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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Wang, L., Zhan, J., Shi, W.: In cloud, can scientific communities benefit from the economies of scale? TPDS 99, 1 (2011)
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).
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).
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.
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).
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)
Alwall, J., Herquet, M., Maltoni, F., Mattelaer, O., Stelzer, T.: MadGraph 5: going beyond. J. High Energy Phys. 6, 1–40 (2011)
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).
Jolliffe, I.T.: Principal Component Analysis (PCA), Springer Series in Statistics., 2nd edn. Springer-Verlag, New York (2002)
Amazon EC2 (Elastic Compute Cloud), http://aws.amazon.com/ec2. Accessed 12 April 2014
Flanagan Scientific Library, http://www.ee.ucl.ac.uk/~mflanaga/java/. Accessed 12 April 2014
DAS2-Grid, http://cs.vu.nl/das2. Accessed 12 April 2014
Grid Workload Archive (GWA), http://gwa.ewi.tudelft.nl/. Accessed 12 April 2014
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)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kim, S., Kim, JS., Hwang, S. et al. Towards effective science cloud provisioning for a large-scale high-throughput computing. Cluster Comput 17, 1157–1169 (2014). https://doi.org/10.1007/s10586-014-0371-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-014-0371-2