An Energy Efficient Algorithm for Workflow Scheduling in IaaS Cloud

Energy efficient workflow scheduling is the demand of the present time's computing platforms such as an infrastructure-as-a-service (IaaS) cloud. An appreciable amount of energy can be saved if a dynamic voltage scaling (DVS) enabled environment is considered. But it is important to decrease makespan of a schedule as well, so that it may not extend beyond the deadline specified by the cloud user. In this paper, we propose a workflow scheduling algorithm which is inspired from hybrid chemical reaction optimization (HCRO) algorithm. The proposed scheme is shown to be energy efficient. Apart from this, it is also shown to minimize makespan. We refer the proposed approach as energy efficient workflow scheduling (EEWS) algorithm. The EEWS is introduced with a novel measure to determine the amount of energy which can be conserved by considering a DVS-enabled environment. Through simulations on a variety of scientific workflow applications, we demonstrate that the proposed scheme performs better than the existing algorithms such as HCRO and multiple priority queues genetic algorithm (MPQGA) in terms of various performance metrics including makespan and the amount of energy conserved. The significance of the proposed algorithm is also judged through the analysis of variance (ANOVA) test and its subsequent LSD analysis.