Hybrid Competitive Swarm Optimization Algorithm Based Scheduling in the Cloud Computing Environment

Abstract

Cloud computing (CC) platform not only offers resource sharing for users and also provide many on-demand services. Business procedures can be managed to utilize the workflow technology on the cloud which signifies most of the problems in employing the resources in an effectual approach because of the dependencies between the tasks. Task scheduling (TS) in a CC platform applies to the procedure of effectively allocating resources computing to tasks or jobs submitted to the CC environment. In a cloud platform, tasks are separated as smaller subtasks that are applied in parallel on distinct machines. There are many features to consider if the TS is in CC platform, comprising the kind of tasks like resources cost, resources required, and resources availability. Therefore, this study presents a Hybrid Competitive Swarm Optimization Algorithm based Task Scheduling (HCSOA-TS) technique in the CC platform. The presented HCSOA-TS technique schedules the tasks proficiently in such a way that maximum resource utilization and performance gets accomplished. In the design of HCSOA, Cauchy mutation operator is included in the CSO algorithm and thereby improves the overall performance. In addition, the derivation of the fitness function by the HCSOA-TS technique undergoes optimal scheduling process and decreases energy usage. The experimental result analysis of the HCSOA-TS technique is tested using a series of measures. The comprehensive comparison study highlighted the improvements of the HCSOA-TS technique over recent approaches.