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Energy-aware strategies for reliability-oriented real-time task allocation on heterogeneous platforms

Abstract : Low energy consumption and high reliability are widely identified as increasingly relevant issues in real-time systems on heterogeneous platforms. In this paper, we propose a multi-criteria optimization strategy to minimize the expected energy consumption while enforcing the reliability threshold and meeting all task deadlines. The tasks are replicated to ensure a prescribed reliability threshold. The platforms are composed of processors with different (and possibly unrelated) characteristics, including speed profile, energy cost and failure rate. We provide several mapping and scheduling heuristics towards this challenging optimization problem. Specifically, a novel approach is designed to control (i) how many replicas to use for each task, (ii) on which processor to map each replica and (iii) when to schedule each replica on its assigned processor. Different mappings achieve different levels of reliability and consume different amounts of energy. Scheduling matters because once a task replica is successful, the other replicas of that task are cancelled, which calls for minimizing the amount of temporal overlap between any replica pair. The experiments are conducted for a comprehensive set of execution scenarios, with a wide range of processor speed profiles and failure rates. The comparison results reveal that our strategies perform better than the random baseline, with a gain of 40% in energy consumption, for nearly all cases. The absolute performance of the heuristics is assessed by a comparison with a lower bound; the best heuristics achieve an excellent performance, with an average value only 4% higher than the lower bound.
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Submitted on : Monday, October 26, 2020 - 10:25:39 AM
Last modification on : Monday, November 16, 2020 - 9:56:02 AM

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Li Han, Yiqin Gao, Jing Liu, Yves Robert, Frédéric Vivien. Energy-aware strategies for reliability-oriented real-time task allocation on heterogeneous platforms. ICPP '20: 49th International Conference on Parallel Processing, Aug 2020, Edmonton AB Canada, France. pp.1-11, ⟨10.1145/3404397.3404419⟩. ⟨hal-02977879⟩

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