Workflow Scheduling in Cloud by Hybridization of Particle Swarm Optimization (PSO) with Grey Wolf Optimization (GWO)

Abstract

Cloud Computing is used commonly in almost every business or research field. It is young but familiar technology which enables the client to use its services without being bothered to know how the services run and leave this job to providers. Cloud providers offer three main services namely Infrastucture as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). Users can choose appropriate services which suittheir requirements. The user has to pay the amounts of resources used in particular duration of time accordingly. It is pay-as-you-go model. It has opened up many new opportunities for researchers as well as business organizations. It is flexible as the users can scale up or scale down resources depending on their requirement.

Various scientific workflows depend on the static configuration of virtual machines, which is not a real condition. The workflow scheduling creates an issue which resists the efficiency of parameters like makespan and cost in the cloud environments. This optimization depends on the Random distribution for local userslike Virtual Machine (VM) or global users (Datacentre) which sometimes take more time therefore increase the overall cost.

To overcome this problem we propose our work on Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) for effective scheduling. This work is based on the optimization of Total Execution Time (TET) and Total Execution Cost (TEC). The results of the proposed approach are found to be effective when compared with existing methods. The hybridization of both the optimization technique is done. The results were compared with the existing BAT algorithm while parsing the workflow into task. These tasks were mapped onto virtual machine. The results obtained are found better in terms of execution time and cost.