Resource Provisioning Cost of Cloud Computing by Adaptive Reservation Techniques
 | International Journal of Computer Trends and Technology (IJCTT) |  |
| © - May Issue 2013 by IJCTT Journal | ||
| Volume-4 Issue-5 | ||
| Year of Publication : 2013 | ||
| Authors :M.Manikandaprabhu, R.SivaSenthil |
M.Manikandaprabhu, R.SivaSenthil "Resource Provisioning Cost of Cloud Computing by Adaptive Reservation Techniques"International Journal of Computer Trends and Technology (IJCTT),V4(5):1118-1124 May Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: - In this paper presents the Cloud providers to provide cloud consumers for two provisioning plans are On-Demand plan and Reservation plans. Because it provides users an efficient way to allocate computing resources are dynamically to meet demands. Normally, cost of utilizing computing resources provisioned by on-demand plan is higher than reservation plan. Because reservation plan can provide offer of consumer can reduce the total resource provisioning cost. It can be achieved in Uncertainty of consumer’s future demand and provider’s resource prices. To control the cloud resources adaptively based on the reservation technique for under over provisioning (RTUOP) algorithm. The RTUOP algorithm is used to multi provisioning stages of long-term plan. The OCRP mainly considered in the demand and price uncertainty. The solutions of the RTUOP algorithm are considered including benders decomposition deterministic equivalent formulation and stochastic integer programming. To overcome this problem to applied by the scenario reduction techniques (SRT) to reduce the number of scenarios and successfully minimize total cost of resource provisioning in cloud environments.
References-
[1] K. Beaty, N. Bobroff, and A. Kochut, “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.
[2] E. Castillo, A.J. Conejo and R. Garcia-Bertrand, “Linear Programming: Complicating Variables, Decomposition Techniques in Mathematical Programming”, chapter 3, pp. 107-139, Springer, 2006.
[3] S. Chaisiri, B.S. Lee, and D. Niyato, “Optimal Virtual Machine Placement across Multiple Cloud Providers,” Proc. IEEE Asia- Pacific Services Computing Conf. (APSCC), 2009.
[4] J. Chen, G. Soundararajan, and C. Amza, “Autonomic Provisioning of Backend Databases in Dynamic Content Web Servers,” Proc. IEEE Int’l Conf. Autonomic Computing, 2006.
[5] G.B. Dantzig and G. Infangerm, “Large-Scale Stochastic Linear Programs: Importance Sampling and Benders Decomposition,” Proc. IMACS World Congress on Computation and Applied Math., 1991.
[6] E. Deelman and G.Juve, “Resource Provisioning Options for Large-Scale Scientific Workflows,” Proc. IEEE Fourth Int’l Conf. e-Science, 2008.
[7] H. Heitsch and W. Romisch, “Scenario Reduction Algorithms in Stochastic Programming,” J. Computational Optimization and Applications, vol. 24, pp. 187-206, 2003.
Keywords — Cloud computing, resource provisioning, virtual machine, stochastic programming, scenario reduction technique