Studies On Metaheuristic Based Multi Objective Workflow Scheduling Schemes In Infrastructure As A Service IaaS Cloud

Abstract

newlineHuge scientific problems and business applications that require extensive computing can newlinebe modeled as workflow applications. Workflows are composed of dependent newlinecomputational tasks. The efficient scheduling and execution of workflows in a distributed newlineenvironment is a significant research issue on account of its ever-increasing computation newlineand data requirements. Cloud computing, the recently evolved distributed computing newlineparadigm, offers several advantages for the deployment of workflow applications with its newlinelarge scale scalable and elastic virtualized resources, which are available on demand. newlineHowever, the heterogeneous and dynamic characteristics of the cloud environment and newlinethe complex structure of workflows make the scheduling of workflow tasks a challenging newlinejob. Many QoS (Quality of Service) constraints are to be addressed and also the optimal newlineuse of computing resources needs to be ensured for efficient workflow scheduling in the newlinecloud. Hence, efficient algorithms are required for the optimised scheduling of workflow newlinetasks in a cloud environment. This research addresses the issues in scheduling workflow newlinetasks by meeting various QoS objectives in a cloud infrastructure. In order to develop newlineefficient scheduling schemes to execute workflow applications, this study investigates the newlinescheduling approaches and resource provisioning methods for workflows in IaaS clouds. newlineThe main objective of the study is to find optimal schedules with the lowest execution newlinecost, execution time, and proper load distribution for workflow execution in the cloud. newlineWorkflow scheduling is modeled as a multi objective optimization problem to find newlineoptimal schedules with the least execution cost, execution time, and proper load newlinedistribution for workflow execution.