A Hybrid Metaheuristic Optimization Approach for Enhanced Resource Allocation in Cloud Computing Environments

Abstract

Cloud computing has emerged as a pivotal paradigm for delivering scalable and on-demand computing resources. Efficient resource allocation is paramount to maximizing the benefits of cloud infrastructure, including performance, cost-effectiveness, and energy efficiency. This paper presents a novel hybrid metaheuristic optimization approach that combines the strengths of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for enhanced resource allocation in cloud environments. The proposed algorithm, named GA-PSO, leverages the global exploration capabilities of GA and the local exploitation capabilities of PSO to achieve a superior balance between exploration and exploitation. The performance of the GA-PSO algorithm is evaluated through extensive simulations under various workload scenarios and compared against traditional GA and PSO algorithms. The results demonstrate that GA-PSO significantly improves resource utilization, reduces makespan, and minimizes energy consumption compared to its counterparts, highlighting its potential as a robust and efficient solution for resource allocation in cloud computing.