Cloud computing is now a most popular technology of the present generation. Energy efficiency is big aspect to think as the big data center is consuming a lot of energy to run and to serve their customers. Energy efficient algorithm and techniques are required to reduce the carbon emissions. In this paper we have worked for consolidation of Virtual Machine(VM) by detecting over-utilized hosts by using Pattern matching and reduced number of migrations by taking a new approach of Mode Absolute Deviation. It analyzes the historical data of CPU usages to search the usage pattern of CPU and finds the dynamic thresholds values for migration of virtual machine. The work has been carried out in CloudSim and the results in our work has been better than previous work[1] and we are able to save energy and reduce the number of migrations by using our proposed method.

Energy Efficient, host overloading, VM Consolidation, VM Migration, Mode, Cloud Computing

[1] O. Sharma and H. Saini, “VM Consolidation for Cloud Data Center Using Median Based Threshold Approach,” Procedia Comput. Sci., vol. 89, pp. 27–33, 2016.
[2] P. Barham, B. Dragovic, K. Fraser, S. Hand, T. Harris, A. Ho, R. Neugebauer, I. Pratt, and A. Warfield, “Xen and the art of virtualization,” ACM SIGOPS Oper. Syst. Rev., vol. 37, no. 5, p. 164, 2003.
[3] T. Guerout, T. Monteil, G. Da Costa, R. Neves Calheiros, R. Buyya, and M. Alexandru, “Energy-aware simulation with DVFS,” Simul. Model. Pract. Theory, vol. 39, pp. 76–91, 2013.
[4] C. Clark, K. Fraser, S. Hand, J. G. Hansen, E. Jul, C. Limpach, I. Pratt, and A. Warfield, “Live migration of virtual machines,” NSDI’05 Proc. 2nd Conf. Symp. Networked Syst. Des. Implement., no. Vmm, pp. 273–286, 2005.
[5] W. Voorsluys, J. Broberg, S. Venugopal, and R. Buyya, “Cost of virtual machine live migration in clouds: A performance evaluation,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 5931 LNCS, pp. 254–265, 2009.
[6] E. Pinheiro and R. Bianchini, “Load balancing and unbalancing for power and performance in cluster-based systems,” … Syst. Low Power, pp. 1–8, 2001.
[7] J. S. Chase, D. C. Anderson, P. N. Thakar, A. M. Vahdat, and R. P. Doyle, “Managing energy and server resources in hosting centers,” Acm Sigops, vol. 35, no. 5, p. 103, 2001.
[8] D. Kusic, J. O. Kephart, J. E. Hanson, N. Kandasamy, and G. Jiang, “Power and Performance Management of Virtualized Computing Environments Via Lookahead Control,” 2008 Int. Conf. Auton. Comput., pp. 3–12, 2008.
[9] J. Luo, X. Li, and M. Chen, “Hybrid shuffled frog leaping algorithm for energy-efficient dynamic consolidation of virtual machines in cloud data centers,” Expert Syst. Appl., vol. 41, no. 13, pp. 5804–5816, 2014.
[10] M. Forsman, A. Glad, L. Lundberg, and D. Ilie, “Algorithms for automated live migration of virtual machines,” J. Syst. Softw., vol. 101, pp. 110–126, 2015.
[11] W. Song, Z. Xiao, Q. Chen, and H. Luo, “Adaptive resource provisioning for the cloud using online bin packing - Wagner,” Comput. IEEE Trans., vol. X, no. X, pp. 1–14, 2013.
[12] G. Han, W. Que, G. Jia, and L. Shu, “An efficient virtual machine consolidation scheme for multimedia cloud computing,” Sensors (Switzerland), vol. 16, no. 2, pp. 1–17, 2016.
[13] R. Buyya, A. Beloglazov, and J. Abawajy, “Energy-Efficient Management of Data Center Resources for Cloud Computing : A Vision , Architectural Elements , and Open Challenges Clou d Computing and D istributed S ystems ( CLOUDS ) Laboratory Department of Computer Science and Software Engineering The,” Univ. Melbourne, Aust., no. Vm, pp. 1–12, 2010.
[14] A. Beloglazov, J. Abawajy, and R. Buyya, “Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing,” Futur. Gener. Comput. Syst., 2012.
[15] A. Beloglazov and R. Buyya, “Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers,” Concurr. Comput. Pract. Exp., vol. 24, no. 13, pp. 1397–1420, 2012.
[16] A. Khajeh-Hosseini, D. Greenwood, J. Smith, and I. Sommerville, “The Cloud Adoption Toolkit: supporting cloud adoption decisions in the enterprise,” Softw. - Pract. Exp., vol. 43, no. 4, pp. 447–465, 2012.
[17] K. Park and V. S. Pai, “CoMon: a mostly-scalable monitoring system for PlanetLab,” ACM SIGOPS Oper. Syst. Rev., vol. 40, no. 1, pp. 65–74, 2006