# Learning Augmented Energy Minimization via Speed Scaling

arXiv: https://arxiv.org/abs/2010.11629 · HTML: https://mlsanity.com/paper/2010.11629/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 30 of 38)

- [Permutation Predictions for Non-Clairvoyant Scheduling](https://mlsanity.com/paper/2202.10199/) (2022)
- [Online Algorithms with Multiple Predictions](https://mlsanity.com/paper/2205.03921/) (2022)
- [Learning-Augmented Dynamic Power Management with Multiple States via New Ski Rental Bounds](https://mlsanity.com/paper/2110.13116/) (2021)
- [A Novel Prediction Setup for Online Speed-Scaling](https://mlsanity.com/paper/2112.03082/) (2021)
- [A Universal Error Measure for Input Predictions Applied to Online Graph Problems](https://mlsanity.com/paper/2205.12850/) (2022)
- [Mixing predictions for online metric algorithms](https://mlsanity.com/paper/2304.01781/) (2023)
- [Online bipartite matching with imperfect advice](https://mlsanity.com/paper/2405.09784/) (2024)
- [Logarithmic Regret from Sublinear Hints](https://mlsanity.com/paper/2111.05257/) (2021)
- [A Regression Approach to Learning-Augmented Online Algorithms](https://mlsanity.com/paper/2205.08717/) (2022)
- [Algorithms for Caching and MTS with reduced number of predictions](https://mlsanity.com/paper/2404.06280/) (2024)
- [Learning-Augmented Algorithms for Online Linear and Semidefinite Programming](https://mlsanity.com/paper/2209.10614/) (2022)
- [Double Coverage with Machine-Learned Advice](https://mlsanity.com/paper/2103.01640/) (2021)
- [Robustification of Online Graph Exploration Methods](https://mlsanity.com/paper/2112.05422/) (2021)
- [Smoothed Online Optimization with Unreliable Predictions](https://mlsanity.com/paper/2202.03519/) (2022)
- [Paging with Succinct Predictions](https://mlsanity.com/paper/2210.02775/) (2022)
- [Active causal structure learning with advice](https://mlsanity.com/paper/2305.19588/) (2023)
- [Learning-Augmented Algorithms for Online TSP on the Line](https://mlsanity.com/paper/2206.00655/) (2022)
- [Non-clairvoyant Scheduling with Partial Predictions](https://mlsanity.com/paper/2405.01013/) (2024)
- [Learning-Augmented Algorithms for Online Steiner Tree](https://mlsanity.com/paper/2112.05353/) (2021)
- [Scheduling with Speed Predictions](https://mlsanity.com/paper/2205.01247/) (2022)
- [Improved Bounds for Online Facility Location with Predictions](https://mlsanity.com/paper/2107.08277/) (2021)
- [Contextual Pandora's Box](https://mlsanity.com/paper/2205.13114/) (2022)
- [Online Dynamic Acknowledgement with Learned Predictions](https://mlsanity.com/paper/2305.18227/) (2023)
- [The Predicted-Updates Dynamic Model: Offline, Incremental, and Decremental to Fully Dynamic Transformations](https://mlsanity.com/paper/2307.08890/) (2023)
- [Learning multivariate Gaussians with imperfect advice](https://mlsanity.com/paper/2411.12700/) (2024)
- [Approximate Proportionality in Online Fair Division](https://mlsanity.com/paper/2508.03253/) (2025)
- [Online Capacity Scaling Augmented With Unreliable Machine Learning Predictions](https://mlsanity.com/paper/2101.12160/) (2021)
- [Learning-Augmented Online Packet Scheduling with Deadlines](https://mlsanity.com/paper/2305.07164/) (2023)
- [On Optimal Consistency-Robustness Trade-Off for Learning-Augmented Multi-Option Ski Rental](https://mlsanity.com/paper/2312.02547/) (2023)
- [Learning-Augmented Algorithms for MTS with Bandit Access to Multiple Predictors](https://mlsanity.com/paper/2506.05479/) (2025)

## Abstract (arXiv)

As power management has become a primary concern in modern data centers, computing resources are being scaled dynamically to minimize energy consumption. We initiate the study of a variant of the classic online speed scaling problem, in which machine learning predictions about the future can be integrated naturally. Inspired by recent work on learning-augmented online algorithms, we propose an algorithm which incorporates predictions in a black-box manner and outperforms any online algorithm if the accuracy is high, yet maintains provable guarantees if the prediction is very inaccurate. We provide both theoretical and experimental evidence to support our claims.