Energy Optimization in Cloud Computing by EGC Algorithm

A. Gokul, Anna university Regional Campus Madurai; E. Shanmuga priya ,Anna university Regional Campus Madurai

Cloud Computing, Energy Efficient, Power Saving, Response Time

Now a days, Cloud computing is an emerging technology, which is a new society for providing remote computing resources through a network. Cloud providers have many problems like getting an energy-efficiency control and satisfying a performance guarantee in cloud. In this paper, we implement three power-saving policies in cloud systems in order to reduce server idle power. To get optimize operational cost within a performance guarantee; we study the challenges of controlling service rates and applying the N-policy. Here we develop a cost function that includes the costs of power consumption, server startups and system congestion. Here we explain the operating modes, incurred costs and the effect of energy-efficiency controls on response times. To minimize cost within a response time under varying arrival rate by finding the optimal service rate and mode-switching restriction is our objectives. We propose an algorithm called Efficient Green Control (EGC) algorithm which is developed for solving optimization problems and making costs or performances tradeoffs in systems with different power-saving policies. By applying the power-saving policies combined with the proposed algorithm, we get our result that the benefits of reducing operational costs and improving response time. Cloud Computing, Energy Efficient, Power Saving, Response Time
    [1] G. Wang and T. E. Ng, “The impact of virtualization on network performance of amazon ec2 data center,” in Proc. IEEE Proc. INFOCOM, 2010, pp. 1–9. [2] R. Ranjan, L. Zhao, X. Wu, A. Liu, A. Quiroz, and M. Parashar, “Peer-to-peer cloud provisioning: Service discovery and load-balancing,”in Cloud Computing. London, U.K.: Springer, 2010, pp. 195–217. [3] R. N. Calheiros, R. Ranjan, and R. Buyya, “Virtual machine provisioning based on analytical performance and QoS in cloud computing environments,” in Proc. Int. Conf. Parallel Process., 2011, pp. 295–304. [4] Server virtualization has stalled, despite the hype [Online]. Available:, 2010. [5] Y. C. Lee and A. Y. Zomaya, “Energy efficient utilization of resources in cloud computing systems,” J. Supercomput., vol. 60, no. 2, pp. 268–280, 2012. [6] Beloglazov, R. Buyya, Y. C. Lee, and A. Zomaya, “A taxonomy and survey of energy-efficient data centers and cloud computing systems,” Adv. Comput., vol. 82, pp. 47–111, 2011. [7] R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, “Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility,” Future Generation Comput. Syst., vol. 25, no. 6, pp. 599–616, 2009. [8] L. Wang, G. Von Laszewski, A. Younge, X. He, M. Kunze, J. Tao, and C. Fu, “Cloud computing: A perspective study,” New Generation Comput., vol. 28, no. 2, pp. 137–146, 2010. [9] R. Ranjan, R. Buyya, and M. Parashar, “Special section on autonomic cloud computing: Technologies, services, and applications,” Concurrency Comput.: Practice Exp., vol. 24, no. 9, pp. 935–937, 2012. [10] M. Yadin and P. Naor, “Queueing systems with a removable service station,” Operations Res., vol . 14, pp. 393–405, 1963.
Paper ID: GRDCF002002
Published in: Conference : International Conference on Innovations in Engineering and Technology (ICIET - 2016)
Page(s): 10 - 12