A.Thakur and M.S. Goraya, "A taxonomic survey on load balancing in cloud", Journal of Network and Computer Applications, vol. 98, pp. 43-57, 2017.
 E. Jafarnejad Ghomi, A.M. Rahmani and N.N. Qader, "Load-balancing algorithms in cloud computing: A survey", Journal of Network and Computer Applications, vol. 88, pp. 50-71, 2017.
 S.B. Melhem, A. Agarwal, N. Goel and N. Zaman,” Markov Prediction Model for Host Load Detection and VM Placement in Live Migration”, IEEE Access, vol. 6, pp. 7190-7205,2018.
 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”, Concurrency and Computation: Practice & Experience, vol. 24, pp. 1397-1420,2012.
 S.B. Melhem , A. Agarwal, N.Goel and M. Zaman, “Selection Process Approaches in Live Migration: A Comparative Study”, 2017 8th International Conference on Information and Communication Systems (ICICS), pp. 23-28, 2017.
 A. Beloglazov, J. Abawajy J, R. Buyya. “Energy-aware resource allocation heuristics for efﬁcient management of datacenters for cloud computing”. Future Generation Computer Systems, vol. 28, pp. 755-768, 2011.
 A. Bala1, I. Chana, “Prediction-based proactive load balancing approach through VM migration”, Engineering with Computers, vol. 32, pp. 581-592, 2016.
 F. Farahnakian, P. Liljeberg, and J. Plosila, “LiRCUP: Linear regression based CPU usage prediction algorithm for live migration of virtualmachines in data centers,'' 39th IEEE Euromicro Conference Series on Software Engineering and Advanced Application, vol. , pp. 357-364, 2013.
 M. Sommer, M. Klink, S. Tomforde and J. Hähner, “Predictive load balancing in cloud computing environments based on ensemble forecasting”, 2016 IEEE International Conference on Autonomic Computing (ICAC), pp. 300-307, 2016.
 M. Lavanya and V. Vaithiyanathan, “load prediction algorithm for dynamic resource allocation”, Indian Journal of Science and Technology, vol.8, 2015.
 F. Farahnakian, T. Pahikkala, P. Liljeberg, J. Plosila, N. T. Hieu and H. Tenhunen, ”Energy-aware VM consolidation in cloud data centers using utilization prediction model”, IEEE Transactions on Cloud Computing, vol. 7, pp. 524-526, 2019.
 A.A. El-Moursy1, A. Abdelsamea , R.Kamran and M. Saad, ” Multi-dimensional regression host utilization algorithm (MDRHU) for host overload detection in cloud computing”, Journal of Cloud Computing: Advances, Systems and Applications,vol.8, 2019.
 D. Patel, R. Gupta, R.K. Pateriya, “Energy-Aware Prediction-Based Load Balancing Approach with VM Migration for the Cloud Environment”. Data, Engineering And Applications, pp. 59-74, 2019.
 S. Ding, H. Zhao, Y. Zhang, X. Xu and R. Nie,” Extreme learning machine: Theory and applications” , Artificial Intelligence Review, vol. 44, pp. 103-115, 2013.
 G.B. Huang, Q.Y. Zhu and C.K. Siew, “Extreme learning machine: Theory and applications”, Neurocomputing, vol. 70, pp. 489-501, 2006.
 O.Ertugrul,” Forecasting electricity load by a novel recurrent extreme learning machines approach “International Journal of Electrical Power & Energy Systems, vol. 78, pp. 429-435, 2016.
 W.Voorsluys, J. Broberg, S. Venugopal, R. Buyya . “Cost of virtual machine live migration in clouds: a performance Evaluation”, In Proceedings of the 1st International Conference on Cloud Computing (CloudCom), Vol. 2009. 2009.