An important goal of modern scheduling systems is to efficiently manage power usage. In energy-efficient scheduling, the operating system controls the speed at which a machine is processing jobs with the dual objective of minimizing energy consumption and optimizing the quality of service cost of the resulting schedule. Since machine-learned predictions about future requests can often be learned from historical data, a recent line of work on learning-augmented algorithms aims to achieve improved performance guarantees by leveraging predictions. In particular, for energy-efficient scheduling, Bamas et. al. [BamasMRS20] and Antoniadis et. al. [antoniadis2021novel] designed algorithms with predictions for the energy minimization with deadlines problem and achieved an improved competitive ratio when the prediction error is small while also maintaining worst-case bounds even when the prediction error is arbitrarily large. In this paper, we consider a general setting for energy-efficient scheduling and provide a flexible learning-augmented algorithmic framework that takes as input an offline and an online algorithm for the desired energy-efficient scheduling problem. We show that, when the prediction error is small, this framework gives improved competitive ratios for many different energy-efficient scheduling problems, including energy minimization with deadlines, while also maintaining a bounded competitive ratio regardless of the prediction error. Finally, we empirically demonstrate that this framework achieves an improved performance on real and synthetic datasets.

翻译：现代调度系统的重要目标之一是高效管理能耗。在能效调度中，操作系统控制机器处理作业的速度，其双重目标是：最小化能耗，并优化调度结果的服务质量成本。由于关于未来请求的机器学习预测通常可从历史数据中学习，近期关于学习增强型算法的研究旨在通过利用预测实现更好的性能保证。具体而言，针对能效调度问题，Bamas等人 [BamasMRS20] 与Antoniadis等人 [antoniadis2021novel] 设计了带有预测的算法以解决带截止日期的能耗最小化问题，并在预测误差较小时实现了改进的竞争比，同时即使在预测误差任意大的情况下也能保持最坏情况下的界限。本文考虑能效调度的通用设置，提出一种灵活的学习增强型算法框架，该框架将所需能效调度问题的离线与在线算法作为输入。我们证明：当预测误差较小时，该框架能为多种能效调度问题（包括带截止日期的能耗最小化）提供改进的竞争比，同时无论预测误差如何均能保持有界竞争比。最后，我们通过实验证明该框架在真实与合成数据集上实现了性能提升。