Learning-augmented dynamic power management with multiple states via new ski rental bounds
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.
|35th Conference on Neural Information Processing Systems, NeurIPS 2021|
|Organisation||Networks and Optimization|
Antoniadis, A, Coester, C.E, Eliáš, M, Polak, A, & Simon, B. (2021). Learning-augmented dynamic power management with multiple states via new ski rental bounds. In Proceedings NeurIPS (Annual Conference on Neural Information Processing Systems).