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A Science Cloud Resource Provisioning Model Using Statistical Analysis of Job History

Authors :
Chongam Kim
Yoonhee Kim
Seoyoung Kim
Jung-in Koh
Source :
DASC
Publication Year :
2011
Publisher :
IEEE, 2011.

Abstract

The advent of cloud computing makes scientists to extend their research environments over supercomputers to on-demand and dynamically scalable resources. Science cloud becomes a trend in various scientific domains these days. However, it is difficult to provide optimal job execution environment rapidly and dynamically depending on user's demands. Therefore, it is very important to predict user's requirements and to prepare execution environment in advance. In addition, it needs scheduling mechanisms for virtual machines to provide some level of guaranteed performance of a user application. In this paper, we propose a cloud resource provisioning model using statistical analysis of job history. In this model, we use job history which is generated from many application executions and identifies characteristics of an application by applying statistical analysis. We utilize a statistical technique, PCA (Principal Component Analysis), to analyze execution history of applications and to extract the factors which contribute much to execution time. The effective factors are used for selecting reference job profile and then VM is deployed on the selected node based on the reference profile. An application is executed on chosen nodes and its performance result is incorporated into job history with the purpose of evaluating profile's credit. As a result, this model can provide efficient management of cloud resource for a service provider and reduce management overhead on cloud.

Details

Database :
OpenAIRE
Journal :
2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing
Accession number :
edsair.doi...........248e6f716c8399f8b04936d5f9e60aed
Full Text :
https://doi.org/10.1109/dasc.2011.134