Cloud Computing is one of the most emerging field for research now a days. The cloud is responsible to provide various set of services to users which requires a lot of energy. As they are growing up with a rapid rate, the burden on cloud is increasing daily. Various researchers are working on cloud efficiency with major factor as energy efficiency. As energy efficiency will not only increase user handling rate but also decrease overall global cost and pollution. In this paper, various previous techniques used for energy efficiency are discussed on the basis various performance parameters to analyse the best available techniques.

Cloud Computing, Machine Learning, Deep Learning, Convolutional neural networks (CNN)

[1] A. Greenberg, J. Hamilton, D. A. Maltz, and P. Patel, “The cost of a cloud: research problems in data center networks,” ACM SIGCOMM computer communication review, vol. 39, no. 1, pp. 68–73, 2008.
[2] T. Heath, B. Diniz, E. V. Carrera, W. Meira Jr, and R. Bianchini, “Energy conservation in heterogeneous server clusters,” in Proceedings of the tenth ACM SIGPLAN symposium on Principles and practice of parallel programming. ACM, 2005, pp. 186–195.
[3] G. Chen, W. He, J. Liu, S. Nath, L. Rigas, L. Xiao, and F. Zhao, “Energyaware server provisioning and load dispatching for connection-intensive internet services.” in NSDI, vol. 8, 2008, pp. 337–350.
[4] R. Urgaonkar, U. C. Kozat, K. Igarashi, and M. J. Neely, “Dynamic resource allocation and power management in virtualized data centers,” in Network Operations and Management Symposium (NOMS), 2010 IEEE. IEEE, 2010, pp. 479–486.
[5] A. Beloglazov and R. Buyya, “Energy efficient resource management in virtualized cloud data centers,” in Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing. IEEE Computer Society, 2010, pp. 826–831.
[6] A. Beloglazov, J. Abawajy, and R. Buyya, “Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing,” Future generation computer systems, vol. 28, no. 5, pp. 755–768, 2012.
[7] A. Gandhi, Y. Chen, D. Gmach, M. Arlitt, and M. Marwah, “Minimizing data center sla violations and power consumption via hybrid resource provisioning,” in Green Computing Conference and Workshops (IGCC), 2011 International. IEEE, 2011, pp. 1–8.
[8] H. N. Van, F. D. Tran, and J.-M. Menaud, “Performance and power management for cloud infrastructures,” in Cloud Computing (CLOUD), 2010 IEEE 3rd International Conference on. IEEE, 2010, pp. 329–336.
[9] Y. C. Lee and A. Y. Zomaya, “Energy efficient utilization of resources in cloud computing systems,” The Journal of Supercomputing, vol. 60, no. 2, pp. 268–280, 2012.
[10] J. Xu and J. A. Fortes, “Multi-objective virtual machine placement in virtualized data center environments,” in Green Computing and Communications (GreenCom), 2010 IEEE/ACM Int’l Conference on & Int’l Conference on Cyber, Physical and Social Computing (CPSCom). IEEE, 2010, pp. 179–188.
[11] Z. Shen, S. Subbiah, X. Gu, and J. Wilkes, “Cloudscale: elastic resource scaling for multi-tenant cloud systems,” in Proceedings of the 2nd ACM Symposium on Cloud Computing. ACM, 2011, p. 5.
[12] B. Heller, S. Seetharaman, P. Mahadevan, Y. Yiakoumis, P. Sharma, S. Banerjee, and N. McKeown, “Elastictree: Saving energy in data center networks.” in NSDI, vol. 10, 2010, pp. 249–264.
[13] A. Berl, E. Gelenbe, M. Di Girolamo, G. Giuliani, H. De Meer, M. Q. Dang, and K. Pentikousis, “Energy-efficient cloud computing,” The computer journal, vol. 53, no. 7, pp. 1045–1051, 2010.
[14] S.-w. Liao, T.-H. Hung, D. Nguyen, C. Chou, C. Tu, and H. Zhou, “Machine learning-based prefetch optimization for data center applications,” in Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis. ACM, 2009, p. 56.
[15] P. Bodık, R. Griffith, C. Sutton, A. Fox, M. Jordan, and D. Patterson, “Statistical machine learning makes automatic control practical for internet datacenters,” in Proceedings of the 2009 conference on Hot topics in cloud computing, 2009, pp. 12–12.