TSPSO: Enhanced Task Scheduling using Optimized Particle Swarm Algorithm in Cloud Computing Environment

Abstract

The most significant constraint in cloud computing infrastructure is job/task scheduling which affords the vital role of efficiency of the entire cloud computing services and offerings. Job/ task scheduling in cloud infrastructure means that to assign the best appropriate cloud resources for the given job/task by considering different factors: execution time and cost, infrastructure scalability and reliability, platform availability and throughput, resource utilization, and make span. The proposed enhanced task scheduling algorithm using particle swarm optimization considers the optimization of makes pan and scheduling time. We propose the proposed model by using dynamic adjustment of parameters with discrete positioning (DAPDP) based algorithm to schedule and allocate cloud jobs/tasks that ensure optimized makes pan and scheduling time. DAPDP can witness a substantial role in attaining reliability by seeing the available, scheduled, and allocated cloud resources. Our approach DAPDP compared with other existing particle swarm and optimization job/task scheduling algorithms to prove that DAPDP can save in makes pan, scheduling, and execution time.