# Online Capacity Scaling Augmented With Unreliable Machine Learning Predictions

arXiv: https://arxiv.org/abs/2101.12160 · HTML: https://mlsanity.com/paper/2101.12160/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 59 references that are in the map.
- First posted on arXiv 28 Jan 2021.

## Builds on (most cited first, 9 of 9)

- [Competitive caching with machine learned advice](https://mlsanity.com/paper/1802.05399/) (2018)
- [Online metric algorithms with untrusted predictions](https://mlsanity.com/paper/2003.02144/) (2020)
- [A Model for Learned Bloom Filters, and Optimizing by Sandwiching](https://mlsanity.com/paper/1901.00902/) (2019)
- [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)
- [On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression](https://mlsanity.com/paper/2008.07281/) (2020)
- [Semi-Online Bipartite Matching](https://mlsanity.com/paper/1812.00134/) (2018)
- [The Supermarket Model with Known and Predicted Service Times](https://mlsanity.com/paper/1905.12155/) (2019)
- [Learning-Assisted Competitive Algorithms for Peak-Aware Energy Scheduling](https://mlsanity.com/paper/1911.07972/) (2019)

## Built on by (most cited first, 3 of 3)

- [Smoothed Online Optimization with Unreliable Predictions](https://mlsanity.com/paper/2202.03519/) (2022)
- [Expert-Calibrated Learning for Online Optimization with Switching Costs](https://mlsanity.com/paper/2204.08572/) (2022)
- [Robust Learning for Smoothed Online Convex Optimization with Feedback Delay](https://mlsanity.com/paper/2310.20098/) (2023)

## Abstract (arXiv)

Modern data centers suffer from immense power consumption. As a result, data center operators have heavily invested in capacity scaling solutions, which dynamically deactivate servers if the demand is low and activate them again when the workload increases. We analyze a continuous-time model for capacity scaling, where the goal is to minimize the weighted sum of flow-time, switching cost, and power consumption in an online fashion. We propose a novel algorithm, called Adaptive Balanced Capacity Scaling (ABCS), that has access to black-box machine learning predictions. ABCS aims to adapt to the predictions and is also robust against unpredictable surges in the workload. In particular, we prove that ABCS is (1+varepsilon)-competitive if the predictions are accurate, and yet, it has a uniformly bounded competitive ratio even if the predictions are completely inaccurate. Finally, we investigate the performance of this algorithm on a real-world dataset and carry out extensive numerical experiments, which positively support the theoretical results.