Hybrid meta-heuristic algorithm for optimal virtual machine placement and migration in cloud computing

Data sharing in cloud computing happens with multiple participants to freely distribute the group data, which focuses on advancing the effectiveness of work in cooperative backgrounds and has attained widespread benefits. The main intent of this article is to accomplish a virtual machines (VMs) placement and migration model using a hybrid meta-heuristic concept. A new meta-heuristic algorithm named DJ-HA is developed for optimal VM placement and migration to reduce the count of active servers, and minimization of makespan, and energy consumption with a faster convergence rate in a cloud background. Then, the VM migration is done based on the multi-objective function concerning energy consumption and makespan using the same hybrid DJ-HA. From the result analysis, the energy consumption of the DJ-HA is correspondingly secured at 4.3%, 3.5%, 31%, and 33% more advanced than PSO, GWO, DHOA, and JA, at the 100th iteration for Experiment 1. Accordingly, the cost function of the suggested DJ-HA is secured at 88.8%, 89.4%, 33.3%, and 50% increased than PSO, GWO, DHOA, and JA at the 100th iteration for Experiment 4. Hence, it is proved that the suggested VM migration using DJ-HA is enriched than the other conventional algorithms.

Automated summary

This paper introduces a new meta-heuristic algorithm named DJ-HA for optimizing the deployment and migration of virtual machines in a cloud environment. Through result analysis, it is proven that the algorithm performs better in terms of energy consumption and makespan.