SciCloud: Scientific Computing on the Cloud

Srirama, Satish Narayana; Batrashev, Oleg; Vainikko, Eero

doi:10.1109/ccgrid.2010.56

Cited by 47 publications

(22 citation statements)

References 3 publications

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“…Mobile Cloud middleware consists of push notification and mobile host technologies for processing remote executions. Mobile host technology [6] provides asynchronous mechanisms. And also this mechanism has a certain limitation and constraints for processing a certain platform.…”

Section: Mobile Cloud Middlewarementioning

confidence: 99%

Smarter Way to Access Multiple Mobile Cloud Applications without Interoperability Issues

Thanapal¹,

Marimuthub²,

Rajkumar³

et al. 2017

IJWMT

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As we are in the world of emerging technologies people have more awareness about it especially Smartphone's. Usage of Smartphone's has been increased tremendously. Different operating systems (OS) are used in smart phones such as MAC OS, Android, and Windows OS etc. Most of the applications are run on their appropriate platform. Cloud is the simplest way to run certain application over cross platforms. Many cloud vendors are there who provides several kinds of applications and software which can be accessed by the users. In that Application Programming Interfaces (APIs) helps us to create a cloud application over the market. However there will be chances to occur Interoperability issues while users were working on a cross platforms. In order to overcome this issue a framework has been proposed named Mobile Cloud Middleware (MCM). MCM will recover the interoperability issues over multiple cloud services on mobile devices. Here in this paper we proposed effective result to solve the Interoperability issue by integrating Mobile Enterprise with MCM.

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Section: Mobile Cloud Middlewarementioning

confidence: 99%

Smarter Way to Access Multiple Mobile Cloud Applications without Interoperability Issues

Thanapal¹,

Marimuthub²,

Rajkumar³

et al. 2017

IJWMT

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“…The Kuu and Katel clusters are physically located at different places, and are connected by a 100 Mbps Ethernet connection. While several applications are obvious from such a private cloud setup, we have used it in solving some of our research problems in distributed computing and mobile web services domains [36]. In the mobile web services domain, we scaled our Mobile Enterprise [37] to the loads possible in cellular networks.…”

Section: Scicloudmentioning

confidence: 99%

“…The main goal of the scientific computing cloud (SciCloud) project [36] is to study the scope of establishing private clouds at universities. With these clouds, students and researchers can efficiently use the already existing resources of university computer networks, in solving computationally intensive scientific, mathematical and academic problems.…”

Section: Scicloudmentioning

confidence: 99%

“…To analyze the cost of science on the clouds, Scientific Computing Cloud [36] (SciCloud) project has been initiated at the University of Tartu. The goal of the project is to study the scope of establishing private clouds at universities, so that the initial experimental costs can be reduced and one can efficiently use the already existing university cluster resources.…”

Section: Introductionmentioning

confidence: 99%

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Scalability of Parallel Scientific Applications on the Cloud

Srirama¹,

Batrashev

Jakovits

et al. 2011

Scientific Programming

Self Cite

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Abstract. Cloud computing, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. To study the effects of moving parallel scientific applications onto the cloud, we deployed several benchmark applications like matrix-vector operations and NAS parallel benchmarks, and DOUG (Domain decomposition On Unstructured Grids) on the cloud. DOUG is an open source software package for parallel iterative solution of very large sparse systems of linear equations. The detailed analysis of DOUG on the cloud showed that parallel applications benefit a lot and scale reasonable on the cloud. We could also observe the limitations of the cloud and its comparison with cluster in terms of performance. However, for efficiently running the scientific applications on the cloud infrastructure, the applications must be reduced to frameworks that can successfully exploit the cloud resources, like the MapReduce framework. Several iterative and embarrassingly parallel algorithms are reduced to the MapReduce model and their performance is measured and analyzed. The analysis showed that Hadoop MapReduce has significant problems with iterative methods, while it suits well for embarrassingly parallel algorithms. Scientific computing often uses iterative methods to solve large problems. Thus, for scientific computing on the cloud, this paper raises the necessity for better frameworks or optimizations for MapReduce.

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“…The model allows the use of configurable resources on a pay-as-you-go basis, thereby eliminating upfront investments. Scientific clouds [2] deployed on large heterogeneous research projects have shown good performance in handling burgeoning data volumes in an economic and efficient manner [3].…”

Section: Introductionmentioning

confidence: 99%

Self-service infrastructure container for data intensive application

Musa

Walker

Owen

et al. 2014

Journal of Cloud Computing: Advances, Systems and Applications

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Cloud based scientific data management -storage, transfer, analysis, and inference extraction -is attracting interest. In this paper, we propose a next generation cloud deployment model suitable for data intensive applications. Our model is a flexible and self-service container-based infrastructure that delivers -network, computing, and storage resources together with the logic to dynamically manage the components in a holistic manner. We demonstrate the strength of our model with a bioinformatics application. Dynamic algorithms for resource provisioning and job allocation suitable for the chosen dataset are packaged and delivered in a privileged virtual machine as part of the container. We tested the model on our private internal experimental cloud that is built on low-cost commodity hardware. We demonstrate the capability of our model to create the required network and computing resources and allocate submitted jobs. The results obtained shows the benefits of increased automation in terms of both a significant improvement in the time to complete a data analysis and a reduction in the cost of analysis. The algorithms proposed reduced the cost of performing analysis by 50% at 15 GB of data analysis. The total time between submitting a job and writing the results after analysis also reduced by more than 1 hr at 15 GB of data analysis.

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SciCloud: Scientific Computing on the Cloud

Cited by 47 publications

References 3 publications

Smarter Way to Access Multiple Mobile Cloud Applications without Interoperability Issues

Smarter Way to Access Multiple Mobile Cloud Applications without Interoperability Issues

Scalability of Parallel Scientific Applications on the Cloud

Self-service infrastructure container for data intensive application

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