Learning-Augmented Dynamic Power Management with Multiple States via New Ski Rental Bounds
This work addresses power management in computing systems, offering incremental improvements by integrating predictions into an existing online framework.
The paper tackles the problem of minimizing power consumption in systems with multiple power-saving states during idle periods of unknown lengths by developing a learning-augmented online algorithm that uses predictions of idle period lengths. The algorithm achieves near-optimal performance with accurate predictions and maintains worst-case guarantees similar to classical online algorithms, supported by experiments.
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.