Keywords: Learning-augmented algorithms, online algorithms, energy-efficient algorithms, power management, ski rental
TL;DR: We design a learning-augmented algorithm for dynamic power management with multiple power-saving states, based on new & tight bounds for learning-augmented ski rental.
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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Supplementary Material: zip
Code: https://github.com/adampolak/dpm
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/learning-augmented-dynamic-power-management/code)
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