Towards effective science cloud provisioning for a large-scale high-throughput computing

The science cloud paradigm has been actively developed and investigated, but still requires a suitable model for science cloud system in order to support increasing scientific computation needs with high performance. This paper presents an effective provisioning model of science cloud, particularly...

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Bibliographic Details
Published in:Cluster computing 2014-12, Vol.17 (4), p.1157-1169
Main Authors: Kim, Seoyoung, Kim, Jik-Soo, Hwang, Soonwook, Kim, Yoonhee
Format: Article
Language:English
Subjects:
Computation
Computer Communication Networks
Computer Science
Employment
Genetic algorithms
Operating Systems
Processor Architectures
Proteins
Provisioning
Science
Scientists
Statistical analysis
Statistical methods
Workloads
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Summary:The science cloud paradigm has been actively developed and investigated, but still requires a suitable model for science cloud system in order to support increasing scientific computation needs with high performance. This paper presents an effective provisioning model of science cloud, particularly for large-scale high throughput computing applications. In this model, we utilize job traces where a statistical method is applied to pick the most influential features to improve application performance. With these features, a system determines where VM is deployed (allocation) and which instance type is proper (provisioning). An adaptive evaluation step which is subsequent to the job execution enables our model to adapt to dynamical computing environments. We show performance achievements by comparing the proposed model with other policies through experiments and expect noticeable improvements on performance as well as reduction of cost from resource consumption through our model.
ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-014-0371-2