills by adding STEM programs and resources for patrons of all ages. A collaboration has been developed between two NASA Science Mission Directorate projects - the NASA Earth Science Education Collaborative and NASA@ My Library - each bringing unique STEM assets and networks to support library staff and bring authentic STEM experiences and resources to learners in public library settings. The collaboration used Earth Day 2017 as a high profile event to engage and support 100 libraries across the U.S. (>50% serving rural communities), in developing locally-relevant programs and events that incorporated cloud observing and resources using NASA GLOBE Observer (GO) citizen science program. GO cloud observations are helping NASA scientists understand clouds from below (the ground) and above (from space). Clouds play an important role in transferring energy from the Sun to different parts of the Earth system. Because clouds can change rapidly, scientists need frequent observations from citizen scientists. Insights from the library focus groups and evaluation include promising practices, requested resources, programming ideas and approaches, particularly approaches to leveraging NASA subject matter experts and networks, to support local library programming.

Lidar Measurements of Wind and Cloud Around Venus from an Orbiting or Floating/flying Platform

NASA Technical Reports Server (NTRS)

Singh, Upendra N.; Limaye, Sanjay; Emmitt, George D.; Refaat, Tamer F.; Kavaya, Michael J.; Yu, Jirong; Petros, Mulugeta

2015-01-01

Given the presence of clouds and haze in the upper portion of the Venus atmosphere, it is reasonable to consider a Doppler wind lidar (DWL) for making remote measurements of the 3-dimensional winds within the tops of clouds and the overlying haze layer. Assuming an orbit altitude of 250 kilometers and cloud tops at 60 kilometers (within the upper cloud layer), an initial performance assessment of an orbiting DWL was made using a numerical instrument and atmospheres model developed for both Earth and Mars. It is reasonable to expect vertical profiles of the 3-dimensional wind speed with 1 kilometer vertical resolution and horizontal spacing of 25 kilometers to several 100 kilometers depending upon the desired integration times. These profiles would begin somewhere just below the tops of the highest clouds and extend into the overlying haze layer to some to-be-determined height. Getting multiple layers of cloud returns is also possible with no negative impact on velocity measurement accuracy. The knowledge and expertise for developing coherent Doppler wind lidar technologies and techniques, for Earth related mission at NASA Langley Research Center is being leveraged to develop an appropriate system suitable for wind measurement around Venus. We are considering a fiber-laser-based lidar system of high efficiency and smaller size and advancing the technology level to meet the requirements for DWL system for Venus from an orbiting or floating/flying platform. This presentation will describe the concept, simulation and technology development plan for wind and cloud measurements on Venus.

GATECloud.net: a platform for large-scale, open-source text processing on the cloud.

PubMed

Tablan, Valentin; Roberts, Ian; Cunningham, Hamish; Bontcheva, Kalina

2013-01-28

Cloud computing is increasingly being regarded as a key enabler of the 'democratization of science', because on-demand, highly scalable cloud computing facilities enable researchers anywhere to carry out data-intensive experiments. In the context of natural language processing (NLP), algorithms tend to be complex, which makes their parallelization and deployment on cloud platforms a non-trivial task. This study presents a new, unique, cloud-based platform for large-scale NLP research--GATECloud. net. It enables researchers to carry out data-intensive NLP experiments by harnessing the vast, on-demand compute power of the Amazon cloud. Important infrastructural issues are dealt with by the platform, completely transparently for the researcher: load balancing, efficient data upload and storage, deployment on the virtual machines, security and fault tolerance. We also include a cost-benefit analysis and usage evaluation.

Creating a Rackspace and NASA Nebula compatible cloud using the OpenStack project (Invited)

NASA Astrophysics Data System (ADS)

Clark, R.

2010-12-01

NASA and Rackspace have both provided technology to the OpenStack that allows anyone to create a private Infrastructure as a Service (IaaS) cloud using open source software and commodity hardware. OpenStack is designed and developed completely in the open and with an open governance process. NASA donated Nova, which powers the compute portion of NASA Nebula Cloud Computing Platform, and Rackspace donated Swift, which powers Rackspace Cloud Files. The project is now in continuous development by NASA, Rackspace, and hundreds of other participants. When you create a private cloud using Openstack, you will have the ability to easily interact with your private cloud, a government cloud, and an ecosystem of public cloud providers, using the same API.

Towards a Cloud Computing Environment: Near Real-time Cloud Product Processing and Distribution for Next Generation Satellites

NASA Astrophysics Data System (ADS)

Nguyen, L.; Chee, T.; Minnis, P.; Palikonda, R.; Smith, W. L., Jr.; Spangenberg, D.

2016-12-01

The NASA LaRC Satellite ClOud and Radiative Property retrieval System (SatCORPS) processes and derives near real-time (NRT) global cloud products from operational geostationary satellite imager datasets. These products are being used in NRT to improve forecast model, aircraft icing warnings, and support aircraft field campaigns. Next generation satellites, such as the Japanese Himawari-8 and the upcoming NOAA GOES-R, present challenges for NRT data processing and product dissemination due to the increase in temporal and spatial resolution. The volume of data is expected to increase to approximately 10 folds. This increase in data volume will require additional IT resources to keep up with the processing demands to satisfy NRT requirements. In addition, these resources are not readily available due to cost and other technical limitations. To anticipate and meet these computing resource requirements, we have employed a hybrid cloud computing environment to augment the generation of SatCORPS products. This paper will describe the workflow to ingest, process, and distribute SatCORPS products and the technologies used. Lessons learn from working on both AWS Clouds and GovCloud will be discussed: benefits, similarities, and differences that could impact decision to use cloud computing and storage. A detail cost analysis will be presented. In addition, future cloud utilization, parallelization, and architecture layout will be discussed for GOES-R.

Cloud detection algorithm comparison and validation for operational Landsat data products

USGS Publications Warehouse

Foga, Steven Curtis; Scaramuzza, Pat; Guo, Song; Zhu, Zhe; Dilley, Ronald; Beckmann, Tim; Schmidt, Gail L.; Dwyer, John L.; Hughes, MJ; Laue, Brady

2017-01-01

Clouds are a pervasive and unavoidable issue in satellite-borne optical imagery. Accurate, well-documented, and automated cloud detection algorithms are necessary to effectively leverage large collections of remotely sensed data. The Landsat project is uniquely suited for comparative validation of cloud assessment algorithms because the modular architecture of the Landsat ground system allows for quick evaluation of new code, and because Landsat has the most comprehensive manual truth masks of any current satellite data archive. Currently, the Landsat Level-1 Product Generation System (LPGS) uses separate algorithms for determining clouds, cirrus clouds, and snow and/or ice probability on a per-pixel basis. With more bands onboard the Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) satellite, and a greater number of cloud masking algorithms, the U.S. Geological Survey (USGS) is replacing the current cloud masking workflow with a more robust algorithm that is capable of working across multiple Landsat sensors with minimal modification. Because of the inherent error from stray light and intermittent data availability of TIRS, these algorithms need to operate both with and without thermal data. In this study, we created a workflow to evaluate cloud and cloud shadow masking algorithms using cloud validation masks manually derived from both Landsat 7 Enhanced Thematic Mapper Plus (ETM +) and Landsat 8 OLI/TIRS data. We created a new validation dataset consisting of 96 Landsat 8 scenes, representing different biomes and proportions of cloud cover. We evaluated algorithm performance by overall accuracy, omission error, and commission error for both cloud and cloud shadow. We found that CFMask, C code based on the Function of Mask (Fmask) algorithm, and its confidence bands have the best overall accuracy among the many algorithms tested using our validation data. The Artificial Thermal-Automated Cloud Cover Algorithm (AT-ACCA) is the most accurate nonthermal-based algorithm. We give preference to CFMask for operational cloud and cloud shadow detection, as it is derived from a priori knowledge of physical phenomena and is operable without geographic restriction, making it useful for current and future land imaging missions without having to be retrained in a machine-learning environment.

Mobile healthcare information management utilizing Cloud Computing and Android OS.

PubMed

Doukas, Charalampos; Pliakas, Thomas; Maglogiannis, Ilias

2010-01-01

Cloud Computing provides functionality for managing information data in a distributed, ubiquitous and pervasive manner supporting several platforms, systems and applications. This work presents the implementation of a mobile system that enables electronic healthcare data storage, update and retrieval using Cloud Computing. The mobile application is developed using Google's Android operating system and provides management of patient health records and medical images (supporting DICOM format and JPEG2000 coding). The developed system has been evaluated using the Amazon's S3 cloud service. This article summarizes the implementation details and presents initial results of the system in practice.

Retrieving and Indexing Spatial Data in the Cloud Computing Environment

NASA Astrophysics Data System (ADS)

Wang, Yonggang; Wang, Sheng; Zhou, Daliang

In order to solve the drawbacks of spatial data storage in common Cloud Computing platform, we design and present a framework for retrieving, indexing, accessing and managing spatial data in the Cloud environment. An interoperable spatial data object model is provided based on the Simple Feature Coding Rules from the OGC such as Well Known Binary (WKB) and Well Known Text (WKT). And the classic spatial indexing algorithms like Quad-Tree and R-Tree are re-designed in the Cloud Computing environment. In the last we develop a prototype software based on Google App Engine to implement the proposed model.

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.