PSOMCD: Particle Swarm Optimization Algorithm Enhanced with Modified Crowding Distance for Load Balancing in Cloud Computing

Abstract

Effective load balancing in cloud computing architectures is crucial towards enhancing resource utilization, response times, and stability in the system. The present study proposes a new strategy with a Particle Swarm Optimization algorithm enhanced with Modified Crowding Distance (PSOMCD) to tackle task scheduling among Virtual Machines (VMs) in dynamic scenarios. The traditional PSO algorithm is supplemented by an enhanced crowding distance mechanism by PSOMCD to improve diversity in decision spaces and convergence to optimal solutions. The multi-objective fitness function addresses principal challenges in cloud computing, including load distribution, energy consumption, and throughput optimization. The performance of the algorithm is demonstrated in simulations, comparing its performance with other optimization techniques available in the literature. Results prove that PSOMCD provides better task allocation, improved load balancing, and decreased energy usage, thus effectively managing resources in dynamic and heterogeneous cloud ecosystems.