A New Approach for Optimizing Resource Provisioning In Cloud Computing Using OCRP Algorithm
The advice studies multiple provisioning stages with demand and price uncertainties. The elucidation methods based on Benders decomposition and sample-average approximation algorithms are used to solve the optimization formulation in an efficient was there is a common myth that a web application can scale up unlimitedly and automatically when application demand increases. Because web applications could have a dramatic difference between their peak load (such as during flash crowd) and their normal load, a traditional infrastructure is ill-suited for them. Uses either grossly over-provisioning for the potential peaks, thus wasting valuable capital, or provisioning low resources for the normal or over requirements alike, but not able to handle peak traffic when it does materialize. This system currently supports on-demand plans on pay per use basis. If the system is extended to support an automated self evolving cloud based on prior reservation plans, then it’s a very feasible beneficial solution for both cloud providers and cloud consumers. Using the elastic provisioning capability of a cloud, a cloud application can ideally provision its infrastructure dynamically based on cloud users requirements using optimal cloud resource provisioning (OCRP) algorithm. The OCRP algorithm is a derivative of stochastic programming model. Using in the multiple provisioning states the OCRP algorithm can furnish the computing resources has been used, as well as for the long plan terms e.g. quarter plan for 4 stages and yearly plan for 12 stages. The demand and price uncertainty is considered in OCRP and according to these variations adjusts the resources of a cloud. The optimal cloud resource provisioning algorithm is proposed for the virtual machine management. The decision of the OCRP algorithm as “the total cost of the resource furnished in the environment is minify by the cloud computing” by obtaining the honed statement of stochastic integer programming is suggested.
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.
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.
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.
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.
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.
GNU Linear Programming Kit (GLPK), http://www.gnu.org/ software/glpk, 2012.
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.
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