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

Part of Advances in Neural Information Processing Systems 34 pre-proceedings (NeurIPS 2021)

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Antonios Antoniadis, Christian Coester, Marek Elias, Adam Polak, Bertrand Simon


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.