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

• URL: http://arxiv.org/abs/2110.13116v1
• Date: Mon, 25 Oct 2021 17:14:20 GMT
• Title: Learning-Augmented Dynamic Power Management with Multiple States via New Ski Rental Bounds
• Authors: Antonios Antoniadis, Christian Coester, Marek Eli\'a\v{s}, Adam Polak, Bertrand Simon
• Abstract summary: We study the online problem of minimizing power consumption in systems with multiple power-saving states. 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.
• Score: 38.80523710193321
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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