Enhanced Task Scheduling Using Optimized Particle Swarm Optimization Algorithm in Cloud Computing Environment

Abstract

The most significant constraint in cloud computing infrastructure is the 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 best appropriate cloud resources for the given job/task by considering of different factors: execution time and cost, infrastructure scalability and reliability, platform availability and throughput, resource utilization and makespan. The proposed enhanced task scheduling algorithm using particle swarm optimization considers optimization of makespan 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 ensues optimized makespan and scheduling time. DAPDP can witness a substantial role in attaining reliability in 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 makespan, scheduling and execution time.