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Energy-Aware Real-Time Task Scheduling for Heterogeneous Multiprocessors with Particle Swarm Optimization Algorithm

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  • Weizhe Zhang
  • Hucheng Xie
  • Boran Cao
  • Albert M. K. Cheng

Abstract

Energy consumption in computer systems has become a more and more important issue. High energy consumption has already damaged the environment to some extent, especially in heterogeneous multiprocessors. In this paper, we first formulate and describe the energy-aware real-time task scheduling problem in heterogeneous multiprocessors. Then we propose a particle swarm optimization (PSO) based algorithm, which can successfully reduce the energy cost and the time for searching feasible solutions. Experimental results show that the PSO-based energy-aware metaheuristic uses 40%–50% less energy than the GA-based and SFLA-based algorithms and spends 10% less time than the SFLA-based algorithm in finding the solutions. Besides, it can also find 19% more feasible solutions than the SFLA-based algorithm.

Suggested Citation

  • Weizhe Zhang & Hucheng Xie & Boran Cao & Albert M. K. Cheng, 2014. "Energy-Aware Real-Time Task Scheduling for Heterogeneous Multiprocessors with Particle Swarm Optimization Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-9, August.
    • Handle: RePEc:hin:jnlmpe:287475
    DOI: 10.1155/2014/287475
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