mlsanityData to 14 Oct 2026

Learning Augmented Energy Minimization via Speed Scaling

arXiv 2010.11629 ↗First posted 22 Oct 2020cs.LG cs.DScited by 38

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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.

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

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Citing papers per year: 2021: 8; 2022: 11; 2023: 6; 2024: 5; 2025: 5; 2026: 3.

38 papers.

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  1. 2022Permutation Predictions for Non-Clairvoyant Schedulingcited by 22
  2. 2022Online Algorithms with Multiple Predictionscited by 22
  3. 2021Learning-Augmented Dynamic Power Management with Multiple States via New Ski Rental Boundscited by 20
  4. 2021A Novel Prediction Setup for Online Speed-Scalingcited by 17
  5. 2022A Universal Error Measure for Input Predictions Applied to Online Graph Problemscited by 14
  6. 2023Mixing predictions for online metric algorithmscited by 13
  7. 2024Online bipartite matching with imperfect advicecited by 13
  8. 2021Logarithmic Regret from Sublinear Hintscited by 12
  9. 2022A Regression Approach to Learning-Augmented Online Algorithmscited by 12
  10. 2024Algorithms for Caching and MTS with reduced number of predictionscited by 12
  11. 2022Learning-Augmented Algorithms for Online Linear and Semidefinite Programmingcited by 11
  12. 2021Double Coverage with Machine-Learned Advicecited by 10
26 more
  1. 2021Robustification of Online Graph Exploration Methodscited by 10
  2. 2022Smoothed Online Optimization with Unreliable Predictionscited by 10
  3. 2022Paging with Succinct Predictionscited by 10
  4. 2023Active causal structure learning with advicecited by 10
  5. 2022Learning-Augmented Algorithms for Online TSP on the Linecited by 8
  6. 2024Non-clairvoyant Scheduling with Partial Predictionscited by 7
  7. 2021Learning-Augmented Algorithms for Online Steiner Treecited by 6
  8. 2022Scheduling with Speed Predictionscited by 6
  9. 2021Improved Bounds for Online Facility Location with Predictionscited by 5
  10. 2022Contextual Pandora's Boxcited by 5
  11. 2023Online Dynamic Acknowledgement with Learned Predictionscited by 5
  12. 2023The Predicted-Updates Dynamic Model: Offline, Incremental, and Decremental to Fully Dynamic Transformationscited by 4
  13. 2024Learning multivariate Gaussians with imperfect advicecited by 4
  14. 2025Approximate Proportionality in Online Fair Divisioncited by 4
  15. 2021Online Capacity Scaling Augmented With Unreliable Machine Learning Predictionscited by 3
  16. 2023Learning-Augmented Online Packet Scheduling with Deadlinescited by 3
  17. 2023On Optimal Consistency-Robustness Trade-Off for Learning-Augmented Multi-Option Ski Rentalcited by 2
  18. 2025Learning-Augmented Algorithms for MTS with Bandit Access to Multiple Predictorscited by 2
  19. 2026The Secretary Problem with Predictions and a Chosen Ordercited by 2
  20. 2022Online TSP with Predictionscited by 1
  21. 2024Energy-Efficient Scheduling with Predictionscited by 1
  22. 2025Optimizing Health Coverage in Ethiopia: A Learning-augmented Approach and Persistent Proportionality Under an Online Budgetcited by 1
  23. 2025Product distribution learning with imperfect advicecited by 1
  24. 2025A Switching Framework for Online Interval Scheduling with Predictionscited by 0
  25. 2026Learning-Augmented Online Scheduling with Parsimonious Preemptioncited by 0
  26. 2026Learning-Augmented Algorithms: Guarantees, Construction Mechanisms, and System-Level Implicationscited by 0