基于任务集群的并行任务的功率感知调度

Lizhe Wang, J. Tao, G. Laszewski, Dan Chen
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引用次数: 15

摘要

众所周知,通过降低高端计算的能耗可以获得各种好处。本文旨在利用DVFS技术开发集群中并行任务的功率感知调度启发式算法。在本文中,提出了优先级约束的并行任务、支持DVFS的集群和能耗的形式化模型。研究非关键作业的松弛时间,在不增加任务整体执行时间的前提下,延长非关键作业的执行时间,降低能耗。本文提出了一种用于并行任务调度的能量感知任务聚类算法,仿真结果证明了本文提出的能量感知调度启发式算法的设计和实现是正确的。
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Power Aware Scheduling for Parallel Tasks via Task Clustering
It has been widely known that various benefits can be achieved by reducing energy consumption for high end computing. This paper aims to develop power aware scheduling heuristics for parallel tasks in a cluster with the DVFS technique. In this paper, formal models are presented for precedenceconstrained parallel tasks, DVFS enabled clusters, and energy consumption. This paper studies the slack time for non-critical jobs, extends their execution time and reduces the energy consumption without increasing the task’s execution time as a whole. This paper develops a power aware task clustering algorithm for parallel task scheduling Simulation results justify the design and implementation of proposed energy aware scheduling heuristics in the paper.
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