Enhanced Particle Swarm Optimization Algorithm for Cloud Computing Environments Workload Scheduling

Abstract

Because of cloud services' increased accessibility, enhanced performance, and affordability, cloud service providers are always looking for ways to speed up work completion in order to increase revenues and save energy costs. Even though many scheduling algorithms have been developed, many of these methods only focus on one aspect of the scheduling process. An innovative method called the Enhanced Particle Swarm Optimisation Algorithm (EPSOA) is put forward to effectively improve optimisation outcomes for the cloud workload scheduling issue. The PSO and the Lévy flight are integrated by EPSOA. The purpose of adding Lévy combat is to increase the PSO's search space and speed up convergence via adaptive crossover. The Cloudsim program is used to simulate and assess the EPSOA model under various test scenarios. By using a variety of factors and contrasting them with those of current algorithms, the efficacy of EPSOA is evaluated. The results show that EPSOA performs better than other algorithms in terms of execution cost, energy consumption, and resource usage, demonstrating its effectiveness in managing the difficulties associated with multi objective cloud job scheduling.