Towards Energy-aware Scheduling of Scientific Workflows

Contemporary scientific computation is increasingly structured using scientific workflows that are executed on highly scalable compute clusters. The execution of these workflows is generally geared towards optimizing run-time performance with the energy footprint of the execution being ignored. However, there is evidence that minimizing both execution time as well as energy consumption are not mutually exclusive. The aim of the work presented in this paper is to highlight the benefits of energy-aware scientific workflow execution. In this paper, we propose a set of requirements for an energy-aware scheduling and present an architecture for the implementation of an energy-aware scheduler.