Enhanced Energy-Efficient Scheduling for Parallel Tasks Using Partial Optimal Slacking

This paper studies the problem of energy-efficient scheduling for parallel tasks in high-performance computing systems, such as clusters and data centers. Our goal is to minimize the energy consumption of parallel tasks within a deadline constraint. Among the existing techniques that reduce the energy for computing systems, dynamic voltage and frequency scaling (DVFS) is generally considered as a promising technique that can strike a balance between the energy consumption and the performance for tasks. By using the DVFS technique, the main line of research is to slack the non-critical path tasks to reduce energy consumption of parallel tasks. However, the existing studies slack the tasks greedily and could not efficiently utilize the slack-room, i.e. the idle time of the processors. In this paper, we develop a novel slacking concept, partial optimal slacking (POS), which can take full advantage of the slack-room by slack-sharing. Our formal analysis shows that POS can lead to optimum energy reduction in the partial task set. Based on the POS concept, we propose a new scheduling algorithm for parallel tasks, namely enhanced an energy-efficient scheduling (EES) algorithm. Through extensive evaluation studies, the results demonstrate that the EES algorithm can further improve the energy efficiency of parallel tasks while meeting the deadline constraint.