Energy-Constrained Provisioning for Scientific Workflow Ensembles

Large computational problems may often be modelled using multiple scientific workflows with similar structure. These workflows can be grouped into ensembles, which may be executed on distributed platforms such as the Cloud. In this paper, we focus on the provisioning of resources for scientific workflow ensembles and address the problem of meeting energy constraints along with either budget or deadline constraints. We propose and evaluate two energy-aware algorithms that can be used for resource provisioning and task scheduling. Experimental evaluation is based on simulations using synthetic data based on parameters of real scientific workflow applications. The results show that our proposed algorithms can meet constraints and minimize energy consumption without compromising the number of completed workflows in an ensemble.