An Efficient Approach for Minimization of Energy and Makespan in Cloud Computing

Abstract

Cloud computing is a rapidly growing technology that offers different services to end consumers in a limited amount of time through a pay-as-you-go model. Furthermore, the rise of cloud computing has an additional benefit for implementation in a massive scientific workflow. The scientific workflow describes a calculation sequence that allows the analysis of distributed and hierarchical data; since this workflow involves a large number of tasks, energy consumption is a major concern. Therefore, we propose a solution that not only reduces energy consumption but also reduces processing time. Furthermore, we have developed an efficient approach by considering energy as a parameter and varying the size in montage scientific workflow model to reduce energy and the time of processing.