A Particle Swarm Optimization Based Pareto Optimal Task Scheduling in Cloud Computing

Abstract

Task scheduling in Cloud computing is a challenging aspect due to the conflicting requirements of end users of cloud and the Cloud Service Provider (CSP). The challenge at the CSP’s end is to schedule tasks submitted by the cloud users in an optimal way such that it should meet the quality of service (QoS) requirements of the user at one end and the running costs of the infrastructure to a minimum level at the other end for better profit. The focus is on two objectives, makespan and cost, to be optimized simultaneously using meta heuristic search techniques for scheduling independent tasks. A new variant of continuous Particle Swarm Optimization (PSO) algorithm, named Integer-PSO, is proposed to solve the bi-objective task scheduling problem in cloud which out performs the smallest position value (SPV) rule based PSO technique.