An Energy Efficient VM Placement Technique for Cloud Datacenters

Deepa.R, Kalasalingam University; R.Kanniga Devi ,

Distributed Generation, Distribution System, Voltage Stability, Particle Swarm Optimization

The use of cloud computing technology has been an important aspect in many applications in recent time. The cloud provides scalable and on-demand services to the customers and keeps the customer information and data secure. The overall performance of the cloud depends on the reliability, availability and standard of the resources. Even though the cloud provides many usefulness it faces many issues and challenges that needs to be handled. One such challenge is the efficient resource allocation to minimize the overall energy consumption. In this paper an efficient VM placement and allocation strategy is proposed for cloud datacenters to reduce the cost incurred for using the resources within the cloud datacenters. The proposed technique is implemented in the CloudSim toolkit where a simulation is done using sample cloud datacenters. The experimented results are noted down.
    [1] BhushanLalSahu, Rajesh Tiwari; “A Comprehensive Study on Cloud Computing”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 9, September 2012. [2] David C. Chou; “Cloud Computing: A Value Creation Model”, ELSEVIER, Journal of Computer Standards and Interfaces, Feb 2015. [3] AbdelzahirAbdelmaboud, Dayang N.A. Jawawi, Imran Ghani, AbubakarElsafi, Barbara Kitchenham; “Quality of Service Approaches in Cloud Computing: A Systematic Mapping Study”, ELSEVIER, Journal of Systems and Software, Volume 101, March 2015. [4] DimitriosZissis, DimitriosLekkas; “Addressing Cloud Computing Security Issues”, Journal of Future Generation Computer Systems, ELSEVIER, Volume 28, Issue 3, March 2012. [5] D.Borgetto, P.Stolf; "An energy efficient approach to virtual machines management in cloud computing", IEEE 3rd International Conference on Cloud Networking, pp. 229-235, October 2014. [6] Microsoft, “Windows azure: Microsoft’s cloud services platform,” http://www.microsoft.com/windowsazure/. [7] N.Kord, H.Haghighi; "An energy-efficient approach for virtual machine placement in cloud based data centres", IEEE International Conference on Information and Knowledge Technology, pp. 44-49, May 2013. [8] Jian Cao, Yihua Wu, Minglu Li; "Energy Efficient Allocation of Virtual Machines in Cloud Computing Environments Based on Demand Forecast", Journal of Advanced in Grid and Pervasive Computing, Springer, pp. 137-151, May 2012. [9] Shuo Fang, R.Kanagavelu, Bu-Sung Lee, ChuanHengFoh, KhinMiMiAung; "Power-Efficient Virtual Machine Placement and Migration in Data Centers", IEEE International Conference on IEEE Cyber, Physical and Social Computing, pp. 1408-1413, August 2013. [10] C.Mastroianni, M.Meo, G.Papuzzo; "Probabilistic Consolidation of Virtual Machines in Self-Organizing Cloud Data Centers", IEEE Transactions on Cloud Computing, Volume 1, pp. 215-228, Feb 2014. [11] F.Farahnakian, P.Lilijeberg, J.Plosila; "Energy-Efficient Virtual Machines Consolidation in Cloud Data Centers Using Reinforcement Learning", IEEE 22nd Euromicro International Conference on Parallel, Distributed and Network-based Processing, pp. 500-507, August 2014. [12] Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov, Cesar A.F. De Rose, RajkumarBuyya; “CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource and Provisioning Algorithms”, Journal of Software Practice and Experience, Wiley Online Library, Volume 41, 2010, pp. 23-50.
Paper ID: GRDCF002042
Published in: Conference : International Conference on Innovations in Engineering and Technology (ICIET - 2016)
Page(s): 198 - 207