Optimization of Cost in Cloud Computing Using OCRP Algorithm

  ijett-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2013 by IJETT Journal
Volume-4 Issue-5                      
Year of Publication : 2013
Authors : A.Poobalan , V.Selvi

MLA 

A.Poobalan , V.Selvi. "Optimization of Cost in Cloud Computing Using OCRP Algorithm". International Journal of Engineering Trends and Technology (IJETT). V4(5):2105-2107 May 2013. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group.

Abstract

In cloud computing, cloud providers can offer cloud consumers two provisioning plans for computing resources, namely reservation and on - demand plans. Reservation plan is cheaper than that provisioned by on - demand plan, since cloud consumer has to pay to provider in advance. By using reservation plan, the consumer can reduce the total res ource provisioning cost. Even though , the best advance reservation of resources is difficult to be achieved due to uncertainty of consumer’s future demand and providers’ resource prices. Due to this problem, an optimal cloud resource provisioning (OCRP) algorithm is proposed by formulating a stochastic programming model. The OCRP algorithm can provision computing resources for being used in multiple provisioning stages as well as a long - term plan, e.g., four stages in a quarter plan and twelve stages in a yearly plan. The d emand and price uncertainty is considered in OCRP. In this paper, different approaches are measured including deterministic equivalent formulation, sample - average approximation, and Benders decomposition in OCRP algorithm.

References

[1] I. Foster, Y. Zhao, and S. Lu, “Cloud Computing and Grid Computing 360 - Degree Compared,” Proc. Grid Computing Environments Workshop (GCE ’08), 2008.
[2] S. Chaisiri, B.S. Lee, and D. Niyato, “Optimal Virtual Machine Placement across Multiple Cl oud Providers,” Proc. IEEE Asia - Pacific Services Computing Conf. (APSCC), 2009.
[3] F.V. Louveaux, “Stochastic Integer Programming,” Handbooks in OR & MS, vol. 10, pp. 213 - 266, 2003.
[4] A.J. Conejo, E. Castillo, and R. Garcı ́a - Bertrand, “Linear Programming: Comp licating Variables,” Decomposition Techniques in Mathematical Programming, chapter 3, pp. 107 - 139, Springer, 2006.
[5] L. Grit, D. Irwin, A. Yumerefendi, and J. Chase, “Virtual Machine Hosting for Networked Clusters: Building the Foundations for Autonomic Orch estration,” Proc. IEEE Int’l Workshop Virtualization Technology in Distributed Computing, 2006.
[6] H.N. Van, F.D. Tran, and J. - M. Menaud, “SLA - Aware Virtual Resource Management for Cloud Infrastructures,” Proc. IEEE Ninth Int’l Conf. Computer and Information Technology, 2009.
[7] M. Cardosa, M.R. Korupolu, and A. Singh, “Shares and Utilities Based Power Consolidation in Virtualized Server Environments,” Proc. IFIP/IEEE 11th Int’l Conf. Symp. Integrated Network Management (IM ’09), 2009.
[8] F. Hermenier, X. Lorca, and J. - M. Menaud, “Entropy: A Consolidation Manager for Clusters,” Proc. ACM SIGPLAN/ SIGOPS Int’l Conf. Virtual Execution Environments (VEE ’09), 2009.
[9] N. Bobroff, A. Kochut, and K. Beaty, “Dynamic Placement of Virtual Machines for Managing SLA Violations,” Proc. IFIP/IEEE Int’l Symp. Integrated Network Management (IM ’07), pp. 119 - 128, May 2007.
[10] P. Jirutitijaroen and C. Singh, “Reliability Constrained Multi - Area Adequacy Planning Using Stochastic Programming with Sample - Average Approximations,” IEEE Trans. Power Systems, vol. 23, no. 2, pp. 504 - 513, May 2008.
[11] GNU Linear Programming Kit (GLPK), ht tp://www.gnu.org/ software/glpk, 2012.
[12] J. Linderoth, A. Shapiro, and S. Wright, “The Empirical Behavior of Sampling Methods for Stochastic Programming,” Ann. Operational Research, vol. 142, no. 1, pp. 215 - 241, 2006

Keywords
Virtualization, virtual machine placement, Deterministic equivalent formulation, Sample - average approximation, Benders decomposition .