Particle Swarm Optimization with Enhanced Neighborhood Search for Task Scheduling in Cloud Computing

Abstract

Due to cloud computing services' dynamic and elastic nature, implementing efficient task scheduling methods becomes primordial for cloud providers to handle the ever-growing demands and meet the Service Level Agreements (SLA) cost-effectively. In this paper, we propose a novel task scheduling approach, named ENS-PSO, that enhances Particle Swarm Op-timization (PSO) with an efficient neighborhood search strategy. We evaluate ENS-PSO using the CloudSim toolkit. Simulation results demonstrate that the proposed task scheduling with en-hanced neighborhood search outperforms other task scheduling algorithms in terms of makespan, energy consumption, and degree of imbalance.