Cluster Computing

, Volume 17, Issue 4, pp 1157–1169 | Cite as

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

  • Seoyoung Kim
  • Jik-Soo Kim
  • Soonwook Hwang
  • Yoonhee KimEmail author


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.


Science cloud High-throughput computing Job profiling Cloud provisioning PCA (Principal components analysis) 



S.Y Kim thanks S.-h. Nam for useful comments and supports. This research was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2013R1A1A3007866)


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Seoyoung Kim
    • 1
  • Jik-Soo Kim
    • 1
  • Soonwook Hwang
    • 1
  • Yoonhee Kim
    • 2
    Email author
  1. 1.National Institute of Supercomputing and Networking, KISTIDaejeon Korea
  2. 2.Department of Computer ScienceSookmyung Women’s UniversitySeoul Korea

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