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Power Aware Scheduling for Parallel Tasks via Task Clustering

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4 Author(s)
Lizhe Wang ; Pervasive Technol. Inst., Indiana Univ., Bloomington, IN, USA ; Jie Tao ; von Laszewski, G. ; Dan Chen

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 precedence-constrained 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.

Published in:

Parallel and Distributed Systems (ICPADS), 2010 IEEE 16th International Conference on

Date of Conference:

8-10 Dec. 2010

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