Energy-Aware Workflow Scheduling Using Frequency Scaling

Abstract

Dynamic Voltage and Frequency Scaling (DVFS) is a power management technique used to decrease the processor frequency and minimize power consumption in modern computing systems. This may lead to higher energy savings for large-scale computational problems, with scientific workflows comprising an important category of applications among these. However, as frequency scaling may result in increased execution time overall, idle time on the processors may also increase, to such a degree that any gains in power are annulled, this depends on the system and workflow characteristics. In this paper, we propose a scheduling algorithm that adopts frequency scaling to reduce overall energy consumption of scientific workflows given an allocation of tasks onto machines and a deadline to complete the execution. Based on the observation that using the lowest possible frequency may not necessarily be energy-efficient, the proposed algorithm works iteratively to scale the frequency further and distribute any slack time, only when overall energy consumption can be decreased. Synthetic data based on parameters of real scientific workflows are used in the evaluation. The results show that the proposed algorithm can achieve energy savings, sometimes at the expense of execution time to reduce the idle time of the processors and decrease overall energy consumption.