This paper proposes a discrete time Markov decision process 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 deterministic 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.

中文翻译：

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动态速度缩放最大限度地减少实时任务的预期能耗

本文提出了一种离散时间马尔可夫决策过程方法来计算最佳在线速度缩放策略，以最小化单个处理器执行具有实时约束的有限或无限作业集的能耗。我们提供了最优策略的几个定性属性：关于工作参数的单调性，与在线确定性算法的比较。在几个场景中的数值实验表明，与离线最优解相比，我们的命题表现良好，并且优于忽略作业统计信息的在线解。