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.

高效节能的工作流调度是当今计算平台（如基础架构即服务（IaaS）云）的需求。如果考虑启用动态电压缩放（DVS）的环境，则可以节省大量能量。但同样重要的是减少时间表的有效期，以使它不会超出云用户指定的期限。在本文中，我们提出了一种基于混合化学反应优化（HCRO）算法的工作流调度算法。所提出的方案被证明是节能的。除此之外，它还显示出最小化制造时间。我们将提出的方法称为节能工作流调度（EEWS）算法。通过一种新颖的方法引入EEWS，以通过考虑启用DVS的环境来确定可以节省的能量。通过对各种科学工作流程应用程序的仿真，我们证明了该方案在各种性能指标（包括制造时间和节能量）方面，比HCRO和多优先级队列遗传算法（MPQGA）等现有算法性能更好。还通过方差分析（ANOVA）检验及其后续的LSD分析来判断所提出算法的重要性。我们证明，在包括性能跨度和节能量在内的各种性能指标方面，该方案比HCRO和多优先级队列遗传算法（MPQGA）等现有算法性能更好。还通过方差分析（ANOVA）检验及其后续的LSD分析来判断所提出算法的重要性。我们证明，在包括性能跨度和节能量在内的各种性能指标方面，该方案比HCRO和多优先级队列遗传算法（MPQGA）等现有算法性能更好。还通过方差分析（ANOVA）检验及其后续的LSD分析来判断所提出算法的重要性。