Optimizing the Cost for Resource Subscription Policy in IaaS Cloud

  ijett-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2013 by IJETT Journal
Volume-6 Number-6
Year of Publication : 2013
Authors : Ms.M.Uthaya Banu , Mr.K.Saravanan

Citation 

Ms.M.Uthaya Banu , Mr.K.Saravanan. "Optimizing the Cost for Resource Subscription Policy in IaaS Cloud". International Journal of Engineering Trends and Technology (IJETT). V6(6):296-301 Dec 2013. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract

Cloud computing allow the users to efficiently and dynamically provision computing resource to meet their IT needs. Cloud Provider offers two subscription plan to the customer namely reservation and on-demand. The reservation plan is typically cheaper than on-demand plan. If the actual computing demand is known in advance reserving the resource would be straightforward. The challenge is how to make properly resource provisioning and how the customers efficiently purchase the provisioning options under reservation and on-demand. To address this issue, two-phase algorithm are proposed to minimize service provision cost in both reservation and on-demand plan. To reserve the correct and optimal amount of resources during reservation, proposed a mathematical formulae in the first phase. To predict resource demand, use kalman filter in the second phase. The evaluation result shows that the two-phase algorithm can significantly reduce the provision cost and the prediction is of reasonable accuracy.

References

[1] Ren-Hung Hwang, Chung-Nan Lee, Yi-Ru Chen and Da-Jing Zhang-Jian,”Cost Optimization of Elasticity Cloud Resource Subscription Policy”,in IEEE Transaction on Service Computing,18 June 2013.
[2] K. Tsakalozos, H. Kllapi, E. Sitaridi, M. Roussopoulos, D. Paparas, and A. Delis, “Flexible Use of Cloud Resources through Profit Maximization and Price Discrimination,” in Proc. 27th IEEE International Conference on Data Engineering (ICDE 2011), April 2011, pp. 75-86.
[3] Y. Hu, J. Wong, G. Iszlai, and M. Litoiu, “Resource Provisioning for Cloud Computing,” in Proc. Conference of the Center for Advanced Studies on Collaborative Research, 2009, pp. 101-111
[4] R.N. Calheiros, R. Ranjan, and R. Buyya, “Virtual Machine Provisioning Based on Analytical Performance and QoS in Cloud Computing Environments,” in Proc. International Conference on Parallel Processing, Sept. 2011, pp. 295-304.
[5] M. Mao, J. Li and M. Humphrey, “Cloud Auto-Scaling with Deadline and Budget Constraints,” in Proc. 11th ACM/IEEE International Conference on Grid Computing (Grid 2010), 2010, pp. 41-48.
[6] S. Chaisiri, R. Kaewpuang, B. S. Lee, and D. Niyato, “Cost Minimization for Provisioning Virtual Servers in Amazon Elastic Compute Cloud,” in Proc. International Symposium on Modeling, Analysis and Simulation, of Computer and Telecommunication Systems (MASCOT), July 2011, pp. 85-95.
[7] C.C.T. Mark, D. Niyato, and C. Tham, “Evolutionary Optimal Virtual Machine Placement and Demand Forecaster for Cloud Computing,” in Proc. IEEE International Conference on Advanced Information Networking and Applications (AINA), 2011, pp. 348-355.
[8] S. Islam, J. Keung, K. Lee, and A. Liu, “An Empirical Study into Adaptive Resource Provisioning in the Cloud,” Future Generation Computer Systems, vol. 28, pp. 155-162, Jan.2012.
[9] E. Caron, F. Desprez, and A. Muresan, “Forecasting for Grid and Cloud Computing OnDemand Resources Based on Pattern Matching,” in Proc. Cloud Computing Technology and Science (CloudCom), 2010, pp. 456 -463.
[10] G. Welch and G. Bishop, “An Introduction to the Kalman Filter,” University of North Carolina at Chapel Hill, Chapel Hill, NC, 1995.

References
Pricing and Resource Allocation, Prediction.