Research Area:  Cloud Computing
Live virtual machine (VM) migration improves the performance of cloud data center in terms of energy efficiency, fault tolerance, and availability. The workload handled by cloud data center is dynamic in nature. This increases the resource requirement of either the migrated virtual machine or collocated virtual machine at any time leading to further migration. Inappropriately handled live VM migration imposes severe application performance degradation. In this paper, a combined forecasting technique to predict the resource requirement of any virtual machine is proposed. Based on the current and predicted resource utilization, live migration is performed by Combined Forecast Load-Aware technique. Experiments were carried out to evaluate the performance of the proposed technique on live VM migration. The outcomes indicate that the proposed approach has minimized the number of migrations, energy usage, and the message overhead when compared with the existing state-of-art technique.
Author(s) Name:  Getzi Jeba Leelipushpam Paulraj,Sharmila Anand John,J. Dinesh Peter and Immanuel Johnraja Jebadurai
Journal name:  Computers & Electrical Engineering
Publisher name:  ELSEVIER
Volume Information:  Volume 69, July 2018, Pages 287-300
Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0045790617315732