A Lion‐Whale optimization‐based migration of virtual machines for data centers in cloud computing

Abstract

SummaryThe scalability, reliability, and flexibility in the cloud computing services are the obligations in the growing demand of computation power. To sustain the scalability, a proper virtual machine migration (VMM) approach is needed with apt balance on quality of service and service‐level agreement violation. In this paper, a novel VMM algorithm based on Lion‐Whale optimization is developed by integrating the Lion optimization algorithm and the Whale optimization algorithm. The optimal virtual machine (VM) migration is performed by the Lion‐Whale VMM based on a new fitness function in the regulation of the resource use, migration cost, and energy consumption of VM placement. The experimentation of the proposed VM migration strategy is performed over 4 cloud setups with a different configuration which are simulated using CloudSim toolkit. The performance of the proposed method is validated over existing optimization‐based VMM algorithms, such as particle swarm optimization and genetic algorithm, using the performance measures, such as energy consumption, migration cost, and resource use. Simulation results reveal the fact that the proposed Lion‐Whale VMM effectively outperforms other existing approaches in optimal VM placement for cloud computing environment with reduced migration cost of 0.01, maximal resource use of 0.36, and minimal energy consumption of 0.09.