Energy-Efficient Scheduling with Predictions
<|reference_start|>Energy-Efficient Scheduling with Predictions: 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.<|reference_end|>
@article{balkanski2024energy-efficient,
    title={Energy-Efficient Scheduling with Predictions},
    author={Eric Balkanski and Noemie Perivier and Clifford Stein and Hao-Ting Wei},
    journal={arXiv preprint arXiv:2402.17143},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17143},
    primaryClass={cs.DS cs.LG}
}