A PSO-based task scheduling algorithm improved using a load-balancing technique for the cloud computing environment

Abstract

Dynamic on-demand resource provisioning is one of the primary goals of the cloud computing task scheduling process. Task scheduling is a nondeterministic polynomial time (NP)-hard problem and is responsible for assigning tasks to virtual machines (VMs) in a way that increases the resource utilization and performance, reduces response time, and keeps the whole system balanced. In this paper, we present a static task scheduling method based on the particle swarm optimization (PSO) algorithm where the tasks are assumed to be non-preemptive and independent. We have improved the performance of the basic PSO method using a load-balancing technique. We have compared our proposed method with round robin (RR) task scheduling, improved PSO task scheduling and a load-balancing technique. The simulation results show that our method outperforms these algorithms by an increase of resource utilization of 22% and a decrease of makespan by 33%, compared with the basic PSO algorithm. The results illustrate that our proposed method converges to the near optimal solution faster than the basic PSO algorithm and is more efficacious with more tasks.