Energy Aware Adaptive Scheduling of Workflows

Scientific workflows consist of multi-step compu-tational tasks executing in the form of data flow and task dependencies. These workflows are defined to be long running and fault tolerant. There is evidence of improving performance achieved through run-time adaptive changes made to the work-flow execution. The aim of the work presented in this paper is to highlight the benefits that adaptive scheduling of scientific workflows have on the energy consumption of the computation. In this paper, an architecture for the implementation of an energy-aware adaptive scheduler is presented. The monitoring, analysis, planning and execution (MAPE) model from autonomic computing is used to propose a set of run-time modifications that will be used by the scheduler to improve the performance and energy consumption of the workflow.