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Self-service infrastructure container for data intensive application

Musa, IK and Walker, SD and Owen, AM and Harrison, AP (2014) 'Self-service infrastructure container for data intensive application.' Journal of Cloud Computing, 3 (1). ISSN 2192-113X

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Abstract

© 2014, Musa et al.; licensee Springer. 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.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Faculty of Science and Health > Mathematical Sciences, Department of
Depositing User: Jim Jamieson
Date Deposited: 12 Nov 2014 13:57
Last Modified: 17 Aug 2017 17:45
URI: http://repository.essex.ac.uk/id/eprint/11529

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