An execution environment oriented approach for scheduling dependent tasks of cloud computing workflows

Abstract

Cloud computing workflows are collections of interdependent tasks. Workflow scheduling is mainly concerned with cost reduction and overall completion time reduction. Our algorithm devised for workflow scheduling aims to address these two QoS factors. Workflows have many tasks which may require different kinds of execution environment for each task. Supporting all these environment dependencies and task dependencies is really difficult for a cloud scheduling system. Workflows are represented using directed acyclic graph (DAG) which shows the dependencies among tasks. Introducing execution environment dependencies to a DAG will lead to a several combinatorial DAGs. In this paper, our system provides a modified DAG which supports task and execution environment dependency. This paper deals with this time-cost tradeoff and tries to balance both these QoS in compromised way. It compromises the cost when the completion time of tasks is higher and vice versa. Hence, the cost as well as makespan will be as minimum as possible. Experimental results show that our algorithm provides a better time-cost balanced solution in imposing execution environment into task DAG.