Energy Aware Scheduling on Heterogeneous Multiprocessors with DVFS and Duplication

Abstract

Duplication and dynamic voltage/frequency scaling (DVFS) creates an interesting trade-off for scheduling task graphs on multiprocessors to improve energy consumption and schedule length (or makespan). With DVFS, tasks are made to run on low voltages, which decreases their computation power. However, it also increases their execution costs and hence, may increase the schedule length. Furthermore, applying DVFS on processors does not impact the communication delay/energy consumption. Duplicating a task on multiple processors reduces the communication delay among them, which further reduces the schedule length. Although duplication reduces the communication energy among processors, it also increases the overall computation energy. In this paper, we explore this trade-off between duplication and DVFS, and propose a polynomial time heuristic to schedule task graphs on heterogeneous multiprocessors. The tasks are carefully duplicated with DVFS to reduce its impact on the computation energy. The results demonstrate that the proposed algorithm is able to effectively balance the makespan and energy consumption over other algorithms in various scenarios.