A new metaheuristic‐based method for solving the virtual machines migration problem in the green cloud computing
Yanfei Xu, Karlo Abnoosian
Abstract
Summary Cloud computing (CC) provides dynamic hiring of server abilities as scalable virtualized services to end‐users. However, data center hosting wastes massive amounts of energy resulting in high operational costs and carbon footprints. Also, virtualization is one of CC's main features, and physical resources are delivered by virtual machine (VM). Therefore, in the present article, a new method is provided to improve the VM energy consumption and execution time in the VM migration problem using a hybrid optimization algorithm. Since this issue is one of the famous NP‐hard problems, a method is proposed in this article works based on genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The hybrid algorithm uses a GA to dominate PSO algorithms' constraints, such as weak convergence and stymie in global optima. The CloudSim simulator is employed to show the efficiency of the method compared to others. Using this method will keep the proficiency and power performance of the data centers at the same level. The results showed that energy consumption in the proposed method is better than the other three methods and has been improved by an average of 23.19%. Also, the results showed that execution time is better than the other three methods and has been improved by an average of 29.01%.