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Communication Dans Un Congrès Année : 2021

Learning-Augmented Dynamic Power Management with Multiple States via New Ski Rental Bounds

Antonios Antoniadis
  • Fonction : Auteur
Christian Coester
  • Fonction : Auteur
Marek Eliáš
  • Fonction : Auteur
Adam Polak
  • Fonction : Auteur
Bertrand Simon

Résumé

We study the online problem of minimizing power consumption in systems with multiple power-saving states. During idle periods of unknown lengths, an algorithm has to choose between power-saving states of different energy consumption and wake-up costs. We develop a learning-augmented online algorithm that makes decisions based on (potentially inaccurate) predicted lengths of the idle periods. The algorithm's performance is near-optimal when predictions are accurate and degrades gracefully with increasing prediction error, with a worst-case guarantee almost identical to the optimal classical online algorithm for the problem. A key ingredient in our approach is a new algorithm for the online ski rental problem in the learning augmented setting with tight dependence on the prediction error. We support our theoretical findings with experiments.

Dates et versions

hal-03807842 , version 1 (10-10-2022)

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Antonios Antoniadis, Christian Coester, Marek Eliáš, Adam Polak, Bertrand Simon. Learning-Augmented Dynamic Power Management with Multiple States via New Ski Rental Bounds. Thirty-fifth Conference on Neural Information Processing Systems, Dec 2021, Online, United States. 11 p. ⟨hal-03807842⟩
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