Energy-Efficient Scheduling with Predictions

• URL: http://arxiv.org/abs/2402.17143v1
• Date: Tue, 27 Feb 2024 02:13:32 GMT
• Title: Energy-Efficient Scheduling with Predictions
• Authors: Eric Balkanski and Noemie Perivier and Clifford Stein and Hao-Ting Wei
• Abstract summary: In energy-efficient scheduling, the operating system controls the speed at which a machine is processing jobs. Recent work on learning-augmented algorithms aims to achieve improved performance guarantees by leveraging predictions. We provide a flexible learning-augmented algorithmic framework that takes as input an offline and an online algorithm for the desired energy-efficient scheduling problem.
• Score: 4.662349748983561
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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