Advertisement

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
Article

Abstract

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.

Keywords

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

Notes

Acknowledgments

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)

References

  1. 1.
    Wang, L., Zhan, J., Shi, W.: In cloud, can scientific communities benefit from the economies of scale? TPDS 99, 1 (2011)Google Scholar
  2. 2.
    Wang, X.Y., et al.: Appliance-based autonomic provisioning framework for virtualized outsourcing data center. In: Proceedings of the Fourth International Conference on Autonomic Computing, p. 29 (2007).Google Scholar
  3. 3.
    Li, H., Groep, D., Wolters, L.: Efficient response time predictions by exploiting application and resource state similarities, In Proceedings of the 6th IEEE/ACM International Workshop on Grid Computing. IEEE Computer Society, pp. 234–241 (2005).Google Scholar
  4. 4.
    Urgaonkar, B., Shenoy, P,. and Roscoe, T.: Resource overbooking and application profiling in a shared Internet hosting platform. ACM Trans. Internet Technol. 9, 1, Article 1 (February 2009), pp. 45. 2009.Google Scholar
  5. 5.
    Raicu, I., Foster, I.T., and Yong Z.: Many-task computing for grids and supercomputers”, MTAGS 2008. In: Workshop on Many-Task Computing on Grids and Supercomputers, pp. 1–11 (2008).Google Scholar
  6. 6.
    Morris, G.M., Goodsell, D.S., Halliday, R.S., Huey, R., Hart, W.E., Belew, R.K., Olson, A.J.: Automated docking using a lamarckian genetic algorithm and and empirical binding free energy function. J. Comput. Chem. 19, 1639–1662 (1998)CrossRefGoogle Scholar
  7. 7.
    Alwall, J., Herquet, M., Maltoni, F., Mattelaer, O., Stelzer, T.: MadGraph 5: going beyond. J. High Energy Phys. 6, 1–40 (2011)Google Scholar
  8. 8.
    Rho, S., Kim, S., Kim, S., Kim, S., Kim, J.-S., and Hwang, S.: HTCaaS: a large-scale high-throughput computing by leveraging grids, supercomputers and cloud, In: Research Poster at IEEE/ACM International Conference for High Performance Computing, Networking, Storage and Analysis (SC’12), November (2012).Google Scholar
  9. 9.
    Jolliffe, I.T.: Principal Component Analysis (PCA), Springer Series in Statistics., 2nd edn. Springer-Verlag, New York (2002)Google Scholar
  10. 10.
    Amazon EC2 (Elastic Compute Cloud), http://aws.amazon.com/ec2. Accessed 12 April 2014
  11. 11.
    Flanagan Scientific Library, http://www.ee.ucl.ac.uk/~mflanaga/java/. Accessed 12 April 2014
  12. 12.
    DAS2-Grid, http://cs.vu.nl/das2. Accessed 12 April 2014
  13. 13.
    Grid Workload Archive (GWA), http://gwa.ewi.tudelft.nl/. Accessed 12 April 2014

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

Personalised recommendations