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.

翻译：我们研究在多个节能状态的系统中最大限度地减少电力消耗的在线问题。 在无所事事的时段,算法必须在不同的能源消耗和觉醒成本之间做出选择。 我们开发了一个学习强化的在线算法,根据(可能不准确的)预计闲置时间长度做出决策。 当预测准确时算法的性能接近最佳,并且随着预测错误的增加而优雅地下降,而最坏的保证则几乎与问题的最佳传统在线算法相同。 我们方法中的一个关键要素是学习强化时在线滑雪租赁问题的新算法,同时严格依赖预测错误。我们用实验来支持我们的理论结论。