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.;

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.

SaaS enabled admission control for MCMC simulation in cloud computing infrastructures

NASA Astrophysics Data System (ADS)

Vázquez-Poletti, J. L.; Moreno-Vozmediano, R.; Han, R.; Wang, W.; Llorente, I. M.

2017-02-01

Markov Chain Monte Carlo (MCMC) methods are widely used in the field of simulation and modelling of materials, producing applications that require a great amount of computational resources. Cloud computing represents a seamless source for these resources in the form of HPC. However, resource over-consumption can be an important drawback, specially if the cloud provision process is not appropriately optimized. In the present contribution we propose a two-level solution that, on one hand, takes advantage of approximate computing for reducing the resource demand and on the other, uses admission control policies for guaranteeing an optimal provision to running applications.

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

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

PubMed

Kaur, Pankaj Deep; Chana, Inderveer

2014-01-01

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

An Application-Based Performance Evaluation of NASAs Nebula Cloud Computing Platform

NASA Technical Reports Server (NTRS)

Saini, Subhash; Heistand, Steve; Jin, Haoqiang; Chang, Johnny; Hood, Robert T.; Mehrotra, Piyush; Biswas, Rupak

2012-01-01

The high performance computing (HPC) community has shown tremendous interest in exploring cloud computing as it promises high potential. In this paper, we examine the feasibility, performance, and scalability of production quality scientific and engineering applications of interest to NASA on NASA's cloud computing platform, called Nebula, hosted at Ames Research Center. This work represents the comprehensive evaluation of Nebula using NUTTCP, HPCC, NPB, I/O, and MPI function benchmarks as well as four applications representative of the NASA HPC workload. Specifically, we compare Nebula performance on some of these benchmarks and applications to that of NASA s Pleiades supercomputer, a traditional HPC system. We also investigate the impact of virtIO and jumbo frames on interconnect performance. Overall results indicate that on Nebula (i) virtIO and jumbo frames improve network bandwidth by a factor of 5x, (ii) there is a significant virtualization layer overhead of about 10% to 25%, (iii) write performance is lower by a factor of 25x, (iv) latency for short MPI messages is very high, and (v) overall performance is 15% to 48% lower than that on Pleiades for NASA HPC applications. We also comment on the usability of the cloud platform.

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.

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.

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

Bioinformatics and Microarray Data Analysis on the Cloud.

PubMed

Calabrese, Barbara; Cannataro, Mario

2016-01-01

High-throughput platforms such as microarray, mass spectrometry, and next-generation sequencing are producing an increasing volume of omics data that needs large data storage and computing power. Cloud computing offers massive scalable computing and storage, data sharing, on-demand anytime and anywhere access to resources and applications, and thus, it may represent the key technology for facing those issues. In fact, in the recent years it has been adopted for the deployment of different bioinformatics solutions and services both in academia and in the industry. Although this, cloud computing presents several issues regarding the security and privacy of data, that are particularly important when analyzing patients data, such as in personalized medicine. This chapter reviews main academic and industrial cloud-based bioinformatics solutions; with a special focus on microarray data analysis solutions and underlines main issues and problems related to the use of such platforms for the storage and analysis of patients data.

Research on cloud-based remote measurement and analysis system

NASA Astrophysics Data System (ADS)

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

2015-02-01

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

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.

Prototyping manufacturing in the cloud

NASA Astrophysics Data System (ADS)

Ciortea, E. M.

2017-08-01

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

Extraction and representation of common feature from uncertain facial expressions with cloud model.

PubMed

Wang, Shuliang; Chi, Hehua; Yuan, Hanning; Geng, Jing

2017-12-01

Human facial expressions are key ingredient to convert an individual's innate emotion in communication. However, the variation of facial expressions affects the reliable identification of human emotions. In this paper, we present a cloud model to extract facial features for representing human emotion. First, the uncertainties in facial expression are analyzed in the context of cloud model. The feature extraction and representation algorithm is established under cloud generators. With forward cloud generator, facial expression images can be re-generated as many as we like for visually representing the extracted three features, and each feature shows different roles. The effectiveness of the computing model is tested on Japanese Female Facial Expression database. Three common features are extracted from seven facial expression images. Finally, the paper is concluded and remarked.

Further developments in cloud statistics for computer simulations

NASA Technical Reports Server (NTRS)

Chang, D. T.; Willand, J. H.

1972-01-01

This study is a part of NASA's continued program to provide global statistics of cloud parameters for computer simulation. The primary emphasis was on the development of the data bank of the global statistical distributions of cloud types and cloud layers and their applications in the simulation of the vertical distributions of in-cloud parameters such as liquid water content. These statistics were compiled from actual surface observations as recorded in Standard WBAN forms. Data for a total of 19 stations were obtained and reduced. These stations were selected to be representative of the 19 primary cloud climatological regions defined in previous studies of cloud statistics. Using the data compiled in this study, a limited study was conducted of the hemogeneity of cloud regions, the latitudinal dependence of cloud-type distributions, the dependence of these statistics on sample size, and other factors in the statistics which are of significance to the problem of simulation. The application of the statistics in cloud simulation was investigated. In particular, the inclusion of the new statistics in an expanded multi-step Monte Carlo simulation scheme is suggested and briefly outlined.

A microphysical parameterization of aqSOA and sulfate formation in clouds

NASA Astrophysics Data System (ADS)

McVay, Renee; Ervens, Barbara

2017-07-01

Sulfate and secondary organic aerosol (cloud aqSOA) can be chemically formed in cloud water. Model implementation of these processes represents a computational burden due to the large number of microphysical and chemical parameters. Chemical mechanisms have been condensed by reducing the number of chemical parameters. Here an alternative is presented to reduce the number of microphysical parameters (number of cloud droplet size classes). In-cloud mass formation is surface and volume dependent due to surface-limited oxidant uptake and/or size-dependent pH. Box and parcel model simulations show that using the effective cloud droplet diameter (proportional to total volume-to-surface ratio) reproduces sulfate and aqSOA formation rates within ≤30% as compared to full droplet distributions; other single diameters lead to much greater deviations. This single-class approach reduces computing time significantly and can be included in models when total liquid water content and effective diameter are available.

NASA Enterprise Managed Cloud Computing (EMCC): Delivering an Initial Operating Capability (IOC) for NASA use of Commercial Infrastructure-as-a-Service (IaaS)

NASA Technical Reports Server (NTRS)

O'Brien, Raymond

2017-01-01

In 2016, Ames supported the NASA CIO in delivering an initial operating capability for Agency use of commercial cloud computing. This presentation provides an overview of the project, the services approach followed, and the major components of the capability that was delivered. The presentation is being given at the request of Amazon Web Services to a contingent representing the Brazilian Federal Government and Defense Organization that is interested in the use of Amazon Web Services (AWS). NASA is currently a customer of AWS and delivered the Initial Operating Capability using AWS as its first commercial cloud provider. The IOC, however, designed to also support other cloud providers in the future.

Lagrangian condensation microphysics with Twomey CCN activation

NASA Astrophysics Data System (ADS)

Grabowski, Wojciech W.; Dziekan, Piotr; Pawlowska, Hanna

2018-01-01

We report the development of a novel Lagrangian microphysics methodology for simulations of warm ice-free clouds. The approach applies the traditional Eulerian method for the momentum and continuous thermodynamic fields such as the temperature and water vapor mixing ratio, and uses Lagrangian super-droplets to represent condensed phase such as cloud droplets and drizzle or rain drops. In other applications of the Lagrangian warm-rain microphysics, the super-droplets outside clouds represent unactivated cloud condensation nuclei (CCN) that become activated upon entering a cloud and can further grow through diffusional and collisional processes. The original methodology allows for the detailed study of not only effects of CCN on cloud microphysics and dynamics, but also CCN processing by a cloud. However, when cloud processing is not of interest, a simpler and computationally more efficient approach can be used with super-droplets forming only when CCN is activated and no super-droplet existing outside a cloud. This is possible by applying the Twomey activation scheme where the local supersaturation dictates the concentration of cloud droplets that need to be present inside a cloudy volume, as typically used in Eulerian bin microphysics schemes. Since a cloud volume is a small fraction of the computational domain volume, the Twomey super-droplets provide significant computational advantage when compared to the original super-droplet methodology. Additional advantage comes from significantly longer time steps that can be used when modeling of CCN deliquescence is avoided. Moreover, other formulation of the droplet activation can be applied in case of low vertical resolution of the host model, for instance, linking the concentration of activated cloud droplets to the local updraft speed. This paper discusses the development and testing of the Twomey super-droplet methodology, focusing on the activation and diffusional growth. Details of the activation implementation, transport of super-droplets in the physical space, and the coupling between super-droplets and the Eulerian temperature and water vapor field are discussed in detail. Some of these are relevant to the original super-droplet methodology as well and to the ice phase modeling using the Lagrangian approach. As a computational example, the scheme is applied to an idealized moist thermal rising in a stratified environment, with the original super-droplet methodology providing a benchmark to which the new scheme is compared.

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.

Superposition and alignment of labeled point clouds.

PubMed

Fober, Thomas; Glinca, Serghei; Klebe, Gerhard; Hüllermeier, Eyke

2011-01-01

Geometric objects are often represented approximately in terms of a finite set of points in three-dimensional euclidean space. In this paper, we extend this representation to what we call labeled point clouds. A labeled point cloud is a finite set of points, where each point is not only associated with a position in three-dimensional space, but also with a discrete class label that represents a specific property. This type of model is especially suitable for modeling biomolecules such as proteins and protein binding sites, where a label may represent an atom type or a physico-chemical property. Proceeding from this representation, we address the question of how to compare two labeled points clouds in terms of their similarity. Using fuzzy modeling techniques, we develop a suitable similarity measure as well as an efficient evolutionary algorithm to compute it. Moreover, we consider the problem of establishing an alignment of the structures in the sense of a one-to-one correspondence between their basic constituents. From a biological point of view, alignments of this kind are of great interest, since mutually corresponding molecular constituents offer important information about evolution and heredity, and can also serve as a means to explain a degree of similarity. In this paper, we therefore develop a method for computing pairwise or multiple alignments of labeled point clouds. To this end, we proceed from an optimal superposition of the corresponding point clouds and construct an alignment which is as much as possible in agreement with the neighborhood structure established by this superposition. We apply our methods to the structural analysis of protein binding sites.

Phenomenology tools on cloud infrastructures using OpenStack

NASA Astrophysics Data System (ADS)

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

2013-04-01

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

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

NASA Astrophysics Data System (ADS)

Basu, Srijita; Sengupta, Anirban; Mazumdar, Chandan

2016-11-01

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

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.

Linearized radiative transfer models for retrieval of cloud parameters from EPIC/DSCOVR measurements

NASA Astrophysics Data System (ADS)

Molina García, Víctor; Sasi, Sruthy; Efremenko, Dmitry S.; Doicu, Adrian; Loyola, Diego

2018-07-01

In this paper, we describe several linearized radiative transfer models which can be used for the retrieval of cloud parameters from EPIC (Earth Polychromatic Imaging Camera) measurements. The approaches under examination are (1) the linearized forward approach, represented in this paper by the linearized discrete ordinate and matrix operator methods with matrix exponential, and (2) the forward-adjoint approach based on the discrete ordinate method with matrix exponential. To enhance the performance of the radiative transfer computations, the correlated k-distribution method and the Principal Component Analysis (PCA) technique are used. We provide a compact description of the proposed methods, as well as a numerical analysis of their accuracy and efficiency when simulating EPIC measurements in the oxygen A-band channel at 764 nm. We found that the computation time of the forward-adjoint approach using the correlated k-distribution method in conjunction with PCA is approximately 13 s for simultaneously computing the derivatives with respect to cloud optical thickness and cloud top height.

Clouds on Hot Jupiters Illustration

NASA Image and Video Library

2016-10-18

Hot Jupiters are exoplanets that orbit their stars so tightly that their temperatures are extremely high, reaching over 2,400 degrees Fahrenheit (1600 Kelvin). They are also tidally locked, so one side of the planet always faces the sun and the other is in permanent darkness. Research suggests that the "dayside" is largely free of clouds, while the "nightside" is heavily clouded. This illustration represents how hot Jupiters of different temperatures and different cloud compositions might appear to a person flying over the dayside of these planets on a spaceship, based on computer modeling. Cooler planets are entirely cloudy, whereas hotter planets have morning clouds only. Clouds of different composition have different colors, whereas the clear sky is bluer than on Earth. For the hottest planets, the atmosphere is hot enough on the evening side to glow like a charcoal. Figure 1 shows an approximation of what various hot Jupiters might look like based on a combination of computer modeling and data from NASA's Kepler Space Telescope. From left to right it shows: sodium sulfide clouds (1000 to 1200 Kelvin), manganese sulfide clouds (1200 to 1600 Kelvin), magnesium silicate clouds (1600 to 1800 Kelvin), magnesium silicate and aluminum oxide clouds (1800 Kelvin) and clouds composed of magnesium silicate, aluminum oxide, iron and calcium titanate (1900 to 2200 Kelvin). http://photojournal.jpl.nasa.gov/catalog/PIA21074

An adaptive process-based cloud infrastructure for space situational awareness applications

NASA Astrophysics Data System (ADS)

Liu, Bingwei; Chen, Yu; Shen, Dan; Chen, Genshe; Pham, Khanh; Blasch, Erik; Rubin, Bruce

2014-06-01

Space situational awareness (SSA) and defense space control capabilities are top priorities for groups that own or operate man-made spacecraft. Also, with the growing amount of space debris, there is an increase in demand for contextual understanding that necessitates the capability of collecting and processing a vast amount sensor data. Cloud computing, which features scalable and flexible storage and computing services, has been recognized as an ideal candidate that can meet the large data contextual challenges as needed by SSA. Cloud computing consists of physical service providers and middleware virtual machines together with infrastructure, platform, and software as service (IaaS, PaaS, SaaS) models. However, the typical Virtual Machine (VM) abstraction is on a per operating systems basis, which is at too low-level and limits the flexibility of a mission application architecture. In responding to this technical challenge, a novel adaptive process based cloud infrastructure for SSA applications is proposed in this paper. In addition, the details for the design rationale and a prototype is further examined. The SSA Cloud (SSAC) conceptual capability will potentially support space situation monitoring and tracking, object identification, and threat assessment. Lastly, the benefits of a more granular and flexible cloud computing resources allocation are illustrated for data processing and implementation considerations within a representative SSA system environment. We show that the container-based virtualization performs better than hypervisor-based virtualization technology in an SSA scenario.

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 case study of tuning MapReduce for efficient Bioinformatics in the cloud

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

Shi, Lizhen; Wang, Zhong; Yu, Weikuan

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

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

An efficient framework for modeling clouds from Landsat8 images

NASA Astrophysics Data System (ADS)

Yuan, Chunqiang; Guo, Jing

2015-03-01

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

NASA Update for Unidata Stratcomm

NASA Technical Reports Server (NTRS)

Lynnes, Chris

2017-01-01

The NASA representative to the Unidata Strategic Committee presented a semiannual update on NASAs work with and use of Unidata technologies. The talk updated Unidata on the program of cloud computing prototypes underway for the Earth Observing System Data and Information System (EOSDIS). Also discussed was a trade study on the use of the Open source Project for a Network Data Access Protocol (OPeNDAP) with Web Object Storage in the cloud.

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

Improving volcanic sulfur dioxide cloud dispersal forecasts by progressive assimilation of satellite observations

NASA Astrophysics Data System (ADS)

Boichu, Marie; Clarisse, Lieven; Khvorostyanov, Dmitry; Clerbaux, Cathy

2014-04-01

Forecasting the dispersal of volcanic clouds during an eruption is of primary importance, especially for ensuring aviation safety. As volcanic emissions are characterized by rapid variations of emission rate and height, the (generally) high level of uncertainty in the emission parameters represents a critical issue that limits the robustness of volcanic cloud dispersal forecasts. An inverse modeling scheme, combining satellite observations of the volcanic cloud with a regional chemistry-transport model, allows reconstructing this source term at high temporal resolution. We demonstrate here how a progressive assimilation of freshly acquired satellite observations, via such an inverse modeling procedure, allows for delivering robust sulfur dioxide (SO2) cloud dispersal forecasts during the eruption. This approach provides a computationally cheap estimate of the expected location and mass loading of volcanic clouds, including the identification of SO2-rich parts.

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

NASA Astrophysics Data System (ADS)

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

2017-10-01

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

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.

Comparison of the different approaches to generate holograms from data acquired with a Kinect sensor

NASA Astrophysics Data System (ADS)

Kang, Ji-Hoon; Leportier, Thibault; Ju, Byeong-Kwon; Song, Jin Dong; Lee, Kwang-Hoon; Park, Min-Chul

2017-05-01

Data of real scenes acquired in real-time with a Kinect sensor can be processed with different approaches to generate a hologram. 3D models can be generated from a point cloud or a mesh representation. The advantage of the point cloud approach is that computation process is well established since it involves only diffraction and propagation of point sources between parallel planes. On the other hand, the mesh representation enables to reduce the number of elements necessary to represent the object. Then, even though the computation time for the contribution of a single element increases compared to a simple point, the total computation time can be reduced significantly. However, the algorithm is more complex since propagation of elemental polygons between non-parallel planes should be implemented. Finally, since a depth map of the scene is acquired at the same time than the intensity image, a depth layer approach can also be adopted. This technique is appropriate for a fast computation since propagation of an optical wavefront from one plane to another can be handled efficiently with the fast Fourier transform. Fast computation with depth layer approach is convenient for real time applications, but point cloud method is more appropriate when high resolution is needed. In this study, since Kinect can be used to obtain both point cloud and depth map, we examine the different approaches that can be adopted for hologram computation and compare their performance.

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 .

Coupling Spectral-bin Cloud Microphysics with the MOSAIC Aerosol Model in WRF-Chem: Methodology and Results for Marine Stratocumulus Clouds

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

Gao, Wenhua; Fan, Jiwen; Easter, Richard C.

Aerosol-cloud interaction processes can be represented more physically with bin cloud microphysics relative to bulk microphysical parameterizations. However, due to computational power limitations in the past, bin cloud microphysics was often run with very simple aerosol treatments. The purpose of this study is to represent better aerosol-cloud interaction processes in the Chemistry version of Weather Research and Forecast model (WRF-Chem) at convection-permitting scales by coupling spectral-bin cloud microphysics (SBM) with the MOSAIC sectional aerosol model. A flexible interface is built that exchanges cloud and aerosol information between them. The interface contains a new bin aerosol activation approach, which replaces themore » treatments in the original SBM. It also includes the modified aerosol resuspension and in-cloud wet removal processes with the droplet loss tendencies and precipitation fluxes from SBM. The newly coupled system is evaluated for two marine stratocumulus cases over the Southeast Pacific Ocean with either a simplified aerosol setup or full-chemistry. We compare the aerosol activation process in the newly-coupled SBM-MOSAIC against the SBM simulation without chemistry using a simplified aerosol setup, and the results show consistent activation rates. A longer time simulation reinforces that aerosol resuspension through cloud drop evaporation plays an important role in replenishing aerosols and impacts cloud and precipitation in marine stratocumulus clouds. Evaluation of the coupled SBM-MOSAIC with full-chemistry using aircraft measurements suggests that the new model works realistically for the marine stratocumulus clouds, and improves the simulation of cloud microphysical properties compared to a simulation using MOSAIC coupled with the Morrison two-moment microphysics.« less

Coupling spectral-bin cloud microphysics with the MOSAIC aerosol model in WRF-Chem: Methodology and results for marine stratocumulus clouds

NASA Astrophysics Data System (ADS)

Gao, Wenhua; Fan, Jiwen; Easter, R. C.; Yang, Qing; Zhao, Chun; Ghan, Steven J.

2016-09-01

Aerosol-cloud interaction processes can be represented more physically with bin cloud microphysics relative to bulk microphysical parameterizations. However, due to computational power limitations in the past, bin cloud microphysics was often run with very simple aerosol treatments. The purpose of this study is to represent better aerosol-cloud interaction processes in the Chemistry version of Weather Research and Forecast model (WRF-Chem) at convection-permitting scales by coupling spectral-bin cloud microphysics (SBM) with the MOSAIC sectional aerosol model. A flexible interface is built that exchanges cloud and aerosol information between them. The interface contains a new bin aerosol activation approach, which replaces the treatments in the original SBM. It also includes the modified aerosol resuspension and in-cloud wet removal processes with the droplet loss tendencies and precipitation fluxes from SBM. The newly coupled system is evaluated for two marine stratocumulus cases over the Southeast Pacific Ocean with either a simplified aerosol setup or full-chemistry. We compare the aerosol activation process in the newly coupled SBM-MOSAIC against the SBM simulation without chemistry using a simplified aerosol setup, and the results show consistent activation rates. A longer time simulation reinforces that aerosol resuspension through cloud drop evaporation plays an important role in replenishing aerosols and impacts cloud and precipitation in marine stratocumulus clouds. Evaluation of the coupled SBM-MOSAIC with full-chemistry using aircraft measurements suggests that the new model works realistically for the marine stratocumulus clouds, and improves the simulation of cloud microphysical properties compared to a simulation using MOSAIC coupled with the Morrison two-moment microphysics.

A unified parameterization of clouds and turbulence using CLUBB and subcolumns in the Community Atmosphere Model

DOE PAGES

Thayer-Calder, K.; Gettelman, A.; Craig, C.; ...

2015-06-30

Most global climate models parameterize separate cloud types using separate parameterizations. This approach has several disadvantages, including obscure interactions between parameterizations and inaccurate triggering of cumulus parameterizations. Alternatively, a unified cloud parameterization uses one equation set to represent all cloud types. Such cloud types include stratiform liquid and ice cloud, shallow convective cloud, and deep convective cloud. Vital to the success of a unified parameterization is a general interface between clouds and microphysics. One such interface involves drawing Monte Carlo samples of subgrid variability of temperature, water vapor, cloud liquid, and cloud ice, and feeding the sample points into amore » microphysics scheme.This study evaluates a unified cloud parameterization and a Monte Carlo microphysics interface that has been implemented in the Community Atmosphere Model (CAM) version 5.3. Results describing the mean climate and tropical variability from global simulations are presented. The new model shows a degradation in precipitation skill but improvements in short-wave cloud forcing, liquid water path, long-wave cloud forcing, precipitable water, and tropical wave simulation. Also presented are estimations of computational expense and investigation of sensitivity to number of subcolumns.« less

A unified parameterization of clouds and turbulence using CLUBB and subcolumns in the Community Atmosphere Model

DOE PAGES

Thayer-Calder, Katherine; Gettelman, A.; Craig, Cheryl; ...

2015-12-01

Most global climate models parameterize separate cloud types using separate parameterizations.This approach has several disadvantages, including obscure interactions between parameterizations and inaccurate triggering of cumulus parameterizations. Alternatively, a unified cloud parameterization uses one equation set to represent all cloud types. Such cloud types include stratiform liquid and ice cloud, shallow convective cloud, and deep convective cloud. Vital to the success of a unified parameterization is a general interface between clouds and microphysics. One such interface involves drawing Monte Carlo samples of subgrid variability of temperature, water vapor, cloud liquid, and cloud ice, and feeding the sample points into a microphysicsmore » scheme. This study evaluates a unified cloud parameterization and a Monte Carlo microphysics interface that has been implemented in the Community Atmosphere Model (CAM) version 5.3. Results describing the mean climate and tropical variability from global simulations are presented. In conclusion, the new model shows a degradation in precipitation skill but improvements in short-wave cloud forcing, liquid water path, long-wave cloud forcing, perceptible water, and tropical wave simulation. Also presented are estimations of computational expense and investigation of sensitivity to number of subcolumns.« less

Computing patient data in the cloud: practical and legal considerations for genetics and genomics research in Europe and internationally.

PubMed

Molnár-Gábor, Fruzsina; Lueck, Rupert; Yakneen, Sergei; Korbel, Jan O

2017-06-20

Biomedical research is becoming increasingly large-scale and international. Cloud computing enables the comprehensive integration of genomic and clinical data, and the global sharing and collaborative processing of these data within a flexibly scalable infrastructure. Clouds offer novel research opportunities in genomics, as they facilitate cohort studies to be carried out at unprecedented scale, and they enable computer processing with superior pace and throughput, allowing researchers to address questions that could not be addressed by studies using limited cohorts. A well-developed example of such research is the Pan-Cancer Analysis of Whole Genomes project, which involves the analysis of petabyte-scale genomic datasets from research centers in different locations or countries and different jurisdictions. Aside from the tremendous opportunities, there are also concerns regarding the utilization of clouds; these concerns pertain to perceived limitations in data security and protection, and the need for due consideration of the rights of patient donors and research participants. Furthermore, the increased outsourcing of information technology impedes the ability of researchers to act within the realm of existing local regulations owing to fundamental differences in the understanding of the right to data protection in various legal systems. In this Opinion article, we address the current opportunities and limitations of cloud computing and highlight the responsible use of federated and hybrid clouds that are set up between public and private partners as an adequate solution for genetics and genomics research in Europe, and under certain conditions between Europe and international partners. This approach could represent a sensible middle ground between fragmented individual solutions and a "one-size-fits-all" approach.

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

Modeling of Electromagnetic Scattering by Discrete and Discretely Heterogeneous Random Media by Using Numerically Exact Solutions of the Maxwell Equations

NASA Technical Reports Server (NTRS)

Dlugach, Janna M.; Mishchenko, Michael I.

2017-01-01

In this paper, we discuss some aspects of numerical modeling of electromagnetic scattering by discrete random medium by using numerically exact solutions of the macroscopic Maxwell equations. Typical examples of such media are clouds of interstellar dust, clouds of interplanetary dust in the Solar system, dusty atmospheres of comets, particulate planetary rings, clouds in planetary atmospheres, aerosol particles with numerous inclusions and so on. Our study is based on the results of extensive computations of different characteristics of electromagnetic scattering obtained by using the superposition T-matrix method which represents a direct computer solver of the macroscopic Maxwell equations for an arbitrary multisphere configuration. As a result, in particular, we clarify the range of applicability of the low-density theories of radiative transfer and coherent backscattering as well as of widely used effective-medium approximations.

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.

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.

Lagrangian Particle Tracking Simulation for Warm-Rain Processes in Quasi-One-Dimensional Domain

NASA Astrophysics Data System (ADS)

Kunishima, Y.; Onishi, R.

2017-12-01

Conventional cloud simulations are based on the Euler method and compute each microphysics process in a stochastic way assuming infinite numbers of particles within each numerical grid. They therefore cannot provide the Lagrangian statistics of individual particles in cloud microphysics (i.e., aerosol particles, cloud particles, and rain drops) nor discuss the statistical fluctuations due to finite number of particles. We here simulate the entire precipitation process of warm-rain, with tracking individual particles. We use the Lagrangian Cloud Simulator (LCS), which is based on the Euler-Lagrangian framework. In that framework, flow motion and scalar transportation are computed with the Euler method, and particle motion with the Lagrangian one. The LCS tracks particle motions and collision events individually with considering the hydrodynamic interaction between approaching particles with a superposition method, that is, it can directly represent the collisional growth of cloud particles. It is essential for trustworthy collision detection to take account of the hydrodynamic interaction. In this study, we newly developed a stochastic model based on the Twomey cloud condensation nuclei (CCN) activation for the Lagrangian tracking simulation and integrated it into the LCS. Coupling with the Euler computation for water vapour and temperature fields, the initiation and condensational growth of water droplets were computed in the Lagrangian way. We applied the integrated LCS for a kinematic simulation of warm-rain processes in a vertically-elongated domain of, at largest, 0.03×0.03×3000 (m3) with horizontal periodicity. Aerosol particles with a realistic number density, 5×107 (m3), were evenly distributed over the domain at the initial state. Prescribed updraft at the early stage initiated development of a precipitating cloud. We have confirmed that the obtained bulk statistics fairly agree with those from a conventional spectral-bin scheme for a vertical column domain. The centre of the discussion will be the Lagrangian statistics which is collected from the individual behaviour of the tracked particles.

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

Aerosol and cloud microphysics covariability in the northeast Pacific boundary layer estimated with ship-based and satellite remote sensing observations: NE Pacific Aerosol-Cloud Interactions

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

Painemal, David; Chiu, J. -Y. Christine; Minnis, Patrick

Ship measurements collected over the northeast Pacific along transects between the port of Los Angeles (33.7°N, 118.2°W) and Honolulu (21.3°N, 157.8°W) during May to August 2013 were utilized to investigate the covariability between marine low cloud microphysical and aerosol properties. Ship-based retrievals of cloud optical depth (τ) from a Sun photometer and liquid water path (LWP) from a microwave radiometer were combined to derive cloud droplet number concentration Nd and compute a cloud-aerosol interaction (ACI) metric defined as ACICCN = ∂ ln(Nd)/∂ ln(CCN), with CCN denoting the cloud condensation nuclei concentration measured at 0.4% (CCN0.4) and 0.3% (CCN0.3) supersaturation. Analysismore » of CCN0.4, accumulation mode aerosol concentration (Na), and extinction coefficient (σext) indicates that Na and σext can be used as CCN0.4 proxies for estimating ACI. ACICCN derived from 10 min averaged Nd and CCN0.4 and CCN0.3, and CCN0.4 regressions using Na and σext, produce high ACICCN: near 1.0, that is, a fractional change in aerosols is associated with an equivalent fractional change in Nd. ACICCN computed in deep boundary layers was small (ACICCN = 0.60), indicating that surface aerosol measurements inadequately represent the aerosol variability below clouds. Satellite cloud retrievals from MODerate-resolution Imaging Spectroradiometer and GOES-15 data were compared against ship-based retrievals and further analyzed to compute a satellite-based ACICCN. Satellite data correlated well with their ship-based counterparts with linear correlation coefficients equal to or greater than 0.78. Combined satellite Nd and ship-based CCN0.4 and Na yielded a maximum ACICCN = 0.88–0.92, a value slightly less than the ship-based ACICCN, but still consistent with aircraft-based studies in the eastern Pacific.« less

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.

Methods of editing cloud and atmospheric layer affected pixels from satellite data

NASA Technical Reports Server (NTRS)

Nixon, P. R. (Principal Investigator); Wiegand, C. L.; Richardson, A. J.; Johnson, M. P.

1982-01-01

Practical methods of computer screening cloud-contaminated pixels from data of various satellite systems are proposed. Examples are given of the location of clouds and representative landscape features in HCMM spectral space of reflectance (VIS) vs emission (IR). Methods of screening out cloud affected HCMM are discussed. The character of subvisible absorbing-emitting atmospheric layers (subvisible cirrus or SCi) in HCMM data is considered and radiosonde soundings are examined in relation to the presence of SCi. The statistical characteristics of multispectral meteorological satellite data in clear and SCi affected areas are discussed. Examples in TIROS-N and NOAA-7 data from several states and Mexico are presented. The VIS-IR cluster screening method for removing clouds is applied to a 262, 144 pixel HCMM scene from south Texas and northeast Mexico. The SCi that remain after cluster screening are sited out by applying a statistically determined IR limit.

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.

MERRA Analytic Services: Meeting the Big Data Challenges of Climate Science through Cloud-Enabled Climate Analytics-as-a-Service

NASA Astrophysics Data System (ADS)

Schnase, J. L.; Duffy, D.; Tamkin, G. S.; Nadeau, D.; Thompson, J. H.; Grieg, C. M.; McInerney, M.; Webster, W. P.

2013-12-01

Climate science is a Big Data domain that is experiencing unprecedented growth. In our efforts to address the Big Data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We focus on analytics, because it is the knowledge gained from our interactions with Big Data that ultimately produce societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and SaaS enabled by Cloud Computing. Within this framework, Cloud Computing plays an important role; however, we see it as only one element in a constellation of capabilities that are essential to delivering climate analytics as a service. These elements are essential because in the aggregate they lead to generativity, a capacity for self-assembly that we feel is the key to solving many of the Big Data challenges in this domain. MERRA Analytic Services (MERRA/AS) is an example of cloud-enabled CAaaS built on this principle. MERRA/AS enables MapReduce analytics over NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of 26 key climate variables. It represents a type of data product that is of growing importance to scientists doing climate change research and a wide range of decision support applications. MERRA/AS brings together the following generative elements in a full, end-to-end demonstration of CAaaS capabilities: (1) high-performance, data proximal analytics, (2) scalable data management, (3) software appliance virtualization, (4) adaptive analytics, and (5) a domain-harmonized API. The effectiveness of MERRA/AS has been demonstrated in several applications. In our experience, Cloud Computing lowers the barriers and risk to organizational change, fosters innovation and experimentation, facilitates technology transfer, and provides the agility required to meet our customers' increasing and changing needs. Cloud Computing is providing a new tier in the data services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. For climate science, Cloud Computing's capacity to engage communities in the construction of new capabilies is perhaps the most important link between Cloud Computing and Big Data.

MERRA Analytic Services: Meeting the Big Data Challenges of Climate Science Through Cloud-enabled Climate Analytics-as-a-service

NASA Technical Reports Server (NTRS)

Schnase, John L.; Duffy, Daniel Quinn; Tamkin, Glenn S.; Nadeau, Denis; Thompson, John H.; Grieg, Christina M.; McInerney, Mark A.; Webster, William P.

2014-01-01

Climate science is a Big Data domain that is experiencing unprecedented growth. In our efforts to address the Big Data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We focus on analytics, because it is the knowledge gained from our interactions with Big Data that ultimately produce societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and SaaS enabled by Cloud Computing. Within this framework, Cloud Computing plays an important role; however, we it see it as only one element in a constellation of capabilities that are essential to delivering climate analytics as a service. These elements are essential because in the aggregate they lead to generativity, a capacity for self-assembly that we feel is the key to solving many of the Big Data challenges in this domain. MERRA Analytic Services (MERRAAS) is an example of cloud-enabled CAaaS built on this principle. MERRAAS enables MapReduce analytics over NASAs Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of 26 key climate variables. It represents a type of data product that is of growing importance to scientists doing climate change research and a wide range of decision support applications. MERRAAS brings together the following generative elements in a full, end-to-end demonstration of CAaaS capabilities: (1) high-performance, data proximal analytics, (2) scalable data management, (3) software appliance virtualization, (4) adaptive analytics, and (5) a domain-harmonized API. The effectiveness of MERRAAS has been demonstrated in several applications. In our experience, Cloud Computing lowers the barriers and risk to organizational change, fosters innovation and experimentation, facilitates technology transfer, and provides the agility required to meet our customers' increasing and changing needs. Cloud Computing is providing a new tier in the data services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. For climate science, Cloud Computing's capacity to engage communities in the construction of new capabilies is perhaps the most important link between Cloud Computing and Big Data.

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.

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.

Gaussian Radial Basis Function for Efficient Computation of Forest Indirect Illumination

NASA Astrophysics Data System (ADS)

Abbas, Fayçal; Babahenini, Mohamed Chaouki

2018-06-01

Global illumination of natural scenes in real time like forests is one of the most complex problems to solve, because the multiple inter-reflections between the light and material of the objects composing the scene. The major problem that arises is the problem of visibility computation. In fact, the computing of visibility is carried out for all the set of leaves visible from the center of a given leaf, given the enormous number of leaves present in a tree, this computation performed for each leaf of the tree which also reduces performance. We describe a new approach that approximates visibility queries, which precede in two steps. The first step is to generate point cloud representing the foliage. We assume that the point cloud is composed of two classes (visible, not-visible) non-linearly separable. The second step is to perform a point cloud classification by applying the Gaussian radial basis function, which measures the similarity in term of distance between each leaf and a landmark leaf. It allows approximating the visibility requests to extract the leaves that will be used to calculate the amount of indirect illumination exchanged between neighbor leaves. Our approach allows efficiently treat the light exchanges in the scene of a forest, it allows a fast computation and produces images of good visual quality, all this takes advantage of the immense power of computation of the GPU.

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.

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.

Stochastic Surface Mesh Reconstruction

NASA Astrophysics Data System (ADS)

Ozendi, M.; Akca, D.; Topan, H.

2018-05-01

A generic and practical methodology is presented for 3D surface mesh reconstruction from the terrestrial laser scanner (TLS) derived point clouds. It has two main steps. The first step deals with developing an anisotropic point error model, which is capable of computing the theoretical precisions of 3D coordinates of each individual point in the point cloud. The magnitude and direction of the errors are represented in the form of error ellipsoids. The following second step is focused on the stochastic surface mesh reconstruction. It exploits the previously determined error ellipsoids by computing a point-wise quality measure, which takes into account the semi-diagonal axis length of the error ellipsoid. The points only with the least errors are used in the surface triangulation. The remaining ones are automatically discarded.

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.

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.

mPano: cloud-based mobile panorama view from single picture

NASA Astrophysics Data System (ADS)

Li, Hongzhi; Zhu, Wenwu

2013-09-01

Panorama view provides people an informative and natural user experience to represent the whole scene. The advances on mobile augmented reality, mobile-cloud computing, and mobile internet can enable panorama view on mobile phone with new functionalities, such as anytime anywhere query where a landmark picture is and what the whole scene looks like. To generate and explore panorama view on mobile devices faces significant challenges due to the limitations of computing capacity, battery life, and memory size of mobile phones, as well as the bandwidth of mobile Internet connection. To address the challenges, this paper presents a novel cloud-based mobile panorama view system that can generate and view panorama-view on mobile devices from a single picture, namely "Pano". In our system, first, we propose a novel iterative multi-modal image retrieval (IMIR) approach to get spatially adjacent images using both tag and content information from the single picture. Second, we propose a cloud-based parallel server synthing approach to generate panorama view in cloud, against today's local-client synthing approach that is almost impossible for mobile phones. Third, we propose predictive-cache solution to reduce latency of image delivery from cloud server to the mobile client. We have built a real mobile panorama view system and perform experiments. The experimental results demonstrated the effectiveness of our system and the proposed key component technologies, especially for landmark images.

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…

A Geospatial Information Grid Framework for Geological Survey.

PubMed

Wu, Liang; Xue, Lei; Li, Chaoling; Lv, Xia; Chen, Zhanlong; Guo, Mingqiang; Xie, Zhong

2015-01-01

The use of digital information in geological fields is becoming very important. Thus, informatization in geological surveys should not stagnate as a result of the level of data accumulation. The integration and sharing of distributed, multi-source, heterogeneous geological information is an open problem in geological domains. Applications and services use geological spatial data with many features, including being cross-region and cross-domain and requiring real-time updating. As a result of these features, desktop and web-based geographic information systems (GISs) experience difficulties in meeting the demand for geological spatial information. To facilitate the real-time sharing of data and services in distributed environments, a GIS platform that is open, integrative, reconfigurable, reusable and elastic would represent an indispensable tool. The purpose of this paper is to develop a geological cloud-computing platform for integrating and sharing geological information based on a cloud architecture. Thus, the geological cloud-computing platform defines geological ontology semantics; designs a standard geological information framework and a standard resource integration model; builds a peer-to-peer node management mechanism; achieves the description, organization, discovery, computing and integration of the distributed resources; and provides the distributed spatial meta service, the spatial information catalog service, the multi-mode geological data service and the spatial data interoperation service. The geological survey information cloud-computing platform has been implemented, and based on the platform, some geological data services and geological processing services were developed. Furthermore, an iron mine resource forecast and an evaluation service is introduced in this paper.

A Geospatial Information Grid Framework for Geological Survey

PubMed Central

Wu, Liang; Xue, Lei; Li, Chaoling; Lv, Xia; Chen, Zhanlong; Guo, Mingqiang; Xie, Zhong

2015-01-01

The use of digital information in geological fields is becoming very important. Thus, informatization in geological surveys should not stagnate as a result of the level of data accumulation. The integration and sharing of distributed, multi-source, heterogeneous geological information is an open problem in geological domains. Applications and services use geological spatial data with many features, including being cross-region and cross-domain and requiring real-time updating. As a result of these features, desktop and web-based geographic information systems (GISs) experience difficulties in meeting the demand for geological spatial information. To facilitate the real-time sharing of data and services in distributed environments, a GIS platform that is open, integrative, reconfigurable, reusable and elastic would represent an indispensable tool. The purpose of this paper is to develop a geological cloud-computing platform for integrating and sharing geological information based on a cloud architecture. Thus, the geological cloud-computing platform defines geological ontology semantics; designs a standard geological information framework and a standard resource integration model; builds a peer-to-peer node management mechanism; achieves the description, organization, discovery, computing and integration of the distributed resources; and provides the distributed spatial meta service, the spatial information catalog service, the multi-mode geological data service and the spatial data interoperation service. The geological survey information cloud-computing platform has been implemented, and based on the platform, some geological data services and geological processing services were developed. Furthermore, an iron mine resource forecast and an evaluation service is introduced in this paper. PMID:26710255

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.

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.

Cloud immersion building shielding factors for US residential structures.

PubMed

Dickson, E D; Hamby, D M

2014-12-01

This paper presents validated building shielding factors designed for contemporary US housing-stock under an idealized, yet realistic, exposure scenario within a semi-infinite cloud of radioactive material. The building shielding factors are intended for use in emergency planning and level three probabilistic risk assessments for a variety of postulated radiological events in which a realistic assessment is necessary to better understand the potential risks for accident mitigation and emergency response planning. Factors are calculated from detailed computational housing-units models using the general-purpose Monte Carlo N-Particle computational code, MCNP5, and are benchmarked from a series of narrow- and broad-beam measurements analyzing the shielding effectiveness of ten common general-purpose construction materials and ten shielding models representing the primary weather barriers (walls and roofs) of likely US housing-stock. Each model was designed to scale based on common residential construction practices and include, to the extent practical, all structurally significant components important for shielding against ionizing radiation. Calculations were performed for floor-specific locations as well as for computing a weighted-average representative building shielding factor for single- and multi-story detached homes, both with and without basement, as well for single-wide manufactured housing-units.

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.

Evaluating virtual hosted desktops for graphics-intensive astronomy

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

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.

Cost Effectiveness Tradeoffs in Computer Standardization and Technology Insertion.

DTIC Science & Technology

1986-06-01

S MONITORING ORGANIZATION REPORT NUMBER(S) P-1931 6aNAME OF PERFORMING ORGANIZATION 16b OFFICE SYMBOL 7a NAME oF MONITORIG ORGANIZAIO0N Institute for...shown in the figure, is a circle with a symbolic V"valve" on top controlling a flow arrow that goes between a symbolic " cloud " (that also resembles a four...leaf clover) and the rectangle which represents the level. If the rate is an inflow, the arrow points to the level and the cloud is called a source

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.

Radiative Feedback of Forming Star Clusters on Their GMC Environments: Theory and Simulation

NASA Astrophysics Data System (ADS)

Howard, C. S.; Pudritz, R. E.; Harris, W. E.

2013-07-01

Star clusters form from dense clumps within a molecular cloud. Radiation from these newly formed clusters feeds back on their natal molecular cloud through heating and ionization which ultimately stops gas accretion into the cluster. Recent studies suggest that radiative feedback effects from a single cluster may be sufficient to disrupt an entire cloud over a short timescale. Simulating cluster formation on a large scale, however, is computationally demanding due to the high number of stars involved. For this reason, we present a model for representing the radiative output of an entire cluster which involves randomly sampling an initial mass function (IMF) as the cluster accretes mass. We show that this model is able to reproduce the star formation histories of observed clusters. To examine the degree to which radiative feedback shapes the evolution of a molecular cloud, we use the FLASH adaptive-mesh refinement hydrodynamics code to simulate cluster formation in a turbulent cloud. Unlike previous studies, sink particles are used to represent a forming cluster rather than individual stars. Our cluster model is then coupled with a raytracing scheme to treat radiative transfer as the clusters grow in mass. This poster will outline the details of our model and present preliminary results from our 3D hydrodynamical simulations.

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.

Implementation of aerosol-cloud interactions in the regional atmosphere-aerosol model COSMO-MUSCAT(5.0) and evaluation using satellite data

NASA Astrophysics Data System (ADS)

Dipu, Sudhakar; Quaas, Johannes; Wolke, Ralf; Stoll, Jens; Mühlbauer, Andreas; Sourdeval, Odran; Salzmann, Marc; Heinold, Bernd; Tegen, Ina

2017-06-01

The regional atmospheric model Consortium for Small-scale Modeling (COSMO) coupled to the Multi-Scale Chemistry Aerosol Transport model (MUSCAT) is extended in this work to represent aerosol-cloud interactions. Previously, only one-way interactions (scavenging of aerosol and in-cloud chemistry) and aerosol-radiation interactions were included in this model. The new version allows for a microphysical aerosol effect on clouds. For this, we use the optional two-moment cloud microphysical scheme in COSMO and the online-computed aerosol information for cloud condensation nuclei concentrations (Cccn), replacing the constant Cccn profile. In the radiation scheme, we have implemented a droplet-size-dependent cloud optical depth, allowing now for aerosol-cloud-radiation interactions. To evaluate the models with satellite data, the Cloud Feedback Model Intercomparison Project Observation Simulator Package (COSP) has been implemented. A case study has been carried out to understand the effects of the modifications, where the modified modeling system is applied over the European domain with a horizontal resolution of 0.25° × 0.25°. To reduce the complexity in aerosol-cloud interactions, only warm-phase clouds are considered. We found that the online-coupled aerosol introduces significant changes for some cloud microphysical properties. The cloud effective radius shows an increase of 9.5 %, and the cloud droplet number concentration is reduced by 21.5 %.

Verification of NWP Cloud Properties using A-Train Satellite Observations

NASA Astrophysics Data System (ADS)

Kucera, P. A.; Weeks, C.; Wolff, C.; Bullock, R.; Brown, B.

2011-12-01

Recently, the NCAR Model Evaluation Tools (MET) has been enhanced to incorporate satellite observations for the verification of Numerical Weather Prediction (NWP) cloud products. We have developed tools that match fields spatially (both in the vertical and horizontal dimensions) to compare NWP products with satellite observations. These matched fields provide diagnostic evaluation of cloud macro attributes such as vertical distribution of clouds, cloud top height, and the spatial and seasonal distribution of cloud fields. For this research study, we have focused on using CloudSat, CALIPSO, and MODIS observations to evaluate cloud fields for a variety of NWP fields and derived products. We have selected cases ranging from large, mid-latitude synoptic systems to well-organized tropical cyclones. For each case, we matched the observed cloud field with gridded model and/or derived product fields. CloudSat and CALIPSO observations and model fields were matched and compared in the vertical along the orbit track. MODIS data and model fields were matched and compared in the horizontal. We then use MET to compute the verification statistics to quantify the performance of the models in representing the cloud fields. In this presentation we will give a summary of our comparison and show verification results for both synoptic and tropical cyclone cases.

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.

Parameterization of Shortwave Cloud Optical Properties for a Mixture of Ice Particle Habits for use in Atmospheric Models

NASA Technical Reports Server (NTRS)

Chou, Ming-Dah; Lee, Kyu-Tae; Yang, Ping; Lau, William K. M. (Technical Monitor)

2002-01-01

Based on the single-scattering optical properties pre-computed with an improved geometric optics method, the bulk absorption coefficient, single-scattering albedo, and asymmetry factor of ice particles have been parameterized as a function of the effective particle size of a mixture of ice habits, the ice water amount, and spectral band. The parameterization has been applied to computing fluxes for sample clouds with various particle size distributions and assumed mixtures of particle habits. It is found that flux calculations are not overly sensitive to the assumed particle habits if the definition of the effective particle size is consistent with the particle habits that the parameterization is based. Otherwise, the error in the flux calculations could reach a magnitude unacceptable for climate studies. Different from many previous studies, the parameterization requires only an effective particle size representing all ice habits in a cloud layer, but not the effective size of individual ice habits.

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.

Mobile-Cloud Assisted Video Summarization Framework for Efficient Management of Remote Sensing Data Generated by Wireless Capsule Sensors

PubMed Central

Mehmood, Irfan; Sajjad, Muhammad; Baik, Sung Wook

2014-01-01

Wireless capsule endoscopy (WCE) has great advantages over traditional endoscopy because it is portable and easy to use, especially in remote monitoring health-services. However, during the WCE process, the large amount of captured video data demands a significant deal of computation to analyze and retrieve informative video frames. In order to facilitate efficient WCE data collection and browsing task, we present a resource- and bandwidth-aware WCE video summarization framework that extracts the representative keyframes of the WCE video contents by removing redundant and non-informative frames. For redundancy elimination, we use Jeffrey-divergence between color histograms and inter-frame Boolean series-based correlation of color channels. To remove non-informative frames, multi-fractal texture features are extracted to assist the classification using an ensemble-based classifier. Owing to the limited WCE resources, it is impossible for the WCE system to perform computationally intensive video summarization tasks. To resolve computational challenges, mobile-cloud architecture is incorporated, which provides resizable computing capacities by adaptively offloading video summarization tasks between the client and the cloud server. The qualitative and quantitative results are encouraging and show that the proposed framework saves information transmission cost and bandwidth, as well as the valuable time of data analysts in browsing remote sensing data. PMID:25225874

Mobile-cloud assisted video summarization framework for efficient management of remote sensing data generated by wireless capsule sensors.

PubMed

Mehmood, Irfan; Sajjad, Muhammad; Baik, Sung Wook

2014-09-15

Wireless capsule endoscopy (WCE) has great advantages over traditional endoscopy because it is portable and easy to use, especially in remote monitoring health-services. However, during the WCE process, the large amount of captured video data demands a significant deal of computation to analyze and retrieve informative video frames. In order to facilitate efficient WCE data collection and browsing task, we present a resource- and bandwidth-aware WCE video summarization framework that extracts the representative keyframes of the WCE video contents by removing redundant and non-informative frames. For redundancy elimination, we use Jeffrey-divergence between color histograms and inter-frame Boolean series-based correlation of color channels. To remove non-informative frames, multi-fractal texture features are extracted to assist the classification using an ensemble-based classifier. Owing to the limited WCE resources, it is impossible for the WCE system to perform computationally intensive video summarization tasks. To resolve computational challenges, mobile-cloud architecture is incorporated, which provides resizable computing capacities by adaptively offloading video summarization tasks between the client and the cloud server. The qualitative and quantitative results are encouraging and show that the proposed framework saves information transmission cost and bandwidth, as well as the valuable time of data analysts in browsing remote sensing data.

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.

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.

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.

Unveiling aerosol-cloud interactions - Part 1: Cloud contamination in satellite products enhances the aerosol indirect forcing estimate

NASA Astrophysics Data System (ADS)

Christensen, Matthew W.; Neubauer, David; Poulsen, Caroline A.; Thomas, Gareth E.; McGarragh, Gregory R.; Povey, Adam C.; Proud, Simon R.; Grainger, Roy G.

2017-11-01

Increased concentrations of aerosol can enhance the albedo of warm low-level cloud. Accurately quantifying this relationship from space is challenging due in part to contamination of aerosol statistics near clouds. Aerosol retrievals near clouds can be influenced by stray cloud particles in areas assumed to be cloud-free, particle swelling by humidification, shadows and enhanced scattering into the aerosol field from (3-D radiative transfer) clouds. To screen for this contamination we have developed a new cloud-aerosol pairing algorithm (CAPA) to link cloud observations to the nearest aerosol retrieval within the satellite image. The distance between each aerosol retrieval and nearest cloud is also computed in CAPA. Results from two independent satellite imagers, the Advanced Along-Track Scanning Radiometer (AATSR) and Moderate Resolution Imaging Spectroradiometer (MODIS), show a marked reduction in the strength of the intrinsic aerosol indirect radiative forcing when selecting aerosol pairs that are located farther away from the clouds (-0.28±0.26 W m-2) compared to those including pairs that are within 15 km of the nearest cloud (-0.49±0.18 W m-2). The larger aerosol optical depths in closer proximity to cloud artificially enhance the relationship between aerosol-loading, cloud albedo, and cloud fraction. These results suggest that previous satellite-based radiative forcing estimates represented in key climate reports may be exaggerated due to the inclusion of retrieval artefacts in the aerosol located near clouds.

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.

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

Machine learning patterns for neuroimaging-genetic studies in the cloud.

PubMed

Da Mota, Benoit; Tudoran, Radu; Costan, Alexandru; Varoquaux, Gaël; Brasche, Goetz; Conrod, Patricia; Lemaitre, Herve; Paus, Tomas; Rietschel, Marcella; Frouin, Vincent; Poline, Jean-Baptiste; Antoniu, Gabriel; Thirion, Bertrand

2014-01-01

Brain imaging is a natural intermediate phenotype to understand the link between genetic information and behavior or brain pathologies risk factors. Massive efforts have been made in the last few years to acquire high-dimensional neuroimaging and genetic data on large cohorts of subjects. The statistical analysis of such data is carried out with increasingly sophisticated techniques and represents a great computational challenge. Fortunately, increasing computational power in distributed architectures can be harnessed, if new neuroinformatics infrastructures are designed and training to use these new tools is provided. Combining a MapReduce framework (TomusBLOB) with machine learning algorithms (Scikit-learn library), we design a scalable analysis tool that can deal with non-parametric statistics on high-dimensional data. End-users describe the statistical procedure to perform and can then test the model on their own computers before running the very same code in the cloud at a larger scale. We illustrate the potential of our approach on real data with an experiment showing how the functional signal in subcortical brain regions can be significantly fit with genome-wide genotypes. This experiment demonstrates the scalability and the reliability of our framework in the cloud with a 2 weeks deployment on hundreds of virtual machines.

Climate Analytics-As-a-Service (CAaas), Advanced Information Systems, and Services to Accelerate the Climate Sciences.

NASA Astrophysics Data System (ADS)

McInerney, M.; Schnase, J. L.; Duffy, D.; Tamkin, G.; Nadeau, D.; Strong, S.; Thompson, J. H.; Sinno, S.; Lazar, D.

2014-12-01

The climate sciences represent a big data domain that is experiencing unprecedented growth. In our efforts to address the big data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We focus on analytics, because it is the knowledge gained from our interactions with big data that ultimately product societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and SaaS enabled by cloud computing. Within this framework, cloud computing plays an important role; however, we see it as only one element in a constellation of capabilities that are essential to delivering climate analytics-as-a-service. These elements are essential because in the aggregate they lead to generativity, a capacity for self-assembly that we feel is the key to solving many of the big data challenges in this domain. This poster will highlight specific examples of CAaaS using climate reanalysis data, high-performance cloud computing, map reduce, and the Climate Data Services API.

Cloudbus Toolkit for Market-Oriented Cloud Computing

NASA Astrophysics Data System (ADS)

Buyya, Rajkumar; Pandey, Suraj; Vecchiola, Christian

This keynote paper: (1) presents the 21st century vision of computing and identifies various IT paradigms promising to deliver computing as a utility; (2) defines the architecture for creating market-oriented Clouds and computing atmosphere by leveraging technologies such as virtual machines; (3) provides thoughts on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation; (4) presents the work carried out as part of our new Cloud Computing initiative, called Cloudbus: (i) Aneka, a Platform as a Service software system containing SDK (Software Development Kit) for construction of Cloud applications and deployment on private or public Clouds, in addition to supporting market-oriented resource management; (ii) internetworking of Clouds for dynamic creation of federated computing environments for scaling of elastic applications; (iii) creation of 3rd party Cloud brokering services for building content delivery networks and e-Science applications and their deployment on capabilities of IaaS providers such as Amazon along with Grid mashups; (iv) CloudSim supporting modelling and simulation of Clouds for performance studies; (v) Energy Efficient Resource Allocation Mechanisms and Techniques for creation and management of Green Clouds; and (vi) pathways for future research.

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.

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.

The Basics of Cloud Computing

ERIC Educational Resources Information Center

Kaestner, Rich

2012-01-01

Most school business officials have heard the term "cloud computing" bandied about and may have some idea of what the term means. In fact, they likely already leverage a cloud-computing solution somewhere within their district. But what does cloud computing really mean? This brief article puts a bit of definition behind the term and helps one…

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.

Relating Solar Resource Variability to Cloud Type

NASA Astrophysics Data System (ADS)

Hinkelman, L. M.; Sengupta, M.

2012-12-01

Power production from renewable energy (RE) resources is rapidly increasing. Generation of renewable energy is quite variable since the solar and wind resources that form the inputs are, themselves, inherently variable. There is thus a need to understand the impact of renewable generation on the transmission grid. Such studies require estimates of high temporal and spatial resolution power output under various scenarios, which can be created from corresponding solar resource data. Satellite-based solar resource estimates are the best source of long-term solar irradiance data for the typically large areas covered by transmission studies. As satellite-based resource datasets are generally available at lower temporal and spatial resolution than required, there is, in turn, a need to downscale these resource data. Downscaling in both space and time requires information about solar irradiance variability, which is primarily a function of cloud types and properties. In this study, we analyze the relationship between solar resource variability and satellite-based cloud properties. One-minute resolution surface irradiance data were obtained from a number of stations operated by the National Oceanic and Atmospheric Administration (NOAA) under the Surface Radiation (SURFRAD) and Integrated Surface Irradiance Study (ISIS) networks as well as from NREL's Solar Radiation Research Laboratory (SRRL) in Golden, Colorado. Individual sites were selected so that a range of meteorological conditions would be represented. Cloud information at a nominal 4 km resolution and half hour intervals was derived from NOAA's Geostationary Operation Environmental Satellite (GOES) series of satellites. Cloud class information from the GOES data set was then used to select and composite irradiance data from the measurement sites. The irradiance variability for each cloud classification was characterized using general statistics of the fluxes themselves and their variability in time, as represented by ramps computed for time scales from 10 s to 0.5 hr. The statistical relationships derived using this method will be presented, comparing and contrasting the statistics computed for the different cloud types. The implications for downscaling irradiances from satellites or forecast models will also be discussed.

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.

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.

Sensitivity of Cirrus Bidirectional Reflectance at MODIS Bands to Vertical Inhomogeneity of Ice Crystal Habits and Size Distribution

NASA Technical Reports Server (NTRS)

Yang, P.; Gao, B.-C.; Baum, B. A.; Wiscombe, W.; Hu, Y.; Nasiri, S. L.; Soulen, P. F.; Heymsfield, A. J.; McFarquhar, G. M.; Miloshevich, L. M.

2000-01-01

A common assumption in satellite imager-based cirrus retrieval algorithms is that the radiative properties of a cirrus cloud may be represented by those associated with a specific ice crystal shape (or habit) and a single particle size distribution. However, observations of cirrus clouds have shown that the shapes and sizes of ice crystals may vary substantially with height within the clouds. In this study we investigate the sensitivity of the top-of-atmosphere bidirectional reflectances at two MODIS bands centered at 0.65 micron and 2.11 micron to the cirrus models assumed to be either a single homogeneous layer or three distinct but contiguous, layers. First, we define the single- and three-layer cirrus cloud models with respect to ice crystal habit and size distribution on the basis of in situ replicator data acquired during the First ISCCP Regional Experiment (FIRE-II), held in Kansas during the fall of 1991. Subsequently, fundamental light scattering and radiative transfer theory is employed to determine the single scattering and the bulk radiative properties of the cirrus cloud. Regarding the radiative transfer computations, we present a discrete form of the adding/doubling principle by introducing a direct transmission function, which is computationally straightforward and efficient an improvement over previous methods. For the 0.65 micron band, at which absorption by ice is negligible, there is little difference between the bidirectional reflectances calculated for the one- and three-layer cirrus models, suggesting that the vertical inhomogeneity effect is relatively unimportant. At the 2.11 micron band, the bidirectional reflectances computed for both optically thin (tau = 1) and thick (tau = 10) cirrus clouds show significant differences between the results for the one- and three-layer models. The reflectances computed for the three-layer cirrus model are substantially larger than those computed for the single-layer cirrus. Finally, we find that cloud reflectance is very sensitive to the optical properties of the small crystals that predominate in the top layer of the three-layer cirrus model. It is critical to define the most realistic geometric shape for the small "quasi-spherical" ice crystals in the top layer for obtaining reliable single-scattering parameters and bulk radiative properties of cirrus.

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

Martin, Shawn

This code consists of Matlab routines which enable the user to perform non-manifold surface reconstruction via triangulation from high dimensional point cloud data. The code was based on an algorithm originally developed in [Freedman (2007), An Incremental Algorithm for Reconstruction of Surfaces of Arbitrary Codimension Computational Geometry: Theory and Applications, 36(2):106-116]. This algorithm has been modified to accommodate non-manifold surface according to the work described in [S. Martin and J.-P. Watson (2009), Non-Manifold Surface Reconstruction from High Dimensional Point Cloud DataSAND #5272610].The motivation for developing the code was a point cloud describing the molecular conformation space of cyclooctane (C8H16). Cyclooctanemore » conformation space was represented using points in 72 dimensions (3 coordinates for each molecule). The code was used to triangulate the point cloud and thereby study the geometry and topology of cyclooctane. Futures applications are envisioned for peptides and proteins.« less

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.

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

RACORO Continental Boundary Layer Cloud Investigations: 1. Case Study Development and Ensemble Large-Scale Forcings

NASA Technical Reports Server (NTRS)

Vogelmann, Andrew M.; Fridlind, Ann M.; Toto, Tami; Endo, Satoshi; Lin, Wuyin; Wang, Jian; Feng, Sha; Zhang, Yunyan; Turner, David D.; Liu, Yangang;

RACORO continental boundary layer cloud investigations: 1. Case study development and ensemble large-scale forcings

NASA Astrophysics Data System (ADS)

Vogelmann, Andrew M.; Fridlind, Ann M.; Toto, Tami; Endo, Satoshi; Lin, Wuyin; Wang, Jian; Feng, Sha; Zhang, Yunyan; Turner, David D.; Liu, Yangang; Li, Zhijin; Xie, Shaocheng; Ackerman, Andrew S.; Zhang, Minghua; Khairoutdinov, Marat

2015-06-01

Observation-based modeling case studies of continental boundary layer clouds have been developed to study cloudy boundary layers, aerosol influences upon them, and their representation in cloud- and global-scale models. Three 60 h case study periods span the temporal evolution of cumulus, stratiform, and drizzling boundary layer cloud systems, representing mixed and transitional states rather than idealized or canonical cases. Based on in situ measurements from the Routine AAF (Atmospheric Radiation Measurement (ARM) Aerial Facility) CLOWD (Clouds with Low Optical Water Depth) Optical Radiative Observations (RACORO) field campaign and remote sensing observations, the cases are designed with a modular configuration to simplify use in large-eddy simulations (LES) and single-column models. Aircraft measurements of aerosol number size distribution are fit to lognormal functions for concise representation in models. Values of the aerosol hygroscopicity parameter, κ, are derived from observations to be 0.10, which are lower than the 0.3 typical over continents and suggestive of a large aerosol organic fraction. Ensemble large-scale forcing data sets are derived from the ARM variational analysis, European Centre for Medium-Range Weather Forecasts, and a multiscale data assimilation system. The forcings are assessed through comparison of measured bulk atmospheric and cloud properties to those computed in "trial" large-eddy simulations, where more efficient run times are enabled through modest reductions in grid resolution and domain size compared to the full-sized LES grid. Simulations capture many of the general features observed, but the state-of-the-art forcings were limited at representing details of cloud onset, and tight gradients and high-resolution transients of importance. Methods for improving the initial conditions and forcings are discussed. The cases developed are available to the general modeling community for studying continental boundary clouds.

Exploring Students' Acceptance of Team Messaging Services: The Roles of Social Presence and Motivation

ERIC Educational Resources Information Center

Huang, Yong-Ming

2017-01-01

Team messaging services represent a type of cloud computing applications that support not only the messaging among users but also the collaboration in a team. Accordingly, team messaging services have great potential to facilitate students' collaboration. However, only few studies utilized such services to support students' collaboration and…

Flexible services for the support of research.

PubMed

Turilli, Matteo; Wallom, David; Williams, Chris; Gough, Steve; Curran, Neal; Tarrant, Richard; Bretherton, Dan; Powell, Andy; Johnson, Matt; Harmer, Terry; Wright, Peter; Gordon, John

2013-01-28

Cloud computing has been increasingly adopted by users and providers to promote a flexible, scalable and tailored access to computing resources. Nonetheless, the consolidation of this paradigm has uncovered some of its limitations. Initially devised by corporations with direct control over large amounts of computational resources, cloud computing is now being endorsed by organizations with limited resources or with a more articulated, less direct control over these resources. The challenge for these organizations is to leverage the benefits of cloud computing while dealing with limited and often widely distributed computing resources. This study focuses on the adoption of cloud computing by higher education institutions and addresses two main issues: flexible and on-demand access to a large amount of storage resources, and scalability across a heterogeneous set of cloud infrastructures. The proposed solutions leverage a federated approach to cloud resources in which users access multiple and largely independent cloud infrastructures through a highly customizable broker layer. This approach allows for a uniform authentication and authorization infrastructure, a fine-grained policy specification and the aggregation of accounting and monitoring. Within a loosely coupled federation of cloud infrastructures, users can access vast amount of data without copying them across cloud infrastructures and can scale their resource provisions when the local cloud resources become insufficient.

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.

Challenges in Securing the Interface Between the Cloud and Pervasive Systems

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

Lagesse, Brent J

2011-01-01

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

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.

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

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.

Making Cloud Computing Available For Researchers and Innovators (Invited)

NASA Astrophysics Data System (ADS)

Winsor, R.

2010-12-01

High Performance Computing (HPC) facilities exist in most academic institutions but are almost invariably over-subscribed. Access is allocated based on academic merit, the only practical method of assigning valuable finite compute resources. Cloud computing on the other hand, and particularly commercial clouds, draw flexibly on an almost limitless resource as long as the user has sufficient funds to pay the bill. How can the commercial cloud model be applied to scientific computing? Is there a case to be made for a publicly available research cloud and how would it be structured? This talk will explore these themes and describe how Cybera, a not-for-profit non-governmental organization in Alberta Canada, aims to leverage its high speed research and education network to provide cloud computing facilities for a much wider user base.

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

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…

Capturing and analyzing wheelchair maneuvering patterns with mobile cloud computing.

PubMed

Fu, Jicheng; Hao, Wei; White, Travis; Yan, Yuqing; Jones, Maria; Jan, Yih-Kuen

2013-01-01

Power wheelchairs have been widely used to provide independent mobility to people with disabilities. Despite great advancements in power wheelchair technology, research shows that wheelchair related accidents occur frequently. To ensure safe maneuverability, capturing wheelchair maneuvering patterns is fundamental to enable other research, such as safe robotic assistance for wheelchair users. In this study, we propose to record, store, and analyze wheelchair maneuvering data by means of mobile cloud computing. Specifically, the accelerometer and gyroscope sensors in smart phones are used to record wheelchair maneuvering data in real-time. Then, the recorded data are periodically transmitted to the cloud for storage and analysis. The analyzed results are then made available to various types of users, such as mobile phone users, traditional desktop users, etc. The combination of mobile computing and cloud computing leverages the advantages of both techniques and extends the smart phone's capabilities of computing and data storage via the Internet. We performed a case study to implement the mobile cloud computing framework using Android smart phones and Google App Engine, a popular cloud computing platform. Experimental results demonstrated the feasibility of the proposed mobile cloud computing framework.

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

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.

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.

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 and cloud microphysics covariability in the northeast Pacific boundary layer estimated with ship-based and satellite remote sensing observations

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

Painemal, David; Chiu, J. -Y. Christine; Minnis, Patrick

We utilized ship measurements collected over the northeast Pacific along transects between the port of Los Angeles (33.7°N, 118.2°W) and Honolulu (21.3°N, 157.8°W) during May to August 2013 in order to investigate the covariability between marine low cloud microphysical and aerosol properties. Ship-based retrievals of cloud optical depth (τ) from a Sun photometer and liquid water path (LWP) from a microwave radiometer were combined to derive cloud droplet number concentration N d and compute a cloud-aerosol interaction (ACI) metric defined as ACI CCN=∂ ln(N d)/∂ ln(CCN), with CCN denoting the cloud condensation nuclei concentration measured at 0.4% (CCN 0.4) andmore » 0.3% (CCN 0.3) supersaturation. Analysis of CCN 0.4, accumulation mode aerosol concentration (N a), and extinction coefficient (σ ext) indicates that N a and σ ext can be used as CCN 0.4 proxies for estimating ACI. ACI CCN derived from 10 min averaged N d and CCN 0.4 and CCN 0.3, and CCN 0.4 regressions using N a and σ ext, produce high ACI CCN: near 1.0, that is, a fractional change in aerosols is associated with an equivalent fractional change in Nd. ACI CCN computed in deep boundary layers was small (ACI CCN=0.60), indicating that surface aerosol measurements inadequately represent the aerosol variability below clouds. Satellite cloud retrievals from MODerate-resolution Imaging Spectroradiometer and GOES-15 data were compared against ship-based retrievals and further analyzed to compute a satellite-based ACI CCN. We found that the satellite data correlated well with their ship-based counterparts with linear correlation coefficients equal to or greater than 0.78. Combined satellite Nd and ship-based CCN 0.4 and Na yielded a maximum ACI CCN=0.88–0.92, a value slightly less than the ship-based ACI CCN, but still consistent with aircraft-based studies in the eastern Pacific.« less

Aerosol and cloud microphysics covariability in the northeast Pacific boundary layer estimated with ship-based and satellite remote sensing observations

DOE PAGES

Painemal, David; Chiu, J. -Y. Christine; Minnis, Patrick; ...

2017-02-27

We utilized ship measurements collected over the northeast Pacific along transects between the port of Los Angeles (33.7°N, 118.2°W) and Honolulu (21.3°N, 157.8°W) during May to August 2013 in order to investigate the covariability between marine low cloud microphysical and aerosol properties. Ship-based retrievals of cloud optical depth (τ) from a Sun photometer and liquid water path (LWP) from a microwave radiometer were combined to derive cloud droplet number concentration N d and compute a cloud-aerosol interaction (ACI) metric defined as ACI CCN=∂ ln(N d)/∂ ln(CCN), with CCN denoting the cloud condensation nuclei concentration measured at 0.4% (CCN 0.4) andmore » 0.3% (CCN 0.3) supersaturation. Analysis of CCN 0.4, accumulation mode aerosol concentration (N a), and extinction coefficient (σ ext) indicates that N a and σ ext can be used as CCN 0.4 proxies for estimating ACI. ACI CCN derived from 10 min averaged N d and CCN 0.4 and CCN 0.3, and CCN 0.4 regressions using N a and σ ext, produce high ACI CCN: near 1.0, that is, a fractional change in aerosols is associated with an equivalent fractional change in Nd. ACI CCN computed in deep boundary layers was small (ACI CCN=0.60), indicating that surface aerosol measurements inadequately represent the aerosol variability below clouds. Satellite cloud retrievals from MODerate-resolution Imaging Spectroradiometer and GOES-15 data were compared against ship-based retrievals and further analyzed to compute a satellite-based ACI CCN. We found that the satellite data correlated well with their ship-based counterparts with linear correlation coefficients equal to or greater than 0.78. Combined satellite Nd and ship-based CCN 0.4 and Na yielded a maximum ACI CCN=0.88–0.92, a value slightly less than the ship-based ACI CCN, but still consistent with aircraft-based studies in the eastern Pacific.« less

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.

Gamma-Weighted Discrete Ordinate Two-Stream Approximation for Computation of Domain Averaged Solar Irradiance

NASA Technical Reports Server (NTRS)

Kato, S.; Smith, G. L.; Barker, H. W.

2001-01-01

An algorithm is developed for the gamma-weighted discrete ordinate two-stream approximation that computes profiles of domain-averaged shortwave irradiances for horizontally inhomogeneous cloudy atmospheres. The algorithm assumes that frequency distributions of cloud optical depth at unresolved scales can be represented by a gamma distribution though it neglects net horizontal transport of radiation. This algorithm is an alternative to the one used in earlier studies that adopted the adding method. At present, only overcast cloudy layers are permitted.

Libraries in the Cloud: Making a Case for Google and Amazon

ERIC Educational Resources Information Center

Buck, Stephanie

2009-01-01

As news outlets create headlines such as "A Cloud & A Prayer," "The Cloud Is the Computer," and "Leveraging Clouds to Make You More Efficient," many readers have been left with cloud confusion. Many definitions exist for cloud computing, and a uniform definition is hard to find. In its most basic form, cloud…

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

More reliable forecasts with less precise computations: a fast-track route to cloud-resolved weather and climate simulators?

PubMed Central

Palmer, T. N.

2014-01-01

This paper sets out a new methodological approach to solving the equations for simulating and predicting weather and climate. In this approach, the conventionally hard boundary between the dynamical core and the sub-grid parametrizations is blurred. This approach is motivated by the relatively shallow power-law spectrum for atmospheric energy on scales of hundreds of kilometres and less. It is first argued that, because of this, the closure schemes for weather and climate simulators should be based on stochastic–dynamic systems rather than deterministic formulae. Second, as high-wavenumber elements of the dynamical core will necessarily inherit this stochasticity during time integration, it is argued that the dynamical core will be significantly over-engineered if all computations, regardless of scale, are performed completely deterministically and if all variables are represented with maximum numerical precision (in practice using double-precision floating-point numbers). As the era of exascale computing is approached, an energy- and computationally efficient approach to cloud-resolved weather and climate simulation is described where determinism and numerical precision are focused on the largest scales only. PMID:24842038

More reliable forecasts with less precise computations: a fast-track route to cloud-resolved weather and climate simulators?

PubMed

Palmer, T N

2014-06-28

This paper sets out a new methodological approach to solving the equations for simulating and predicting weather and climate. In this approach, the conventionally hard boundary between the dynamical core and the sub-grid parametrizations is blurred. This approach is motivated by the relatively shallow power-law spectrum for atmospheric energy on scales of hundreds of kilometres and less. It is first argued that, because of this, the closure schemes for weather and climate simulators should be based on stochastic-dynamic systems rather than deterministic formulae. Second, as high-wavenumber elements of the dynamical core will necessarily inherit this stochasticity during time integration, it is argued that the dynamical core will be significantly over-engineered if all computations, regardless of scale, are performed completely deterministically and if all variables are represented with maximum numerical precision (in practice using double-precision floating-point numbers). As the era of exascale computing is approached, an energy- and computationally efficient approach to cloud-resolved weather and climate simulation is described where determinism and numerical precision are focused on the largest scales only.

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…

Investigation of diocotron modes in toroidally trapped electron plasmas using non-destructive method

NASA Astrophysics Data System (ADS)

Lachhvani, Lavkesh; Pahari, Sambaran; Sengupta, Sudip; Yeole, Yogesh G.; Bajpai, Manu; Chattopadhyay, P. K.

2017-10-01

Experiments with trapped electron plasmas in a SMall Aspect Ratio Toroidal device (SMARTEX-C) have demonstrated a flute-like mode represented by oscillations on capacitive (wall) probes. Although analogous to diocotron mode observed in linear electron traps, the mode evolution in toroids can have interesting consequences due to the presence of in-homogeneous magnetic field. In SMARTEX-C, the probe signals are observed to undergo transition from small, near-sinusoidal oscillations to large amplitude, non-linear "double-peaked" oscillations. To interpret the wall probe signal and bring forth the dynamics, an expression for the induced current on the probe for an oscillating charge is derived, utilizing Green's Reciprocation Theorem. Equilibrium position, poloidal velocity of the charge cloud, and charge content of the cloud, required to compute the induced current, are estimated from the experiments. Signal through capacitive probes is thereby computed numerically for possible charge cloud trajectories. In order to correlate with experiments, starting with an intuitive guess of the trajectory, the model is evolved and tweaked to arrive at a signal consistent with experimentally observed probe signals. A possible vortex like dynamics is predicted, hitherto unexplored in toroidal geometries, for a limited set of experimental observations from SMARTEX-C. Though heuristic, a useful interpretation of capacitive probe data in terms of charge cloud dynamics is obtained.

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.

RACORO continental boundary layer cloud investigations. Part I: Case study development and ensemble large-scale forcings

DOE PAGES

Vogelmann, Andrew M.; Fridlind, Ann M.; Toto, Tami; ...

2015-06-19

Observation-based modeling case studies of continental boundary layer clouds have been developed to study cloudy boundary layers, aerosol influences upon them, and their representation in cloud- and global-scale models. Three 60-hour case study periods span the temporal evolution of cumulus, stratiform, and drizzling boundary layer cloud systems, representing mixed and transitional states rather than idealized or canonical cases. Based on in-situ measurements from the RACORO field campaign and remote-sensing observations, the cases are designed with a modular configuration to simplify use in large-eddy simulations (LES) and single-column models. Aircraft measurements of aerosol number size distribution are fit to lognormal functionsmore » for concise representation in models. Values of the aerosol hygroscopicity parameter, κ, are derived from observations to be ~0.10, which are lower than the 0.3 typical over continents and suggestive of a large aerosol organic fraction. Ensemble large-scale forcing datasets are derived from the ARM variational analysis, ECMWF forecasts, and a multi-scale data assimilation system. The forcings are assessed through comparison of measured bulk atmospheric and cloud properties to those computed in 'trial' large-eddy simulations, where more efficient run times are enabled through modest reductions in grid resolution and domain size compared to the full-sized LES grid. Simulations capture many of the general features observed, but the state-of-the-art forcings were limited at representing details of cloud onset, and tight gradients and high-resolution transients of importance. Methods for improving the initial conditions and forcings are discussed. The cases developed are available to the general modeling community for studying continental boundary clouds.« less

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.

A cloud-based system for measuring radiation treatment plan similarity

NASA Astrophysics Data System (ADS)

Andrea, Jennifer

PURPOSE: Radiation therapy is used to treat cancer using carefully designed plans that maximize the radiation dose delivered to the target and minimize damage to healthy tissue, with the dose administered over multiple occasions. Creating treatment plans is a laborious process and presents an obstacle to more frequent replanning, which remains an unsolved problem. However, in between new plans being created, the patient's anatomy can change due to multiple factors including reduction in tumor size and loss of weight, which results in poorer patient outcomes. Cloud computing is a newer technology that is slowly being used for medical applications with promising results. The objective of this work was to design and build a system that could analyze a database of previously created treatment plans, which are stored with their associated anatomical information in studies, to find the one with the most similar anatomy to a new patient. The analyses would be performed in parallel on the cloud to decrease the computation time of finding this plan. METHODS: The system used SlicerRT, a radiation therapy toolkit for the open-source platform 3D Slicer, for its tools to perform the similarity analysis algorithm. Amazon Web Services was used for the cloud instances on which the analyses were performed, as well as for storage of the radiation therapy studies and messaging between the instances and a master local computer. A module was built in SlicerRT to provide the user with an interface to direct the system on the cloud, as well as to perform other related tasks. RESULTS: The cloud-based system out-performed previous methods of conducting the similarity analyses in terms of time, as it analyzed 100 studies in approximately 13 minutes, and produced the same similarity values as those methods. It also scaled up to larger numbers of studies to analyze in the database with a small increase in computation time of just over 2 minutes. CONCLUSION: This system successfully analyzes a large database of radiation therapy studies and finds the one that is most similar to a new patient, which represents a potential step forward in achieving feasible adaptive radiation therapy replanning.

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

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.

Cloud Based Earth Observation Data Exploitation Platforms

NASA Astrophysics Data System (ADS)

Romeo, A.; Pinto, S.; Loekken, S.; Marin, A.

2017-12-01

In the last few years data produced daily by several private and public Earth Observation (EO) satellites reached the order of tens of Terabytes, representing for scientists and commercial application developers both a big opportunity for their exploitation and a challenge for their management. New IT technologies, such as Big Data and cloud computing, enable the creation of web-accessible data exploitation platforms, which offer to scientists and application developers the means to access and use EO data in a quick and cost effective way. RHEA Group is particularly active in this sector, supporting the European Space Agency (ESA) in the Exploitation Platforms (EP) initiative, developing technology to build multi cloud platforms for the processing and analysis of Earth Observation data, and collaborating with larger European initiatives such as the European Plate Observing System (EPOS) and the European Open Science Cloud (EOSC). An EP is a virtual workspace, providing a user community with access to (i) large volume of data, (ii) algorithm development and integration environment, (iii) processing software and services (e.g. toolboxes, visualization routines), (iv) computing resources, (v) collaboration tools (e.g. forums, wiki, etc.). When an EP is dedicated to a specific Theme, it becomes a Thematic Exploitation Platform (TEP). Currently, ESA has seven TEPs in a pre-operational phase dedicated to geo-hazards monitoring and prevention, costal zones, forestry areas, hydrology, polar regions, urban areas and food security. On the technology development side, solutions like the multi cloud EO data processing platform provides the technology to integrate ICT resources and EO data from different vendors in a single platform. In particular it offers (i) Multi-cloud data discovery, (ii) Multi-cloud data management and access and (iii) Multi-cloud application deployment. This platform has been demonstrated with the EGI Federated Cloud, Innovation Platform Testbed Poland and the Amazon Web Services cloud. This work will present an overview of the TEPs and the multi-cloud EO data processing platform, and discuss their main achievements and their impacts in the context of distributed Research Infrastructures such as EPOS and EOSC.

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.

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.

Explicit Cloud Nucleation from Arbitrary Mixtures of Aerosol Types and Sizes Using an Ultra-Efficient In-Line Aerosol Bin Model in High-Resolution Simulations of Hurricanes

NASA Astrophysics Data System (ADS)

Walko, R. L.; Ashby, T.; Cotton, W. R.

2017-12-01

The fundamental role of atmospheric aerosols in the process of cloud droplet nucleation is well known, and there is ample evidence that the concentration, size, and chemistry of aerosols can strongly influence microphysical, thermodynamic, and ultimately dynamic properties and evolution of clouds and convective systems. With the increasing availability of observation- and model-based environmental representations of different types of anthropogenic and natural aerosols, there is increasing need for models to be able to represent which aerosols nucleate and which do not in supersaturated conditions. However, this is a very complex process that involves competition for water vapor between multiple aerosol species (chemistries) and different aerosol sizes within each species. Attempts have been made to parameterize the nucleation properties of mixtures of different aerosol species, but it is very difficult or impossible to represent all possible mixtures that may occur in practice. As part of a modeling study of the impact of anthropogenic and natural aerosols on hurricanes, we developed an ultra-efficient aerosol bin model to represent nucleation in a high-resolution atmospheric model that explicitly represents cloud- and subcloud-scale vertical motion. The bin model is activated at any time and location in a simulation where supersaturation occurs and is potentially capable of activating new cloud droplets. The bins are populated from the aerosol species that are present at the given time and location and by multiple sizes from each aerosol species according to a characteristic size distribution, and the chemistry of each species is represented by its absorption or adsorption characteristics. The bin model is integrated in time increments that are smaller than that of the atmospheric model in order to temporally resolve the peak supersaturation, which determines the total nucleated number. Even though on the order of 100 bins are typically utilized, this leads only to a 10 or 20% increase in overall computational cost due to the efficiency of the bin model. This method is highly versatile in that it automatically accommodates any possible number and mixture of different aerosol species. Applications of this model to simulations of Typhoon Nuri will be presented.

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.

2D modeling of direct laser metal deposition process using a finite particle method

NASA Astrophysics Data System (ADS)

Anedaf, T.; Abbès, B.; Abbès, F.; Li, Y. M.

2018-05-01

Direct laser metal deposition is one of the material additive manufacturing processes used to produce complex metallic parts. A thorough understanding of the underlying physical phenomena is required to obtain a high-quality parts. In this work, a mathematical model is presented to simulate the coaxial laser direct deposition process tacking into account of mass addition, heat transfer, and fluid flow with free surface and melting. The fluid flow in the melt pool together with mass and energy balances are solved using the Computational Fluid Dynamics (CFD) software NOGRID-points, based on the meshless Finite Pointset Method (FPM). The basis of the computations is a point cloud, which represents the continuum fluid domain. Each finite point carries all fluid information (density, velocity, pressure and temperature). The dynamic shape of the molten zone is explicitly described by the point cloud. The proposed model is used to simulate a single layer cladding.

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).

Optical fibre multi-parameter sensing with secure cloud based signal capture and processing

NASA Astrophysics Data System (ADS)

Newe, Thomas; O'Connell, Eoin; Meere, Damien; Yuan, Hongwei; Leen, Gabriel; O'Keeffe, Sinead; Lewis, Elfed

2016-05-01

Recent advancements in cloud computing technologies in the context of optical and optical fibre based systems are reported. The proliferation of real time and multi-channel based sensor systems represents significant growth in data volume. This coupled with a growing need for security presents many challenges and presents a huge opportunity for an evolutionary step in the widespread application of these sensing technologies. A tiered infrastructural system approach is adopted that is designed to facilitate the delivery of Optical Fibre-based "SENsing as a Service- SENaaS". Within this infrastructure, novel optical sensing platforms, deployed within different environments, are interfaced with a Cloud-based backbone infrastructure which facilitates the secure collection, storage and analysis of real-time data. Feedback systems, which harness this data to affect a change within the monitored location/environment/condition, are also discussed. The cloud based system presented here can also be used with chemical and physical sensors that require real-time data analysis, processing and feedback.

MarDRe: efficient MapReduce-based removal of duplicate DNA reads in the cloud.

PubMed

Expósito, Roberto R; Veiga, Jorge; González-Domínguez, Jorge; Touriño, Juan

2017-09-01

This article presents MarDRe, a de novo cloud-ready duplicate and near-duplicate removal tool that can process single- and paired-end reads from FASTQ/FASTA datasets. MarDRe takes advantage of the widely adopted MapReduce programming model to fully exploit Big Data technologies on cloud-based infrastructures. Written in Java to maximize cross-platform compatibility, MarDRe is built upon the open-source Apache Hadoop project, the most popular distributed computing framework for scalable Big Data processing. On a 16-node cluster deployed on the Amazon EC2 cloud platform, MarDRe is up to 8.52 times faster than a representative state-of-the-art tool. Source code in Java and Hadoop as well as a user's guide are freely available under the GNU GPLv3 license at http://mardre.des.udc.es . rreye@udc.es. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

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

An Assessment of New Satellite Data Products for the Development of a Long-Term Global Solar Resource at 10-100 km

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

Stackhouse Jr., Paul W.; Minnis, Patrick; Perez, Richard

A project representing an effort to reprocess the NASA based solar resource data sets is reviewed. The effort represented a collaboration between NASA, NOAA, NREL and the SUNY-Albany and aimed to deliver a 10 km resolution, 3-hourly data set spanning from 1983 through near-present. Part of the project was to transition project capability to NREL for annual processing to extend data set. Due to delays in the key input project called ISCCP, we evaluate only Beta versions of this data set and also introduce the potential use of another NASA Langley based cloud data set for the CERES project. Themore » CERES project uses these cloud properties to compute global top-of-atmosphere and surface fluxes at the 1x1 degree resolution. Here, we also briefly discuss these data sets in potential usage for solar resource benchmarking.« less

An Assessment of New Satellite Data Products for the Development of a Long-Term Global Solar Resource at 10-100 km

NASA Technical Reports Server (NTRS)

Stackhouse, Paul W., Jr.; Minnis, Patrick; Perez, Richard; Sengupta, Manajit; Knapp, Kenneth; Mikovitz, J. Colleen; Schlemmer, James; Scarino, Benjamin; Zhang, Taiping; Cox, Stephen J.

2016-01-01

A project representing an effort to reprocess the NASA based solar resource data sets is reviewed. The effort represented a collaboration between NASA, NOAA, NREL and the SUNY-Albany and aimed to deliver a 10 km resolution, 3-hourly data set spanning from 1983 through near-present. Part of the project was to transition project capability to NREL for annual processing to extend data set. Due to delays in the key input project called ISCCP, we evaluate only Beta versions of this data set and also introduce the potential use of another NASA Langley based cloud data set for the CERES project. The CERES project uses these cloud properties to compute global top-of-atmosphere and surface fluxes at the 1x1 degree resolution. Here, we also briefly discuss these data sets in potential usage for solar resource benchmarking.

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.

Applying super-droplets as a compact representation of warm-rain microphysics for aerosol-cloud-aerosol interactions

NASA Astrophysics Data System (ADS)

Arabas, S.; Jaruga, A.; Pawlowska, H.; Grabowski, W. W.

2012-12-01

Clouds may influence aerosol characteristics of their environment. The relevant processes include wet deposition (rainout or washout) and cloud condensation nuclei (CCN) recycling through evaporation of cloud droplets and drizzle drops. Recycled CCN physicochemical properties may be altered if the evaporated droplets go through collisional growth or irreversible chemical reactions (e.g. SO2 oxidation). The key challenge of representing these processes in a numerical cloud model stems from the need to track properties of activated CCN throughout the cloud lifecycle. Lack of such "memory" characterises the so-called bulk, multi-moment as well as bin representations of cloud microphysics. In this study we apply the particle-based scheme of Shima et al. 2009. Each modelled particle (aka super-droplet) is a numerical proxy for a multiplicity of real-world CCN, cloud, drizzle or rain particles of the same size, nucleus type,and position. Tracking cloud nucleus properties is an inherent feature of the particle-based frameworks, making them suitable for studying aerosol-cloud-aerosol interactions. The super-droplet scheme is furthermore characterized by linear scalability in the number of computational particles, and no numerical diffusion in the condensational and in the Monte-Carlo type collisional growth schemes. The presentation will focus on processing of aerosol by a drizzling stratocumulus deck. The simulations are carried out using a 2D kinematic framework and a VOCALS experiment inspired set-up (see http://www.rap.ucar.edu/~gthompsn/workshop2012/case1/).

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

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.

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

PubMed

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

2011-10-13

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

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

PubMed Central

2011-01-01

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

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…

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.

A new airborne sampler for interstitial particles in ice and liquid clouds

NASA Astrophysics Data System (ADS)

Moharreri, A.; Craig, L.; Rogers, D. C.; Brown, M.; Dhaniyala, S.

2011-12-01

In-situ measurements of cloud droplets and aerosols using aircraft platforms are required for understanding aerosol-cloud processes and aiding development of improved aerosol-cloud models. A variety of clouds with different temperature ranges and cloud particle sizes/phases must be studied for comprehensive knowledge about the role of aerosols in the formation and evolution of cloud systems under different atmospheric conditions. While representative aerosol measurements are regularly made from aircrafts under clear air conditions, aerosol measurements in clouds are often contaminated by the generation of secondary particles from the high speed impaction of ice particles and liquid droplets on the surfaces of the aircraft probes/inlets. A new interstitial particle sampler, called the blunt-body aerosol sampler (BASE) has been designed and used for aerosol sampling during two recent airborne campaigns using NCAR/NSF C-130 aircraft: PLOWS (2009-2010) and ICE-T (2011). Central to the design of the new interstitial inlet is an upstream blunt body housing that acts to shield/deflect large cloud droplets and ice particles from an aft sampling region. The blunt-body design also ensures that small shatter particles created from the impaction of cloud-droplets on the blunt-body are not present in the aft region where the interstitial inlet is located. Computational fluid dynamics (CFD) simulations along with particle transport modeling and wind tunnel studies have been utilized in different stages of design and development of this inlet. The initial flights tests during the PLOWS campaign showed that the inlet had satisfactory performance only in warm clouds and when large precipitation droplets were absent. In the presence of large droplets and ice, the inlet samples were contaminated with significant shatter artifacts. These initial results were reanalyzed in conjunction with a computational droplet shatter model and the numerical results were used to arrive at an improved sampler design. Analysis of the data from the recent ICE-T campaign with the improved sampler design shows that the modified version of BASE can provide shatter-artifact free sampling of aerosol particles in the presence of ice particles and significantly reduced shatter artifacts in warm clouds. Detailed design and modeling aspects of the sampler will be discussed and the sampler performance in warm and cold clouds will be presented and compared with measurements made using other aerosol inlets flown on the NCAR/NSF C-130 aircraft.

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

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…

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

Cloud Infrastructure & Applications - CloudIA

NASA Astrophysics Data System (ADS)

Sulistio, Anthony; Reich, Christoph; Doelitzscher, Frank

The idea behind Cloud Computing is to deliver Infrastructure-as-a-Services and Software-as-a-Service over the Internet on an easy pay-per-use business model. To harness the potentials of Cloud Computing for e-Learning and research purposes, and to small- and medium-sized enterprises, the Hochschule Furtwangen University establishes a new project, called Cloud Infrastructure & Applications (CloudIA). The CloudIA project is a market-oriented cloud infrastructure that leverages different virtualization technologies, by supporting Service-Level Agreements for various service offerings. This paper describes the CloudIA project in details and mentions our early experiences in building a private cloud using an existing infrastructure.

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

Extratropical Cyclone in the Southern Ocean

NASA Technical Reports Server (NTRS)

2001-01-01

These images from the Multi-angle Imaging SpectroRadiometer portray an occluded extratropical cyclone situated in the Southern Ocean, about 650 kilometers south of the Eyre Peninsula, South Australia.

Parts of the Yorke Peninsula and a portion of the Murray-Darling River basin are visible between the clouds near the top of the left-hand image, a true-color view from MISR's nadir(vertical-viewing) camera. Retrieved cloud-tracked wind velocities are indicated by the superimposed arrows. The image on the right displays cloud-top heights. Areas where cloud heights could not be retrieved are shown in black. Both the wind vectors and the cloud heights were derived using data from multiple MISR cameras within automated computer processing algorithms. The stereoscopic algorithms used to generate these results are still being refined, and future versions of these products may show modest changes.

Extratropical cyclones are the dominant weather system at midlatitudes, and the term is used generically for region allow-pressure systems in the mid- to high-latitudes. In the southern hemisphere, cyclonic rotation is clockwise. These storms obtain their energy from temperature differences between air masses on either side of warm and cold fronts, and their characteristic pattern is of warm and cold fronts radiating out from a migrating low pressure center which forms, deepens, and dissipates as the fronts fold and collapse on each other. The center of this cyclone has started to decay, with the band of cloud to the south most likely representing the main front that was originally connected with the cyclonic circulation.

These views were acquired on October 11, 2001 during Terra orbit 9650, and represent an area of about 380 kilometers x 1900 kilometers.

Extratropical Cyclone in the Southern Ocean

NASA Technical Reports Server (NTRS)

2002-01-01

These images from the Multi-angle Imaging SpectroRadiometer (MISR) portray an occluded extratropical cyclone situated in the Southern Ocean, about 650 kilometers south of the Eyre Peninsula, South Australia. The left-hand image, a true-color view from MISR's nadir (vertical-viewing) camera, shows clouds just south of the Yorke Peninsula and the Murray-Darling river basin in Australia. Retrieved cloud-tracked wind velocities are indicated by the superimposed arrows. The image on the right displays cloud-top heights. Areas where cloud heights could not be retrieved are shown in black. Both the wind vectors and the cloud heights were derived using data from multiple MISR cameras within automated computer processing algorithms. The stereoscopic algorithms used to generate these results are still being refined, and future versions of these products may show modest changes. Extratropical cyclones are the dominant weather system at midlatitudes, and the term is used generically for regional low-pressure systems in the mid- to high-latitudes. In the southern hemisphere, cyclonic rotation is clockwise. These storms obtain their energy from temperature differences between air masses on either side of warm and cold fronts, and their characteristic pattern is of warm and cold fronts radiating out from a migrating low pressure center which forms, deepens, and dissipates as the fronts fold and collapse on each other. The center of this cyclone has started to decay, with the band of cloud to the south most likely representing the main front that was originally connected with the cyclonic circulation. These views were acquired on October 11, 2001, and the large view represents an area of about 380 kilometers x 1900 kilometers. Image courtesy NASA/GSFC/LaRC/JPL, MISR Team.

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.

Covariance analyses of satellite-derived mesoscale wind fields

NASA Technical Reports Server (NTRS)

Maddox, R. A.; Vonder Haar, T. H.

1979-01-01

Statistical structure functions have been computed independently for nine satellite-derived mesoscale wind fields that were obtained on two different days. Small cumulus clouds were tracked at 5 min intervals, but since these clouds occurred primarily in the warm sectors of midlatitude cyclones the results cannot be considered representative of the circulations within cyclones in general. The field structure varied considerably with time and was especially affected if mesoscale features were observed. The wind fields on the 2 days studied were highly anisotropic with large gradients in structure occurring approximately normal to the mean flow. Structure function calculations for the combined set of satellite winds were used to estimate random error present in the fields. It is concluded for these data that the random error in vector winds derived from cumulus cloud tracking using high-frequency satellite data is less than 1.75 m/s. Spatial correlation functions were also computed for the nine data sets. Normalized correlation functions were considerably different for u and v components and decreased rapidly as data point separation increased for both components. The correlation functions for transverse and longitudinal components decreased less rapidly as data point separation increased.

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…

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

NASA Astrophysics Data System (ADS)

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

2012-06-01

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

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.

The beta distribution: A statistical model for world cloud cover

NASA Technical Reports Server (NTRS)

Falls, L. W.

1973-01-01

Much work has been performed in developing empirical global cloud cover models. This investigation was made to determine an underlying theoretical statistical distribution to represent worldwide cloud cover. The beta distribution with probability density function is given to represent the variability of this random variable. It is shown that the beta distribution possesses the versatile statistical characteristics necessary to assume the wide variety of shapes exhibited by cloud cover. A total of 160 representative empirical cloud cover distributions were investigated and the conclusion was reached that this study provides sufficient statical evidence to accept the beta probability distribution as the underlying model for world cloud cover.

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.

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.

Bioinformatics on the cloud computing platform Azure.

PubMed

Shanahan, Hugh P; Owen, Anne M; Harrison, Andrew P

2014-01-01

We discuss the applicability of the Microsoft cloud computing platform, Azure, for bioinformatics. We focus on the usability of the resource rather than its performance. We provide an example of how R can be used on Azure to analyse a large amount of microarray expression data deposited at the public database ArrayExpress. We provide a walk through to demonstrate explicitly how Azure can be used to perform these analyses in Appendix S1 and we offer a comparison with a local computation. We note that the use of the Platform as a Service (PaaS) offering of Azure can represent a steep learning curve for bioinformatics developers who will usually have a Linux and scripting language background. On the other hand, the presence of an additional set of libraries makes it easier to deploy software in a parallel (scalable) fashion and explicitly manage such a production run with only a few hundred lines of code, most of which can be incorporated from a template. We propose that this environment is best suited for running stable bioinformatics software by users not involved with its development.

Bioinformatics on the Cloud Computing Platform Azure

PubMed Central

Shanahan, Hugh P.; Owen, Anne M.; Harrison, Andrew P.

2014-01-01

We discuss the applicability of the Microsoft cloud computing platform, Azure, for bioinformatics. We focus on the usability of the resource rather than its performance. We provide an example of how R can be used on Azure to analyse a large amount of microarray expression data deposited at the public database ArrayExpress. We provide a walk through to demonstrate explicitly how Azure can be used to perform these analyses in Appendix S1 and we offer a comparison with a local computation. We note that the use of the Platform as a Service (PaaS) offering of Azure can represent a steep learning curve for bioinformatics developers who will usually have a Linux and scripting language background. On the other hand, the presence of an additional set of libraries makes it easier to deploy software in a parallel (scalable) fashion and explicitly manage such a production run with only a few hundred lines of code, most of which can be incorporated from a template. We propose that this environment is best suited for running stable bioinformatics software by users not involved with its development. PMID:25050811

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

Numerical studies of a model fermion-boson system

NASA Astrophysics Data System (ADS)

Cheng, T.; Gospodarczyk, E. R.; Su, Q.; Grobe, R.

2010-02-01

We study the spectral and dynamical properties of a simplified model system of interacting fermions and bosons. The spatial discretization and an effective truncation of the Hilbert space permit us to compute the distribution of the bare fermions and bosons in the energy eigenstates of the coupled system. These states represent the physical particles and are used to examine the validity of the analytical predictions by perturbation theory and by the Greenberg-Schweber approximation that assumes all fermions are at rest. As an example of our numerical framework, we examine how a bare electron can trigger the creation of a cloud of virtual bosons around. We relate this cloud to the properties of the associated energy eigenstates.

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

A lightweight distributed framework for computational offloading in mobile cloud computing.

PubMed

Shiraz, Muhammad; Gani, Abdullah; Ahmad, Raja Wasim; Adeel Ali Shah, Syed; Karim, Ahmad; Rahman, Zulkanain Abdul

2014-01-01

The latest developments in mobile computing technology have enabled intensive applications on the modern Smartphones. However, such applications are still constrained by limitations in processing potentials, storage capacity and battery lifetime of the Smart Mobile Devices (SMDs). Therefore, Mobile Cloud Computing (MCC) leverages the application processing services of computational clouds for mitigating resources limitations in SMDs. Currently, a number of computational offloading frameworks are proposed for MCC wherein the intensive components of the application are outsourced to computational clouds. Nevertheless, such frameworks focus on runtime partitioning of the application for computational offloading, which is time consuming and resources intensive. The resource constraint nature of SMDs require lightweight procedures for leveraging computational clouds. Therefore, this paper presents a lightweight framework which focuses on minimizing additional resources utilization in computational offloading for MCC. The framework employs features of centralized monitoring, high availability and on demand access services of computational clouds for computational offloading. As a result, the turnaround time and execution cost of the application are reduced. The framework is evaluated by testing prototype application in the real MCC environment. The lightweight nature of the proposed framework is validated by employing computational offloading for the proposed framework and the latest existing frameworks. Analysis shows that by employing the proposed framework for computational offloading, the size of data transmission is reduced by 91%, energy consumption cost is minimized by 81% and turnaround time of the application is decreased by 83.5% as compared to the existing offloading frameworks. Hence, the proposed framework minimizes additional resources utilization and therefore offers lightweight solution for computational offloading in MCC.

A Lightweight Distributed Framework for Computational Offloading in Mobile Cloud Computing

PubMed Central

Shiraz, Muhammad; Gani, Abdullah; Ahmad, Raja Wasim; Adeel Ali Shah, Syed; Karim, Ahmad; Rahman, Zulkanain Abdul

2014-01-01

The latest developments in mobile computing technology have enabled intensive applications on the modern Smartphones. However, such applications are still constrained by limitations in processing potentials, storage capacity and battery lifetime of the Smart Mobile Devices (SMDs). Therefore, Mobile Cloud Computing (MCC) leverages the application processing services of computational clouds for mitigating resources limitations in SMDs. Currently, a number of computational offloading frameworks are proposed for MCC wherein the intensive components of the application are outsourced to computational clouds. Nevertheless, such frameworks focus on runtime partitioning of the application for computational offloading, which is time consuming and resources intensive. The resource constraint nature of SMDs require lightweight procedures for leveraging computational clouds. Therefore, this paper presents a lightweight framework which focuses on minimizing additional resources utilization in computational offloading for MCC. The framework employs features of centralized monitoring, high availability and on demand access services of computational clouds for computational offloading. As a result, the turnaround time and execution cost of the application are reduced. The framework is evaluated by testing prototype application in the real MCC environment. The lightweight nature of the proposed framework is validated by employing computational offloading for the proposed framework and the latest existing frameworks. Analysis shows that by employing the proposed framework for computational offloading, the size of data transmission is reduced by 91%, energy consumption cost is minimized by 81% and turnaround time of the application is decreased by 83.5% as compared to the existing offloading frameworks. Hence, the proposed framework minimizes additional resources utilization and therefore offers lightweight solution for computational offloading in MCC. PMID:25127245

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…

A Quantitative Investigation of Cloud Computing Adoption in Nigeria: Testing an Enhanced Technology Acceptance Model

ERIC Educational Resources Information Center

Ishola, Bashiru Abayomi

2017-01-01

Cloud computing has recently emerged as a potential alternative to the traditional on-premise computing that businesses can leverage to achieve operational efficiencies. Consequently, technology managers are often tasked with the responsibilities to analyze the barriers and variables critical to organizational cloud adoption decisions. This…

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

The Intercomparison of 3D Radiation Codes (I3RC): Showcasing Mathematical and Computational Physics in a Critical Atmospheric Application

NASA Astrophysics Data System (ADS)

Davis, A. B.; Cahalan, R. F.

2001-05-01

The Intercomparison of 3D Radiation Codes (I3RC) is an on-going initiative involving an international group of over 30 researchers engaged in the numerical modeling of three-dimensional radiative transfer as applied to clouds. Because of their strong variability and extreme opacity, clouds are indeed a major source of uncertainty in the Earth's local radiation budget (at GCM grid scales). Also 3D effects (at satellite pixel scales) invalidate the standard plane-parallel assumption made in the routine of cloud-property remote sensing at NASA and NOAA. Accordingly, the test-cases used in I3RC are based on inputs and outputs which relate to cloud effects in atmospheric heating rates and in real-world remote sensing geometries. The main objectives of I3RC are to (1) enable participants to improve their models, (2) publish results as a community, (3) archive source code, and (4) educate. We will survey the status of I3RC and its plans for the near future with a special emphasis on the mathematical models and computational approaches. We will also describe some of the prime applications of I3RC's efforts in climate models, cloud-resolving models, and remote-sensing observations of clouds, or that of the surface in their presence. In all these application areas, computational efficiency is the main concern and not accuracy. One of I3RC's main goals is to document the performance of as wide a variety as possible of three-dimensional radiative transfer models for a small but representative number of ``cases.'' However, it is dominated by modelers working at the level of linear transport theory (i.e., they solve the radiative transfer equation) and an overwhelming majority of these participants use slow-but-robust Monte Carlo techniques. This means that only a small portion of the efficiency vs. accuracy vs. flexibility domain is currently populated by I3RC participants. To balance this natural clustering the present authors have organized a systematic outreach towards modelers that have used approximate methods in radiation transport. In this context, different, presumably simpler, equations (such as diffusion) are used in order to make a significant gain on the efficiency axis. We will describe in some detail the most promising approaches to approximate 3D radiative transfer in clouds. Somewhat paradoxically, and in spite of its importance in the above-mentioned applications, approximate radiative transfer modeling lags significantly behind its exact counterpart because the required mathematical and computational culture is essentially alien to the native atmospheric radiation community. I3RC is receiving enough funding from NASA/HQ and DOE/ARM for its essential operations out of NASA/GSFC. However, this does not cover the time and effort of any of the participants; so only existing models were entered. At present, none of inherently approximate methods are represented, only severe truncations of some exact methods. We therefore welcome the Math/Geo initiative at NSF which should enable the proper consortia of experts in atmospheric radiation and in applied mathematics to fill an important niche.

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.

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.

Highly parameterized model calibration with cloud computing: an example of regional flow model calibration in northeast Alberta, Canada

NASA Astrophysics Data System (ADS)

Hayley, Kevin; Schumacher, J.; MacMillan, G. J.; Boutin, L. C.

2014-05-01

Expanding groundwater datasets collected by automated sensors, and improved groundwater databases, have caused a rapid increase in calibration data available for groundwater modeling projects. Improved methods of subsurface characterization have increased the need for model complexity to represent geological and hydrogeological interpretations. The larger calibration datasets and the need for meaningful predictive uncertainty analysis have both increased the degree of parameterization necessary during model calibration. Due to these competing demands, modern groundwater modeling efforts require a massive degree of parallelization in order to remain computationally tractable. A methodology for the calibration of highly parameterized, computationally expensive models using the Amazon EC2 cloud computing service is presented. The calibration of a regional-scale model of groundwater flow in Alberta, Canada, is provided as an example. The model covers a 30,865-km2 domain and includes 28 hydrostratigraphic units. Aquifer properties were calibrated to more than 1,500 static hydraulic head measurements and 10 years of measurements during industrial groundwater use. Three regionally extensive aquifers were parameterized (with spatially variable hydraulic conductivity fields), as was the aerial recharge boundary condition, leading to 450 adjustable parameters in total. The PEST-based model calibration was parallelized on up to 250 computing nodes located on Amazon's EC2 servers.

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

A Novel Tool for Simulating Aerosol-cloud Interactions with a Sectional Model Implemented to a Large-Eddy Simulator

NASA Astrophysics Data System (ADS)

Tonttila, J.; Romakkaniemi, S.; Kokkola, H.; Maalick, Z.; Korhonen, H.; Liqing, H.

2015-12-01

A new cloud-resolving model setup for studying aerosol-cloud interactions, with a special emphasis on partitioning and wet deposition of semi-volatile aerosol species, is presented. The model is based on modified versions of two well-established model components: the Large-Eddy Simulator (LES) UCLALES, and the sectional aerosol model SALSA, previously employed in the ECHAM climate model family. Implementation of the UCLALES-SALSA is described in detail. As the basis for this work, SALSA has been extended to include a sectional representation of the size distributions of cloud droplets and precipitation. Microphysical processes operating on clouds and precipitation have also been added. Given our main motivation, the cloud droplet size bins are defined according to the dry particle diameter. The droplet wet diameter is solved dynamically through condensation equations, but represents an average droplet diameter inside each size bin. This approach allows for accurate tracking of the aerosol properties inside clouds, but minimizes the computational cost. Since the actual cloud droplet diameter is not fully resolved inside the size bins, processes such as precipitation formation rely on parameterizations. For realistic growth of drizzle drops to rain, which is critical for the aerosol wet deposition, the precipitation size bins are defined according to the actual drop size. With these additions, the implementation of the SALSA model replaces most of the microphysical and thermodynamical components within the LES. The cloud properties and aerosol-cloud interactions simulated by the model are analysed and evaluated against detailed cloud microphysical boxmodel results and in-situ aerosol-cloud interaction observations from the Puijo measurement station in Kuopio, Finland. The ability of the model to reproduce the impacts of wet deposition on the aerosol population is demonstrated.

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.

Estimation of time-series properties of gourd observed solar irradiance data using cloud properties derived from satellite observations

NASA Astrophysics Data System (ADS)

Watanabe, T.; Nohara, D.

2017-12-01

The shorter temporal scale variation in the downward solar irradiance at the ground level (DSI) is not understood well because researches in the shorter-scale variation in the DSI is based on the ground observation and ground observation stations are located coarsely. Use of dataset derived from satellite observation will overcome such defect. DSI data and MODIS cloud properties product are analyzed simultaneously. Three metrics: mean, standard deviation and sample entropy, are used to evaluate time-series properties of the DSI. Three metrics are computed from two-hours time-series centered at the observation time of MODIS over the ground observation stations. We apply the regression methods to design prediction models of each three metrics from cloud properties. The validation of the model accuracy show that mean and standard deviation are predicted with a higher degree of accuracy and that the accuracy of prediction of sample entropy, which represents the complexity of time-series, is not high. One of causes of lower prediction skill of sample entropy is the resolution of the MODIS cloud properties. Higher sample entropy is corresponding to the rapid fluctuation, which is caused by the small and unordered cloud. It seems that such clouds isn't retrieved well.

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.

A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow Systems.

PubMed

Li, Xuejun; Xu, Jia; Yang, Yun

2015-01-01

Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO) algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts.

A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow Systems

PubMed Central

Li, Xuejun; Xu, Jia; Yang, Yun

2015-01-01

Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO) algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts. PMID:26357510

Multiscale Cloud System Modeling

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Moncrieff, Mitchell W.

2009-01-01

The central theme of this paper is to describe how cloud system resolving models (CRMs) of grid spacing approximately 1 km have been applied to various important problems in atmospheric science across a wide range of spatial and temporal scales and how these applications relate to other modeling approaches. A long-standing problem concerns the representation of organized precipitating convective cloud systems in weather and climate models. Since CRMs resolve the mesoscale to large scales of motion (i.e., 10 km to global) they explicitly address the cloud system problem. By explicitly representing organized convection, CRMs bypass restrictive assumptions associated with convective parameterization such as the scale gap between cumulus and large-scale motion. Dynamical models provide insight into the physical mechanisms involved with scale interaction and convective organization. Multiscale CRMs simulate convective cloud systems in computational domains up to global and have been applied in place of contemporary convective parameterizations in global models. Multiscale CRMs pose a new challenge for model validation, which is met in an integrated approach involving CRMs, operational prediction systems, observational measurements, and dynamical models in a new international project: the Year of Tropical Convection, which has an emphasis on organized tropical convection and its global effects.

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.

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.

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.

An application of statistical mechanics for representing equilibrium perimeter distributions of tropical convective clouds

NASA Astrophysics Data System (ADS)

Garrett, T. J.; Alva, S.; Glenn, I. B.; Krueger, S. K.

2015-12-01

There are two possible approaches for parameterizing sub-grid cloud dynamics in a coarser grid model. The most common is to use a fine scale model to explicitly resolve the mechanistic details of clouds to the best extent possible, and then to parameterize these behaviors cloud state for the coarser grid. A second is to invoke physical intuition and some very general theoretical principles from equilibrium statistical mechanics. This approach avoids any requirement to resolve time-dependent processes in order to arrive at a suitable solution. The second approach is widely used elsewhere in the atmospheric sciences: for example the Planck function for blackbody radiation is derived this way, where no mention is made of the complexities of modeling a large ensemble of time-dependent radiation-dipole interactions in order to obtain the "grid-scale" spectrum of thermal emission by the blackbody as a whole. We find that this statistical approach may be equally suitable for modeling convective clouds. Specifically, we make the physical argument that the dissipation of buoyant energy in convective clouds is done through mixing across a cloud perimeter. From thermodynamic reasoning, one might then anticipate that vertically stacked isentropic surfaces are characterized by a power law dlnN/dlnP = -1, where N(P) is the number clouds of perimeter P. In a Giga-LES simulation of convective clouds within a 100 km square domain we find that such a power law does appear to characterize simulated cloud perimeters along isentropes, provided a sufficient cloudy sample. The suggestion is that it may be possible to parameterize certain important aspects of cloud state without appealing to computationally expensive dynamic simulations.

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

The impact on UT/LS cirrus clouds in the CAM/CARMA model using a new interactive aerosol parameterization.

NASA Astrophysics Data System (ADS)

Maloney, C.; Toon, B.; Bardeen, C.

2017-12-01

Recent studies indicate that heterogeneous nucleation may play a large role in cirrus cloud formation in the UT/LS, a region previously thought to be primarily dominated by homogeneous nucleation. As a result, it is beneficial to ensure that general circulation models properly represent heterogeneous nucleation in ice cloud simulations. Our work strives towards addressing this issue in the NSF/DOE Community Earth System Model's atmospheric model, CAM. More specifically we are addressing the role of heterogeneous nucleation in the coupled sectional microphysics cloud model, CARMA. Currently, our CAM/CARMA cirrus model only performs homogenous ice nucleation while ignoring heterogeneous nucleation. In our work, we couple the CAM/CARMA cirrus model with the Modal Aerosol Model (MAM). By combining the aerosol model with CAM/CARMA we can both account for heterogeneous nucleation, as well as directly link the sulfates used for homogeneous nucleation to computed fields instead of the current static field being utilized. Here we present our initial results and compare our findings to observations from the long running CALIPSO and MODIS satellite missions.

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.

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

Radiotherapy Monte Carlo simulation using cloud computing technology.

PubMed

Poole, C M; Cornelius, I; Trapp, J V; Langton, C M

2012-12-01

Cloud computing allows for vast computational resources to be leveraged quickly and easily in bursts as and when required. Here we describe a technique that allows for Monte Carlo radiotherapy dose calculations to be performed using GEANT4 and executed in the cloud, with relative simulation cost and completion time evaluated as a function of machine count. As expected, simulation completion time decreases as 1/n for n parallel machines, and relative simulation cost is found to be optimal where n is a factor of the total simulation time in hours. Using the technique, we demonstrate the potential usefulness of cloud computing as a solution for rapid Monte Carlo simulation for radiotherapy dose calculation without the need for dedicated local computer hardware as a proof of principal.

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.

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.

Cold Season QPF: Sensitivities to Snow Parameterizations and Comparisons to NASA CloudSat Observations

NASA Technical Reports Server (NTRS)

Molthan, A. L.; Haynes, J. A.; Jedlovec, G. L.; Lapenta, W. M.

2009-01-01

As operational numerical weather prediction is performed at increasingly finer spatial resolution, precipitation traditionally represented by sub-grid scale parameterization schemes is now being calculated explicitly through the use of single- or multi-moment, bulk water microphysics schemes. As computational resources grow, the real-time application of these schemes is becoming available to a broader audience, ranging from national meteorological centers to their component forecast offices. A need for improved quantitative precipitation forecasts has been highlighted by the United States Weather Research Program, which advised that gains in forecasting skill will draw upon improved simulations of clouds and cloud microphysical processes. Investments in space-borne remote sensing have produced the NASA A-Train of polar orbiting satellites, specially equipped to observe and catalog cloud properties. The NASA CloudSat instrument, a recent addition to the A-Train and the first 94 GHz radar system operated in space, provides a unique opportunity to compare observed cloud profiles to their modeled counterparts. Comparisons are available through the use of a radiative transfer model (QuickBeam), which simulates 94 GHz radar returns based on the microphysics of cloudy model profiles and the prescribed characteristics of their constituent hydrometeor classes. CloudSat observations of snowfall are presented for a case in the central United States, with comparisons made to precipitating clouds as simulated by the Weather Research and Forecasting Model and the Goddard single-moment microphysics scheme. An additional forecast cycle is performed with a temperature-based parameterization of the snow distribution slope parameter, with comparisons to CloudSat observations provided through the QuickBeam simulator.

Unveiling aerosol-cloud interactions - Part 2: Minimising the effects of aerosol swelling and wet scavenging in ECHAM6-HAM2 for comparison to satellite data

NASA Astrophysics Data System (ADS)

Neubauer, David; Christensen, Matthew W.; Poulsen, Caroline A.; Lohmann, Ulrike

2017-11-01

Aerosol-cloud interactions (ACIs) are uncertain and the estimates of the ACI effective radiative forcing (ERFaci) magnitude show a large variability. Within the Aerosol_cci project the susceptibility of cloud properties to changes in aerosol properties is derived from the high-resolution AATSR (Advanced Along-Track Scanning Radiometer) data set using the Cloud-Aerosol Pairing Algorithm (CAPA) (as described in our companion paper) and compared to susceptibilities from the global aerosol climate model ECHAM6-HAM2 and MODIS-CERES (Moderate Resolution Imaging Spectroradiometer - Clouds and the Earth's Radiant Energy System) data. For ECHAM6-HAM2 the dry aerosol is analysed to mimic the effect of CAPA. Furthermore the analysis is done for different environmental regimes. The aerosol-liquid water path relationship in ECHAM6-HAM2 is systematically stronger than in AATSR-CAPA data and cannot be explained by an overestimation of autoconversion when using diagnostic precipitation but rather by aerosol swelling in regions where humidity is high and clouds are present. When aerosol water is removed from the analysis in ECHAM6-HAM2 the strength of the susceptibilities of liquid water path, cloud droplet number concentration and cloud albedo as well as ERFaci agree much better with those of AATSR-CAPA or MODIS-CERES. When comparing satellite-derived to model-derived susceptibilities, this study finds it more appropriate to use dry aerosol in the computation of model susceptibilities. We further find that the statistical relationships inferred from different satellite sensors (AATSR-CAPA vs. MODIS-CERES) as well as from ECHAM6-HAM2 are not always of the same sign for the tested environmental conditions. In particular the susceptibility of the liquid water path is negative in non-raining scenes for MODIS-CERES but positive for AATSR-CAPA and ECHAM6-HAM2. Feedback processes like cloud-top entrainment that are missing or not well represented in the model are therefore not well constrained by satellite observations. In addition to aerosol swelling, wet scavenging and aerosol processing have an impact on liquid water path, cloud albedo and cloud droplet number susceptibilities. Aerosol processing leads to negative liquid water path susceptibilities to changes in aerosol index (AI) in ECHAM6-HAM2, likely due to aerosol-size changes by aerosol processing. Our results indicate that for statistical analysis of aerosol-cloud interactions the unwanted effects of aerosol swelling, wet scavenging and aerosol processing need to be minimised when computing susceptibilities of cloud variables to changes in aerosol.

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.

TomoMiner and TomoMinerCloud: A software platform for large-scale subtomogram structural analysis

PubMed Central

Frazier, Zachary; Xu, Min; Alber, Frank

2017-01-01

SUMMARY Cryo-electron tomography (cryoET) captures the 3D electron density distribution of macromolecular complexes in close to native state. With the rapid advance of cryoET acquisition technologies, it is possible to generate large numbers (>100,000) of subtomograms, each containing a macromolecular complex. Often, these subtomograms represent a heterogeneous sample due to variations in structure and composition of a complex in situ form or because particles are a mixture of different complexes. In this case subtomograms must be classified. However, classification of large numbers of subtomograms is a time-intensive task and often a limiting bottleneck. This paper introduces an open source software platform, TomoMiner, for large-scale subtomogram classification, template matching, subtomogram averaging, and alignment. Its scalable and robust parallel processing allows efficient classification of tens to hundreds of thousands of subtomograms. Additionally, TomoMiner provides a pre-configured TomoMinerCloud computing service permitting users without sufficient computing resources instant access to TomoMiners high-performance features. PMID:28552576

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.

Multivariate quadrature for representing cloud condensation nuclei activity of aerosol populations

DOE PAGES

Fierce, Laura; McGraw, Robert L.

2017-07-26

Here, sparse representations of atmospheric aerosols are needed for efficient regional- and global-scale chemical transport models. Here we introduce a new framework for representing aerosol distributions, based on the quadrature method of moments. Given a set of moment constraints, we show how linear programming, combined with an entropy-inspired cost function, can be used to construct optimized quadrature representations of aerosol distributions. The sparse representations derived from this approach accurately reproduce cloud condensation nuclei (CCN) activity for realistically complex distributions simulated by a particleresolved model. Additionally, the linear programming techniques described in this study can be used to bound key aerosolmore » properties, such as the number concentration of CCN. Unlike the commonly used sparse representations, such as modal and sectional schemes, the maximum-entropy approach described here is not constrained to pre-determined size bins or assumed distribution shapes. This study is a first step toward a particle-based aerosol scheme that will track multivariate aerosol distributions with sufficient computational efficiency for large-scale simulations.« less

Multivariate quadrature for representing cloud condensation nuclei activity of aerosol populations

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

Fierce, Laura; McGraw, Robert L.

Here, sparse representations of atmospheric aerosols are needed for efficient regional- and global-scale chemical transport models. Here we introduce a new framework for representing aerosol distributions, based on the quadrature method of moments. Given a set of moment constraints, we show how linear programming, combined with an entropy-inspired cost function, can be used to construct optimized quadrature representations of aerosol distributions. The sparse representations derived from this approach accurately reproduce cloud condensation nuclei (CCN) activity for realistically complex distributions simulated by a particleresolved model. Additionally, the linear programming techniques described in this study can be used to bound key aerosolmore » properties, such as the number concentration of CCN. Unlike the commonly used sparse representations, such as modal and sectional schemes, the maximum-entropy approach described here is not constrained to pre-determined size bins or assumed distribution shapes. This study is a first step toward a particle-based aerosol scheme that will track multivariate aerosol distributions with sufficient computational efficiency for large-scale simulations.« less

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

ARM - Midlatitude Continental Convective Clouds

DOE Data Explorer

Jensen, Mike; Bartholomew, Mary Jane; Genio, Anthony Del; Giangrande, Scott; Kollias, Pavlos

2012-01-19

Convective processes play a critical role in the Earth's energy balance through the redistribution of heat and moisture in the atmosphere and their link to the hydrological cycle. Accurate representation of convective processes in numerical models is vital towards improving current and future simulations of Earths climate system. Despite improvements in computing power, current operational weather and global climate models are unable to resolve the natural temporal and spatial scales important to convective processes and therefore must turn to parameterization schemes to represent these processes. In turn, parameterization schemes in cloud-resolving models need to be evaluated for their generality and application to a variety of atmospheric conditions. Data from field campaigns with appropriate forcing descriptors have been traditionally used by modelers for evaluating and improving parameterization schemes.

ARM - Midlatitude Continental Convective Clouds (comstock-hvps)

DOE Data Explorer

Jensen, Mike; Comstock, Jennifer; Genio, Anthony Del; Giangrande, Scott; Kollias, Pavlos

2012-01-06

Convective processes play a critical role in the Earth's energy balance through the redistribution of heat and moisture in the atmosphere and their link to the hydrological cycle. Accurate representation of convective processes in numerical models is vital towards improving current and future simulations of Earths climate system. Despite improvements in computing power, current operational weather and global climate models are unable to resolve the natural temporal and spatial scales important to convective processes and therefore must turn to parameterization schemes to represent these processes. In turn, parameterization schemes in cloud-resolving models need to be evaluated for their generality and application to a variety of atmospheric conditions. Data from field campaigns with appropriate forcing descriptors have been traditionally used by modelers for evaluating and improving parameterization schemes.

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

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

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.

Evaluating the Performance of Single and Double Moment Microphysics Schemes During a Synoptic-Scale Snowfall Event

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.

2011-01-01

Increases in computing resources have allowed for the utilization of high-resolution weather forecast models capable of resolving cloud microphysical and precipitation processes among varying numbers of hydrometeor categories. Several microphysics schemes are currently available within the Weather Research and Forecasting (WRF) model, ranging from single-moment predictions of precipitation content to double-moment predictions that include a prediction of particle number concentrations. Each scheme incorporates several assumptions related to the size distribution, shape, and fall speed relationships of ice crystals in order to simulate cold-cloud processes and resulting precipitation. Field campaign data offer a means of evaluating the assumptions present within each scheme. The Canadian CloudSat/CALIPSO Validation Project (C3VP) represented collaboration among the CloudSat, CALIPSO, and NASA Global Precipitation Measurement mission communities, to observe cold season precipitation processes relevant to forecast model evaluation and the eventual development of satellite retrievals of cloud properties and precipitation rates. During the C3VP campaign, widespread snowfall occurred on 22 January 2007, sampled by aircraft and surface instrumentation that provided particle size distributions, ice water content, and fall speed estimations along with traditional surface measurements of temperature and precipitation. In this study, four single-moment and two double-moment microphysics schemes were utilized to generate hypothetical WRF forecasts of the event, with C3VP data used in evaluation of their varying assumptions. Schemes that incorporate flexibility in size distribution parameters and density assumptions are shown to be preferable to fixed constants, and that a double-moment representation of the snow category may be beneficial when representing the effects of aggregation. These results may guide forecast centers in optimal configurations of their forecast models for winter weather and identify best practices present within these various schemes.

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.

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.

NASA Briefing for Unidata

NASA Technical Reports Server (NTRS)

Lynnes, Christopher

2016-01-01

The NASA representative to the Unidata Strategic Committee presented a semiannual update on NASAs work with and use of Unidata technologies. The talk covered the program of cloud computing prototypes being undertaken for the Earth Observing System Data and Information System (EOSDIS). Also discussed were dataset interoperability recommendations ratified via the EOSDIS Standards Office and the HDF Product Designer tool with respect to its possible applicability to data in network Common Data Form (NetCDF) version 4.

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.

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.

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

PubMed Central

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

2016-01-01

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

Extended outlook: description, utilization, and daily applications of cloud technology in radiology.

PubMed

Gerard, Perry; Kapadia, Neil; Chang, Patricia T; Acharya, Jay; Seiler, Michael; Lefkovitz, Zvi

2013-12-01

The purpose of this article is to discuss the concept of cloud technology, its role in medical applications and radiology, the role of the radiologist in using and accessing these vast resources of information, and privacy concerns and HIPAA compliance strategies. Cloud computing is the delivery of shared resources, software, and information to computers and other devices as a metered service. This technology has a promising role in the sharing of patient medical information and appears to be particularly suited for application in radiology, given the field's inherent need for storage and access to large amounts of data. The radiology cloud has significant strengths, such as providing centralized storage and access, reducing unnecessary repeat radiologic studies, and potentially allowing radiologic second opinions more easily. There are significant cost advantages to cloud computing because of a decreased need for infrastructure and equipment by the institution. Private clouds may be used to ensure secure storage of data and compliance with HIPAA. In choosing a cloud service, there are important aspects, such as disaster recovery plans, uptime, and security audits, that must be considered. Given that the field of radiology has become almost exclusively digital in recent years, the future of secure storage and easy access to imaging studies lies within cloud computing technology.

The monitoring and managing application of cloud computing based on Internet of Things.

PubMed

Luo, Shiliang; Ren, Bin

2016-07-01

Cloud computing and the Internet of Things are the two hot points in the Internet application field. The application of the two new technologies is in hot discussion and research, but quite less on the field of medical monitoring and managing application. Thus, in this paper, we study and analyze the application of cloud computing and the Internet of Things on the medical field. And we manage to make a combination of the two techniques in the medical monitoring and managing field. The model architecture for remote monitoring cloud platform of healthcare information (RMCPHI) was established firstly. Then the RMCPHI architecture was analyzed. Finally an efficient PSOSAA algorithm was proposed for the medical monitoring and managing application of cloud computing. Simulation results showed that our proposed scheme can improve the efficiency about 50%. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Enhancing Instruction through Constructivism, Cooperative Learning, and Cloud Computing

ERIC Educational Resources Information Center

Denton, David W.

2012-01-01

Cloud computing technologies, such as Google Docs and Microsoft Office Live, have the potential to enhance instructional methods predicated on constructivism and cooperative learning. Cloud-based application features like file sharing and online publishing are prompting departments of education across the nation to adopt these technologies.…

Fast calculation method of computer-generated hologram using a depth camera with point cloud gridding

NASA Astrophysics Data System (ADS)

Zhao, Yu; Shi, Chen-Xiao; Kwon, Ki-Chul; Piao, Yan-Ling; Piao, Mei-Lan; Kim, Nam

2018-03-01

We propose a fast calculation method for a computer-generated hologram (CGH) of real objects that uses a point cloud gridding method. The depth information of the scene is acquired using a depth camera and the point cloud model is reconstructed virtually. Because each point of the point cloud is distributed precisely to the exact coordinates of each layer, each point of the point cloud can be classified into grids according to its depth. A diffraction calculation is performed on the grids using a fast Fourier transform (FFT) to obtain a CGH. The computational complexity is reduced dramatically in comparison with conventional methods. The feasibility of the proposed method was confirmed by numerical and optical experiments.

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

PubMed

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

2013-08-02

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

Cloud computing approaches to accelerate drug discovery value chain.

PubMed

Garg, Vibhav; Arora, Suchir; Gupta, Chitra

2011-12-01

Continued advancements in the area of technology have helped high throughput screening (HTS) evolve from a linear to parallel approach by performing system level screening. Advanced experimental methods used for HTS at various steps of drug discovery (i.e. target identification, target validation, lead identification and lead validation) can generate data of the order of terabytes. As a consequence, there is pressing need to store, manage, mine and analyze this data to identify informational tags. This need is again posing challenges to computer scientists to offer the matching hardware and software infrastructure, while managing the varying degree of desired computational power. Therefore, the potential of "On-Demand Hardware" and "Software as a Service (SAAS)" delivery mechanisms cannot be denied. This on-demand computing, largely referred to as Cloud Computing, is now transforming the drug discovery research. Also, integration of Cloud computing with parallel computing is certainly expanding its footprint in the life sciences community. The speed, efficiency and cost effectiveness have made cloud computing a 'good to have tool' for researchers, providing them significant flexibility, allowing them to focus on the 'what' of science and not the 'how'. Once reached to its maturity, Discovery-Cloud would fit best to manage drug discovery and clinical development data, generated using advanced HTS techniques, hence supporting the vision of personalized medicine.

Computational biology in the cloud: methods and new insights from computing at scale.

PubMed

Kasson, Peter M

2013-01-01

The past few years have seen both explosions in the size of biological data sets and the proliferation of new, highly flexible on-demand computing capabilities. The sheer amount of information available from genomic and metagenomic sequencing, high-throughput proteomics, experimental and simulation datasets on molecular structure and dynamics affords an opportunity for greatly expanded insight, but it creates new challenges of scale for computation, storage, and interpretation of petascale data. Cloud computing resources have the potential to help solve these problems by offering a utility model of computing and storage: near-unlimited capacity, the ability to burst usage, and cheap and flexible payment models. Effective use of cloud computing on large biological datasets requires dealing with non-trivial problems of scale and robustness, since performance-limiting factors can change substantially when a dataset grows by a factor of 10,000 or more. New computing paradigms are thus often needed. The use of cloud platforms also creates new opportunities to share data, reduce duplication, and to provide easy reproducibility by making the datasets and computational methods easily available.

Importance of aggregation and small ice crystals in cirrus clouds, based on observations and an ice particle growth model

NASA Technical Reports Server (NTRS)

Mitchell, David L.; Chai, Steven K.; Dong, Yayi; Arnott, W. Patrick; Hallett, John

1993-01-01

The 1 November 1986 FIRE I case study was used to test an ice particle growth model which predicts bimodal size spectra in cirrus clouds. The model was developed from an analytically based model which predicts the height evolution of monomodal ice particle size spectra from the measured ice water content (IWC). Size spectra from the monomodal model are represented by a gamma distribution, N(D) = N(sub o)D(exp nu)exp(-lambda D), where D = ice particle maximum dimension. The slope parameter, lambda, and the parameter N(sub o) are predicted from the IWC through the growth processes of vapor diffusion and aggregation. The model formulation is analytical, computationally efficient, and well suited for incorporation into larger models. The monomodal model has been validated against two other cirrus cloud case studies. From the monomodal size spectra, the size distributions which determine concentrations of ice particles less than about 150 mu m are predicted.

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

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

A Novel Market-Oriented Dynamic Collaborative Cloud Service Platform

NASA Astrophysics Data System (ADS)

Hassan, Mohammad Mehedi; Huh, Eui-Nam

In today's world the emerging Cloud computing (Weiss, 2007) offer a new computing model where resources such as computing power, storage, online applications and networking infrastructures can be shared as "services" over the internet. Cloud providers (CPs) are incentivized by the profits to be made by charging consumers for accessing these services. Consumers, such as enterprises, are attracted by the opportunity for reducing or eliminating costs associated with "in-house" provision of these services.

A Simple Technique for Securing Data at Rest Stored in a Computing Cloud

NASA Astrophysics Data System (ADS)

Sedayao, Jeff; Su, Steven; Ma, Xiaohao; Jiang, Minghao; Miao, Kai

"Cloud Computing" offers many potential benefits, including cost savings, the ability to deploy applications and services quickly, and the ease of scaling those application and services once they are deployed. A key barrier for enterprise adoption is the confidentiality of data stored on Cloud Computing Infrastructure. Our simple technique implemented with Open Source software solves this problem by using public key encryption to render stored data at rest unreadable by unauthorized personnel, including system administrators of the cloud computing service on which the data is stored. We validate our approach on a network measurement system implemented on PlanetLab. We then use it on a service where confidentiality is critical - a scanning application that validates external firewall implementations.

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

PubMed Central

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

2017-01-01

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

Cloud Quantum Computing of an Atomic Nucleus

NASA Astrophysics Data System (ADS)

Dumitrescu, E. F.; McCaskey, A. J.; Hagen, G.; Jansen, G. R.; Morris, T. D.; Papenbrock, T.; Pooser, R. C.; Dean, D. J.; Lougovski, P.

2018-05-01

We report a quantum simulation of the deuteron binding energy on quantum processors accessed via cloud servers. We use a Hamiltonian from pionless effective field theory at leading order. We design a low-depth version of the unitary coupled-cluster ansatz, use the variational quantum eigensolver algorithm, and compute the binding energy to within a few percent. Our work is the first step towards scalable nuclear structure computations on a quantum processor via the cloud, and it sheds light on how to map scientific computing applications onto nascent quantum devices.

Cloud Quantum Computing of an Atomic Nucleus

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

Dumitrescu, Eugene F.; McCaskey, Alex J.; Hagen, Gaute

Here, we report a quantum simulation of the deuteron binding energy on quantum processors accessed via cloud servers. We use a Hamiltonian from pionless effective field theory at leading order. We design a low-depth version of the unitary coupled-cluster ansatz, use the variational quantum eigensolver algorithm, and compute the binding energy to within a few percent. Our work is the first step towards scalable nuclear structure computations on a quantum processor via the cloud, and it sheds light on how to map scientific computing applications onto nascent quantum devices.

Enterprise Cloud Architecture for Chinese Ministry of Railway

NASA Astrophysics Data System (ADS)

Shan, Xumei; Liu, Hefeng

Enterprise like PRC Ministry of Railways (MOR), is facing various challenges ranging from highly distributed computing environment and low legacy system utilization, Cloud Computing is increasingly regarded as one workable solution to address this. This article describes full scale cloud solution with Intel Tashi as virtual machine infrastructure layer, Hadoop HDFS as computing platform, and self developed SaaS interface, gluing virtual machine and HDFS with Xen hypervisor. As a result, on demand computing task application and deployment have been tackled per MOR real working scenarios at the end of article.

Dynamic VM Provisioning for TORQUE in a Cloud Environment

NASA Astrophysics Data System (ADS)

Zhang, S.; Boland, L.; Coddington, P.; Sevior, M.

2014-06-01

Cloud computing, also known as an Infrastructure-as-a-Service (IaaS), is attracting more interest from the commercial and educational sectors as a way to provide cost-effective computational infrastructure. It is an ideal platform for researchers who must share common resources but need to be able to scale up to massive computational requirements for specific periods of time. This paper presents the tools and techniques developed to allow the open source TORQUE distributed resource manager and Maui cluster scheduler to dynamically integrate OpenStack cloud resources into existing high throughput computing clusters.

Cloud Quantum Computing of an Atomic Nucleus.

PubMed

Dumitrescu, E F; McCaskey, A J; Hagen, G; Jansen, G R; Morris, T D; Papenbrock, T; Pooser, R C; Dean, D J; Lougovski, P

2018-05-25

We report a quantum simulation of the deuteron binding energy on quantum processors accessed via cloud servers. We use a Hamiltonian from pionless effective field theory at leading order. We design a low-depth version of the unitary coupled-cluster ansatz, use the variational quantum eigensolver algorithm, and compute the binding energy to within a few percent. Our work is the first step towards scalable nuclear structure computations on a quantum processor via the cloud, and it sheds light on how to map scientific computing applications onto nascent quantum devices.

Cloud Quantum Computing of an Atomic Nucleus

DOE PAGES

Dumitrescu, Eugene F.; McCaskey, Alex J.; Hagen, Gaute; ...

2018-05-23

Here, we report a quantum simulation of the deuteron binding energy on quantum processors accessed via cloud servers. We use a Hamiltonian from pionless effective field theory at leading order. We design a low-depth version of the unitary coupled-cluster ansatz, use the variational quantum eigensolver algorithm, and compute the binding energy to within a few percent. Our work is the first step towards scalable nuclear structure computations on a quantum processor via the cloud, and it sheds light on how to map scientific computing applications onto nascent quantum devices.

Putting Order Into the Cloud: Object-oriented UML-based Rule Enforcement for Document and Application Organization

DTIC Science & Technology

2010-09-01

Cloud computing describes a new distributed computing paradigm for IT data and services that involves over-the-Internet provision of dynamically scalable and often virtualized resources. While cost reduction and flexibility in storage, services, and maintenance are important considerations when deciding on whether or how to migrate data and applications to the cloud, large organizations like the Department of Defense need to consider the organization and structure of data on the cloud and the operations on such data in order to reap the full benefit of cloud

Advanced cloud fault tolerance system

NASA Astrophysics Data System (ADS)

Sumangali, K.; Benny, Niketa

2017-11-01

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

USGEO DMWG Cloud Computing Recommendations

NASA Astrophysics Data System (ADS)

de la Beaujardiere, J.; McInerney, M.; Frame, M. T.; Summers, C.

2017-12-01

The US Group on Earth Observations (USGEO) Data Management Working Group (DMWG) has been developing Cloud Computing Recommendations for Earth Observations. This inter-agency report is currently in draft form; DMWG hopes to have released the report as a public Request for Information (RFI) by the time of AGU. The recommendations are geared toward organizations that have already decided to use the Cloud for some of their activities (i.e., the focus is not on "why you should use the Cloud," but rather "If you plan to use the Cloud, consider these suggestions.") The report comprises Introductory Material, including Definitions, Potential Cloud Benefits, and Potential Cloud Disadvantages, followed by Recommendations in several areas: Assessing When to Use the Cloud, Transferring Data to the Cloud, Data and Metadata Contents, Developing Applications in the Cloud, Cost Minimization, Security Considerations, Monitoring and Metrics, Agency Support, and Earth Observations-specific recommendations. This talk will summarize the recommendations and invite comment on the RFI.

Cloud GIS Based Watershed Management

NASA Astrophysics Data System (ADS)

Bediroğlu, G.; Colak, H. E.

2017-11-01

In this study, we generated a Cloud GIS based watershed management system with using Cloud Computing architecture. Cloud GIS is used as SAAS (Software as a Service) and DAAS (Data as a Service). We applied GIS analysis on cloud in terms of testing SAAS and deployed GIS datasets on cloud in terms of DAAS. We used Hybrid cloud computing model in manner of using ready web based mapping services hosted on cloud (World Topology, Satellite Imageries). We uploaded to system after creating geodatabases including Hydrology (Rivers, Lakes), Soil Maps, Climate Maps, Rain Maps, Geology and Land Use. Watershed of study area has been determined on cloud using ready-hosted topology maps. After uploading all the datasets to systems, we have applied various GIS analysis and queries. Results shown that Cloud GIS technology brings velocity and efficiency for watershed management studies. Besides this, system can be easily implemented for similar land analysis and management studies.

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

PubMed Central

2012-01-01

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

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

PubMed

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

2012-01-01

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

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

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

Hasenkamp, Daren; Sim, Alexander; Wehner, Michael

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

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

NASA Astrophysics Data System (ADS)

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

2013-10-01

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

Evaluation of a Cloud Resolving Model Using TRMM Observations for Multiscale Modeling Applications

NASA Technical Reports Server (NTRS)

Posselt, Derek J.; L'Ecuyer, Tristan; Tao, Wei-Kuo; Hou, Arthur Y.; Stephens, Graeme L.

2007-01-01

The climate change simulation community is moving toward use of global cloud resolving models (CRMs), however, current computational resources are not sufficient to run global CRMs over the hundreds of years necessary to produce climate change estimates. As an intermediate step between conventional general circulation models (GCMs) and global CRMs, many climate analysis centers are embedding a CRM in each grid cell of a conventional GCM. These Multiscale Modeling Frameworks (MMFs) represent a theoretical advance over the use of conventional GCM cloud and convection parameterizations, but have been shown to exhibit an overproduction of precipitation in the tropics during the northern hemisphere summer. In this study, simulations of clouds, precipitation, and radiation over the South China Sea using the CRM component of the NASA Goddard MMF are evaluated using retrievals derived from the instruments aboard the Tropical Rainfall Measuring Mission (TRMM) satellite platform for a 46-day time period that spans 5 May - 20 June 1998. The NASA Goddard Cumulus Ensemble (GCE) model is forced with observed largescale forcing derived from soundings taken during the intensive observing period of the South China Sea Monsoon Experiment. It is found that the GCE configuration used in the NASA Goddard MMF responds too vigorously to the imposed large-scale forcing, accumulating too much moisture and producing too much cloud cover during convective phases, and overdrying the atmosphere and suppressing clouds during monsoon break periods. Sensitivity experiments reveal that changes to ice cloud microphysical parameters have a relatively large effect on simulated clouds, precipitation, and radiation, while changes to grid spacing and domain length have little effect on simulation results. The results motivate a more detailed and quantitative exploration of the sources and magnitude of the uncertainty associated with specified cloud microphysical parameters in the CRM components of MMFs.

Utilizing HDF4 File Content Maps for the Cloud

NASA Technical Reports Server (NTRS)

Lee, Hyokyung Joe

2016-01-01

We demonstrate a prototype study that HDF4 file content map can be used for efficiently organizing data in cloud object storage system to facilitate cloud computing. This approach can be extended to any binary data formats and to any existing big data analytics solution powered by cloud computing because HDF4 file content map project started as long term preservation of NASA data that doesn't require HDF4 APIs to access data.

Department of Defense Use of Commercial Cloud Computing Capabilities and Services

DTIC Science & Technology

2015-11-01

models (Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service ( SaaS )), and four deployment models (Public...NIST defines three main models for cloud computing: IaaS, PaaS, and SaaS . These models help differentiate the implementation responsibilities that fall...and SaaS . 3. Public, Private, Community, and Hybrid Clouds Cloud services come in different forms, depending on the customer’s specific needs

Survey on Security Issues in File Management in Cloud Computing Environment

NASA Astrophysics Data System (ADS)

Gupta, Udit

2015-06-01

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

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

PubMed

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

2015-01-01

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

Benefits of cloud computing for PACS and archiving.

PubMed

Koch, Patrick

2012-01-01

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

Estimating Cloud Cover

ERIC Educational Resources Information Center

Moseley, Christine

2007-01-01

The purpose of this activity was to help students understand the percentage of cloud cover and make more accurate cloud cover observations. Students estimated the percentage of cloud cover represented by simulated clouds and assigned a cloud cover classification to those simulations. (Contains 2 notes and 3 tables.)

Using In Situ Observations and Satellite Retrievals to Constrain Large-Eddy Simulations and Single-Column Simulations: Implications for Boundary-Layer Cloud Parameterization in the NASA GISS GCM

NASA Astrophysics Data System (ADS)

Remillard, J.

2015-12-01

Two low-cloud periods from the CAP-MBL deployment of the ARM Mobile Facility at the Azores are selected through a cluster analysis of ISCCP cloud property matrices, so as to represent two low-cloud weather states that the GISS GCM severely underpredicts not only in that region but also globally. The two cases represent (1) shallow cumulus clouds occurring in a cold-air outbreak behind a cold front, and (2) stratocumulus clouds occurring when the region was dominated by a high-pressure system. Observations and MERRA reanalysis are used to derive specifications used for large-eddy simulations (LES) and single-column model (SCM) simulations. The LES captures the major differences in horizontal structure between the two low-cloud fields, but there are unconstrained uncertainties in cloud microphysics and challenges in reproducing W-band Doppler radar moments. The SCM run on the vertical grid used for CMIP-5 runs of the GCM does a poor job of representing the shallow cumulus case and is unable to maintain an overcast deck in the stratocumulus case, providing some clues regarding problems with low-cloud representation in the GCM. SCM sensitivity tests with a finer vertical grid in the boundary layer show substantial improvement in the representation of cloud amount for both cases. GCM simulations with CMIP-5 versus finer vertical gridding in the boundary layer are compared with observations. The adoption of a two-moment cloud microphysics scheme in the GCM is also tested in this framework. The methodology followed in this study, with the process-based examination of different time and space scales in both models and observations, represents a prototype for GCM cloud parameterization improvements.

CloudMan as a platform for tool, data, and analysis distribution.

PubMed

Afgan, Enis; Chapman, Brad; Taylor, James

2012-11-27

Cloud computing provides an infrastructure that facilitates large scale computational analysis in a scalable, democratized fashion, However, in this context it is difficult to ensure sharing of an analysis environment and associated data in a scalable and precisely reproducible way. CloudMan (usecloudman.org) enables individual researchers to easily deploy, customize, and share their entire cloud analysis environment, including data, tools, and configurations. With the enabled customization and sharing of instances, CloudMan can be used as a platform for collaboration. The presented solution improves accessibility of cloud resources, tools, and data to the level of an individual researcher and contributes toward reproducibility and transparency of research solutions.

Using Amazon's Elastic Compute Cloud to dynamically scale CMS computational resources

NASA Astrophysics Data System (ADS)

Evans, D.; Fisk, I.; Holzman, B.; Melo, A.; Metson, S.; Pordes, R.; Sheldon, P.; Tiradani, A.

2011-12-01

Large international scientific collaborations such as the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider have traditionally addressed their data reduction and analysis needs by building and maintaining dedicated computational infrastructure. Emerging cloud computing services such as Amazon's Elastic Compute Cloud (EC2) offer short-term CPU and storage resources with costs based on usage. These services allow experiments to purchase computing resources as needed, without significant prior planning and without long term investments in facilities and their management. We have demonstrated that services such as EC2 can successfully be integrated into the production-computing model of CMS, and find that they work very well as worker nodes. The cost-structure and transient nature of EC2 services makes them inappropriate for some CMS production services and functions. We also found that the resources are not truely "on-demand" as limits and caps on usage are imposed. Our trial workflows allow us to make a cost comparison between EC2 resources and dedicated CMS resources at a University, and conclude that it is most cost effective to purchase dedicated resources for the "base-line" needs of experiments such as CMS. However, if the ability to use cloud computing resources is built into an experiment's software framework before demand requires their use, cloud computing resources make sense for bursting during times when spikes in usage are required.

Exploring the Strategies for a Community College Transition into a Cloud-Computing Environment

ERIC Educational Resources Information Center

DeBary, Narges

2017-01-01

The use of the Internet has resulted in the birth of an innovative virtualization technology called cloud computing. Virtualization can tremendously improve the instructional and operational systems of a community college. Although the incidental adoption of the cloud solutions in the community colleges of higher education has been increased,…

Cloud Computing E-Communication Services in the University Environment

ERIC Educational Resources Information Center

Babin, Ron; Halilovic, Branka

2017-01-01

The use of cloud computing services has grown dramatically in post-secondary institutions in the last decade. In particular, universities have been attracted to the low-cost and flexibility of acquiring cloud software services from Google, Microsoft and others, to implement e-mail, calendar and document management and other basic office software.…

Cloud Computing Technologies in Writing Class: Factors Influencing Students' Learning Experience

ERIC Educational Resources Information Center

Wang, Jenny

2017-01-01

The proposed interactive online group within the cloud computing technologies as a main contribution of this paper provides easy and simple access to the cloud-based Software as a Service (SaaS) system and delivers effective educational tools for students and teacher on after-class group writing assignment activities. Therefore, this study…

Relationship between Trustworthiness, Transparency, and Security in Cloud Computing Environments: A Regression Analysis

ERIC Educational Resources Information Center

Ibrahim, Sara

2017-01-01

The insider security threat causes new and dangerous dimensions in cloud computing. Those internal threats are originated from contractors or the business partners' input that have access to the systems. A study of trustworthiness and transparency might assist the organizations to monitor employees' activity more cautiously on cloud technologies…

Distributed Hydrologic Modeling Apps for Decision Support in the Cloud

NASA Astrophysics Data System (ADS)

Swain, N. R.; Latu, K.; Christiensen, S.; Jones, N.; Nelson, J.

2013-12-01

Advances in computation resources and greater availability of water resources data represent an untapped resource for addressing hydrologic uncertainties in water resources decision-making. The current practice of water authorities relies on empirical, lumped hydrologic models to estimate watershed response. These models are not capable of taking advantage of many of the spatial datasets that are now available. Physically-based, distributed hydrologic models are capable of using these data resources and providing better predictions through stochastic analysis. However, there exists a digital divide that discourages many science-minded decision makers from using distributed models. This divide can be spanned using a combination of existing web technologies. The purpose of this presentation is to present a cloud-based environment that will offer hydrologic modeling tools or 'apps' for decision support and the web technologies that have been selected to aid in its implementation. Compared to the more commonly used lumped-parameter models, distributed models, while being more intuitive, are still data intensive, computationally expensive, and difficult to modify for scenario exploration. However, web technologies such as web GIS, web services, and cloud computing have made the data more accessible, provided an inexpensive means of high-performance computing, and created an environment for developing user-friendly apps for distributed modeling. Since many water authorities are primarily interested in the scenario exploration exercises with hydrologic models, we are creating a toolkit that facilitates the development of a series of apps for manipulating existing distributed models. There are a number of hurdles that cloud-based hydrologic modeling developers face. One of these is how to work with the geospatial data inherent with this class of models in a web environment. Supporting geospatial data in a website is beyond the capabilities of standard web frameworks and it requires the use of additional software. In particular, there are at least three elements that are needed: a geospatially enabled database, a map server, and geoprocessing toolbox. We recommend a software stack for geospatial web application development comprising: MapServer, PostGIS, and 52 North with Python as the scripting language to tie them together. Another hurdle that must be cleared is managing the cloud-computing load. We are using HTCondor as a solution to this end. Finally, we are creating a scripting environment wherein developers will be able to create apps that use existing hydrologic models in our system with minimal effort. This capability will be accomplished by creating a plugin for a Python content management system called CKAN. We are currently developing cyberinfrastructure that utilizes this stack and greatly lowers the investment required to deploy cloud-based modeling apps. This material is based upon work supported by the National Science Foundation under Grant No. 1135482

Prediction Based Proactive Thermal Virtual Machine Scheduling in Green Clouds

PubMed Central

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. PMID:24737962

Fog Computing and Edge Computing Architectures for Processing Data From Diabetes Devices Connected to the Medical Internet of Things.

PubMed

Klonoff, David C

2017-07-01

The Internet of Things (IoT) is generating an immense volume of data. With cloud computing, medical sensor and actuator data can be stored and analyzed remotely by distributed servers. The results can then be delivered via the Internet. The number of devices in IoT includes such wireless diabetes devices as blood glucose monitors, continuous glucose monitors, insulin pens, insulin pumps, and closed-loop systems. The cloud model for data storage and analysis is increasingly unable to process the data avalanche, and processing is being pushed out to the edge of the network closer to where the data-generating devices are. Fog computing and edge computing are two architectures for data handling that can offload data from the cloud, process it nearby the patient, and transmit information machine-to-machine or machine-to-human in milliseconds or seconds. Sensor data can be processed near the sensing and actuating devices with fog computing (with local nodes) and with edge computing (within the sensing devices). Compared to cloud computing, fog computing and edge computing offer five advantages: (1) greater data transmission speed, (2) less dependence on limited bandwidths, (3) greater privacy and security, (4) greater control over data generated in foreign countries where laws may limit use or permit unwanted governmental access, and (5) lower costs because more sensor-derived data are used locally and less data are transmitted remotely. Connected diabetes devices almost all use fog computing or edge computing because diabetes patients require a very rapid response to sensor input and cannot tolerate delays for cloud computing.

A Practical Evaluation of a High-Security Energy-Efficient Gateway for IoT Fog Computing Applications

PubMed Central

Castedo, Luis

2017-01-01

Fog computing extends cloud computing to the edge of a network enabling new Internet of Things (IoT) applications and services, which may involve critical data that require privacy and security. In an IoT fog computing system, three elements can be distinguished: IoT nodes that collect data, the cloud, and interconnected IoT gateways that exchange messages with the IoT nodes and with the cloud. This article focuses on securing IoT gateways, which are assumed to be constrained in terms of computational resources, but that are able to offload some processing from the cloud and to reduce the latency in the responses to the IoT nodes. However, it is usually taken for granted that IoT gateways have direct access to the electrical grid, which is not always the case: in mission-critical applications like natural disaster relief or environmental monitoring, it is common to deploy IoT nodes and gateways in large areas where electricity comes from solar or wind energy that charge the batteries that power every device. In this article, how to secure IoT gateway communications while minimizing power consumption is analyzed. The throughput and power consumption of Rivest–Shamir–Adleman (RSA) and Elliptic Curve Cryptography (ECC) are considered, since they are really popular, but have not been thoroughly analyzed when applied to IoT scenarios. Moreover, the most widespread Transport Layer Security (TLS) cipher suites use RSA as the main public key-exchange algorithm, but the key sizes needed are not practical for most IoT devices and cannot be scaled to high security levels. In contrast, ECC represents a much lighter and scalable alternative. Thus, RSA and ECC are compared for equivalent security levels, and power consumption and data throughput are measured using a testbed of IoT gateways. The measurements obtained indicate that, in the specific fog computing scenario proposed, ECC is clearly a much better alternative than RSA, obtaining energy consumption reductions of up to 50% and a data throughput that doubles RSA in most scenarios. These conclusions are then corroborated by a frame temporal analysis of Ethernet packets. In addition, current data compression algorithms are evaluated, concluding that, when dealing with the small payloads related to IoT applications, they do not pay off in terms of real data throughput and power consumption. PMID:28850104

A Practical Evaluation of a High-Security Energy-Efficient Gateway for IoT Fog Computing Applications.

PubMed

Suárez-Albela, Manuel; Fernández-Caramés, Tiago M; Fraga-Lamas, Paula; Castedo, Luis

2017-08-29

Fog computing extends cloud computing to the edge of a network enabling new Internet of Things (IoT) applications and services, which may involve critical data that require privacy and security. In an IoT fog computing system, three elements can be distinguished: IoT nodes that collect data, the cloud, and interconnected IoT gateways that exchange messages with the IoT nodes and with the cloud. This article focuses on securing IoT gateways, which are assumed to be constrained in terms of computational resources, but that are able to offload some processing from the cloud and to reduce the latency in the responses to the IoT nodes. However, it is usually taken for granted that IoT gateways have direct access to the electrical grid, which is not always the case: in mission-critical applications like natural disaster relief or environmental monitoring, it is common to deploy IoT nodes and gateways in large areas where electricity comes from solar or wind energy that charge the batteries that power every device. In this article, how to secure IoT gateway communications while minimizing power consumption is analyzed. The throughput and power consumption of Rivest-Shamir-Adleman (RSA) and Elliptic Curve Cryptography (ECC) are considered, since they are really popular, but have not been thoroughly analyzed when applied to IoT scenarios. Moreover, the most widespread Transport Layer Security (TLS) cipher suites use RSA as the main public key-exchange algorithm, but the key sizes needed are not practical for most IoT devices and cannot be scaled to high security levels. In contrast, ECC represents a much lighter and scalable alternative. Thus, RSA and ECC are compared for equivalent security levels, and power consumption and data throughput are measured using a testbed of IoT gateways. The measurements obtained indicate that, in the specific fog computing scenario proposed, ECC is clearly a much better alternative than RSA, obtaining energy consumption reductions of up to 50% and a data throughput that doubles RSA in most scenarios. These conclusions are then corroborated by a frame temporal analysis of Ethernet packets. In addition, current data compression algorithms are evaluated, concluding that, when dealing with the small payloads related to IoT applications, they do not pay off in terms of real data throughput and power consumption.

Provenance based data integrity checking and verification in cloud environments

PubMed Central

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

2017-01-01

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

Provenance based data integrity checking and verification in cloud environments.

PubMed

Imran, Muhammad; Hlavacs, Helmut; Haq, Inam Ul; Jan, Bilal; Khan, Fakhri Alam; Ahmad, Awais

2017-01-01

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

Cloud based emergency health care information service in India.

PubMed

Karthikeyan, N; Sukanesh, R

2012-12-01

A hospital is a health care organization providing patient treatment by expert physicians, surgeons and equipments. A report from a health care accreditation group says that miscommunication between patients and health care providers is the reason for the gap in providing emergency medical care to people in need. In developing countries, illiteracy is the major key root for deaths resulting from uncertain diseases constituting a serious public health problem. Mentally affected, differently abled and unconscious patients can't communicate about their medical history to the medical practitioners. Also, Medical practitioners can't edit or view DICOM images instantly. Our aim is to provide palm vein pattern recognition based medical record retrieval system, using cloud computing for the above mentioned people. Distributed computing technology is coming in the new forms as Grid computing and Cloud computing. These new forms are assured to bring Information Technology (IT) as a service. In this paper, we have described how these new forms of distributed computing will be helpful for modern health care industries. Cloud Computing is germinating its benefit to industrial sectors especially in medical scenarios. In Cloud Computing, IT-related capabilities and resources are provided as services, via the distributed computing on-demand. This paper is concerned with sprouting software as a service (SaaS) by means of Cloud computing with an aim to bring emergency health care sector in an umbrella with physical secured patient records. In framing the emergency healthcare treatment, the crucial thing considered necessary to decide about patients is their previous health conduct records. Thus a ubiquitous access to appropriate records is essential. Palm vein pattern recognition promises a secured patient record access. Likewise our paper reveals an efficient means to view, edit or transfer the DICOM images instantly which was a challenging task for medical practitioners in the past years. We have developed two services for health care. 1. Cloud based Palm vein recognition system 2. Distributed Medical image processing tools for medical practitioners.

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

NASA Astrophysics Data System (ADS)

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

2015-07-01

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

Analysis and Research on Spatial Data Storage Model Based on Cloud Computing Platform

NASA Astrophysics Data System (ADS)

Hu, Yong

2017-12-01

In this paper, the data processing and storage characteristics of cloud computing are analyzed and studied. On this basis, a cloud computing data storage model based on BP neural network is proposed. In this data storage model, it can carry out the choice of server cluster according to the different attributes of the data, so as to complete the spatial data storage model with load balancing function, and have certain feasibility and application advantages.

The Role of Networks in Cloud Computing

NASA Astrophysics Data System (ADS)

Lin, Geng; Devine, Mac

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

On the Large-Scaling Issues of Cloud-based Applications for Earth Science Dat

NASA Astrophysics Data System (ADS)

Hua, H.

2016-12-01

Next generation science data systems are needed to address the incoming flood of data from new missions such as NASA's SWOT and NISAR where its SAR data volumes and data throughput rates are order of magnitude larger than present day missions. Existing missions, such as OCO-2, may also require high turn-around time for processing different science scenarios where on-premise and even traditional HPC computing environments may not meet the high processing needs. Additionally, traditional means of procuring hardware on-premise are already limited due to facilities capacity constraints for these new missions. Experiences have shown that to embrace efficient cloud computing approaches for large-scale science data systems requires more than just moving existing code to cloud environments. At large cloud scales, we need to deal with scaling and cost issues. We present our experiences on deploying multiple instances of our hybrid-cloud computing science data system (HySDS) to support large-scale processing of Earth Science data products. We will explore optimization approaches to getting best performance out of hybrid-cloud computing as well as common issues that will arise when dealing with large-scale computing. Novel approaches were utilized to do processing on Amazon's spot market, which can potentially offer 75%-90% costs savings but with an unpredictable computing environment based on market forces.

Visual Analysis of Cloud Computing Performance Using Behavioral Lines.

PubMed

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

2016-02-29

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

Cloud computing for energy management in smart grid - an application survey

NASA Astrophysics Data System (ADS)

Naveen, P.; Kiing Ing, Wong; Kobina Danquah, Michael; Sidhu, Amandeep S.; Abu-Siada, Ahmed

2016-03-01

The smart grid is the emerging energy system wherein the application of information technology, tools and techniques that make the grid run more efficiently. It possesses demand response capacity to help balance electrical consumption with supply. The challenges and opportunities of emerging and future smart grids can be addressed by cloud computing. To focus on these requirements, we provide an in-depth survey on different cloud computing applications for energy management in the smart grid architecture. In this survey, we present an outline of the current state of research on smart grid development. We also propose a model of cloud based economic power dispatch for smart grid.

Helix Nebula and CERN: A Symbiotic approach to exploiting commercial clouds

NASA Astrophysics Data System (ADS)

Barreiro Megino, Fernando H.; Jones, Robert; Kucharczyk, Katarzyna; Medrano Llamas, Ramón; van der Ster, Daniel

2014-06-01

The recent paradigm shift toward cloud computing in IT, and general interest in "Big Data" in particular, have demonstrated that the computing requirements of HEP are no longer globally unique. Indeed, the CERN IT department and LHC experiments have already made significant R&D investments in delivering and exploiting cloud computing resources. While a number of technical evaluations of interesting commercial offerings from global IT enterprises have been performed by various physics labs, further technical, security, sociological, and legal issues need to be address before their large-scale adoption by the research community can be envisaged. Helix Nebula - the Science Cloud is an initiative that explores these questions by joining the forces of three European research institutes (CERN, ESA and EMBL) with leading European commercial IT enterprises. The goals of Helix Nebula are to establish a cloud platform federating multiple commercial cloud providers, along with new business models, which can sustain the cloud marketplace for years to come. This contribution will summarize the participation of CERN in Helix Nebula. We will explain CERN's flagship use-case and the model used to integrate several cloud providers with an LHC experiment's workload management system. During the first proof of concept, this project contributed over 40.000 CPU-days of Monte Carlo production throughput to the ATLAS experiment with marginal manpower required. CERN's experience, together with that of ESA and EMBL, is providing a great insight into the cloud computing industry and highlighted several challenges that are being tackled in order to ease the export of the scientific workloads to the cloud environments.

Computer Education and Instructional Technology Teacher Trainees' Opinions about Cloud Computing Technology

ERIC Educational Resources Information Center

Karamete, Aysen

2015-01-01

This study aims to show the present conditions about the usage of cloud computing in the department of Computer Education and Instructional Technology (CEIT) amongst teacher trainees in School of Necatibey Education, Balikesir University, Turkey. In this study, a questionnaire with open-ended questions was used. 17 CEIT teacher trainees…

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

NASA Astrophysics Data System (ADS)

Mohan Kumar, M.; Vijayan, R.

2017-11-01

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

Dynamic Extension of a Virtualized Cluster by using Cloud Resources

NASA Astrophysics Data System (ADS)

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

2012-12-01

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

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

PubMed Central

Camargo, João; Rochol, Juergen; Gerla, Mario

2018-01-01

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

Signal and image processing algorithm performance in a virtual and elastic computing environment

NASA Astrophysics Data System (ADS)

Bennett, Kelly W.; Robertson, James

2013-05-01

The U.S. Army Research Laboratory (ARL) supports the development of classification, detection, tracking, and localization algorithms using multiple sensing modalities including acoustic, seismic, E-field, magnetic field, PIR, and visual and IR imaging. Multimodal sensors collect large amounts of data in support of algorithm development. The resulting large amount of data, and their associated high-performance computing needs, increases and challenges existing computing infrastructures. Purchasing computer power as a commodity using a Cloud service offers low-cost, pay-as-you-go pricing models, scalability, and elasticity that may provide solutions to develop and optimize algorithms without having to procure additional hardware and resources. This paper provides a detailed look at using a commercial cloud service provider, such as Amazon Web Services (AWS), to develop and deploy simple signal and image processing algorithms in a cloud and run the algorithms on a large set of data archived in the ARL Multimodal Signatures Database (MMSDB). Analytical results will provide performance comparisons with existing infrastructure. A discussion on using cloud computing with government data will discuss best security practices that exist within cloud services, such as AWS.