Solving Energy-Latency Dilemma: Task Allocation for Parallel Applications in Heterogeneous Embedded Systems

              Tao Xie                                                      Xiao Qin*, Mais Nijim

Department of Computer Science                     Department of Computer Science

   San Diego State University                New Mexico Institute of Mining and Technology

 San Diego, California 92182                               Socorro, New Mexico 87801

        xie@cs.sdsu.edu                                            {xqin,mais@cs.nmt.edu}

Parallel applications with energy and low-latency constraints are emerging in various networked embedded systems like digital signal processing, vehicle tracking, and infrastructure monitoring. However, conventional energy-driven task allocation schemes for a cluster of embedded nodes only concentrate on energy-saving when making allocation decisions. Consequently, the length of the schedules could be very long, which is unfavorable or in some situations even not tolerated. In this paper, we address the issue of allocating a group of parallel tasks on a heterogeneous embedded system with an objective of energy-saving and short-latency. A novel task allocation strategy, or BEATA (Balanced Energy-Aware Task Allocation), is developed to find an optimal allocation that minimizes overall energy consumption while confining the length of schedule to an ideal range. Experimental results show that BEATA significantly improves the performance of embedded systems in terms of energy-saving and schedule length over an existing allocation scheme.

Proceedings of the 35th International Conference on Parallel Processing (ICPP 2006), Columbus, Ohio, Aug. 2006.

* Contact author.  http://www.cs.nmt.edu/~xqin

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