# Energy-Efficient Scheduling with Predictions

arXiv: https://arxiv.org/abs/2402.17143 · HTML: https://mlsanity.com/paper/2402.17143/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 27 Feb 2024.

## Builds on (most cited first, 10 of 10)

- [Competitive caching with machine learned advice](https://mlsanity.com/paper/1802.05399/) (2018)
- [Scheduling with Predictions and the Price of Misprediction](https://mlsanity.com/paper/1902.00732/) (2019)
- [Learning Augmented Energy Minimization via Speed Scaling](https://mlsanity.com/paper/2010.11629/) (2020)
- [Permutation Predictions for Non-Clairvoyant Scheduling](https://mlsanity.com/paper/2202.10199/) (2022)
- [A Novel Prediction Setup for Online Speed-Scaling](https://mlsanity.com/paper/2112.03082/) (2021)
- [Scheduling with Speed Predictions](https://mlsanity.com/paper/2205.01247/) (2022)
- [Strategyproof Scheduling with Predictions](https://mlsanity.com/paper/2209.04058/) (2022)
- [Speed-Oblivious Online Scheduling: Knowing (Precise) Speeds is not Necessary](https://mlsanity.com/paper/2302.00985/) (2023)
- [Energy-Efficient Algorithms](https://mlsanity.com/paper/1605.08448/) (2016)
- [Scheduling with Predictions](https://mlsanity.com/paper/2212.10433/) (2022)

## Built on by (most cited first, 1 of 1)

- [Learning-Augmented Algorithms for MTS with Bandit Access to Multiple Predictors](https://mlsanity.com/paper/2506.05479/) (2025)

## Abstract (arXiv)

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.