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

Bruno Gaujal 1 Alain Girault 2 Stéphan Plassart 1, 2
1 POLARIS - Performance analysis and optimization of LARge Infrastructures and Systems
LIG - Laboratoire d'Informatique de Grenoble, Inria Grenoble - Rhône-Alpes
2 SPADES - Sound Programming of Adaptive Dependable Embedded Systems
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
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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Submitted on : Friday, July 3, 2020 - 10:13:14 AM
Last modification on : Monday, December 14, 2020 - 3:40:12 PM
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Bruno Gaujal, Alain Girault, Stéphan Plassart. Dynamic Speed Scaling Minimizing Expected Energy Consumption for Real-Time Tasks. Journal of Scheduling, Springer Verlag, 2020, pp.1-25. ⟨10.1007/s10951-020-00660-9⟩. ⟨hal-02888573⟩

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