Hybrid Meta-Heuristic Algorithm for Optimal Virtual Machine Migration in Cloud Computing

Abstract

Virtual Machine (VM) migration is one of the most important features of cloud computing for resource utilization optimization, energy minimization, and quality of service enhancement. Existing migration solutions, however, suffer from excessive migration overhead, energy inefficiency, and ineffective allocation of resources. This study proposes a novel hybrid meta-heuristic algorithm through the integration of Particle Swarm Optimization (PSO) and Seahorse Optimization (SHO) to address the drawbacks. The proposed PSOSHO algorithm takes advantage of the global exploration capability of PSO and the adaptive exploitation feature of SHO and provides a sound solution for VM migration in dynamic cloud computing environments. Extensive simulation experiments were conducted for a different number of cloud tasks, and the results demonstrated that PSOSHO significantly outperforms existing algorithms. Specifically, it achieves improvements of up to 54% in load factor, 60% in migration count, 48% in migration cost, 7% in energy consumption, 27% in resource availability, and 37% in computation time. These results confirm the effectiveness and robustness of the proposed methodology for optimal VM migration and resource management in virtualized cloud computing infrastructures.