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Journal Articles Journal of Scheduling Year : 2020

Dynamic Speed Scaling Minimizing Expected Energy Consumption for Real-Time Tasks

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Abstract

This paper proposes a Discrete Time Markov Decision Process (MDP) approach to compute the optimal on-line speed scaling policy to minimize the energy consumption of a single processor executing a finite or infinite set of jobs with real-time constraints. We provide several qualitative properties of the optimal policy: monotonicity with respect to the jobs parameters, comparison with on-line de-terministic algorithms. Numerical experiments in several scenarios show that our proposition performs well when compared with off-line optimal solutions and out-performs on-line solutions oblivious to statistical information on the jobs.
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Dates and versions

hal-02888573 , version 1 (03-07-2020)

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Cite

Bruno Gaujal, Alain Girault, Stéphan Plassart. Dynamic Speed Scaling Minimizing Expected Energy Consumption for Real-Time Tasks. Journal of Scheduling, 2020, pp.1-25. ⟨10.1007/s10951-020-00660-9⟩. ⟨hal-02888573⟩
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