# Online Page Migration with ML Advice

arXiv: https://arxiv.org/abs/2006.05028 · HTML: https://mlsanity.com/paper/2006.05028/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2020.

## Built on by (most cited first, 8 of 8)

- [Learning Predictions for Algorithms with Predictions](https://mlsanity.com/paper/2202.09312/) (2022)
- [Algorithms for Caching and MTS with reduced number of predictions](https://mlsanity.com/paper/2404.06280/) (2024)
- [Algorithm Configuration for Structured Pfaffian Settings](https://mlsanity.com/paper/2409.04367/) (2024)
- [Learning-Augmented Algorithms for Online Steiner Tree](https://mlsanity.com/paper/2112.05353/) (2021)
- [Learning-Augmented Private Algorithms for Multiple Quantile Release](https://mlsanity.com/paper/2210.11222/) (2022)
- [The Predicted-Updates Dynamic Model: Offline, Incremental, and Decremental to Fully Dynamic Transformations](https://mlsanity.com/paper/2307.08890/) (2023)
- [Incremental Approximate Single-Source Shortest Paths with Predictions](https://mlsanity.com/paper/2502.08125/) (2025)
- [Solving the Online Assignment Problem with Machine Learned Advice](https://mlsanity.com/paper/2208.04016/) (2022)

## Abstract (arXiv)

We consider online algorithms for the em page migration problem that use predictions, potentially imperfect, to improve their performance. The best known online algorithms for this problem, due to Westbrook'94 and Bienkowski et al'17, have competitive ratios strictly bounded away from 1. In contrast, we show that if the algorithm is given a prediction of the input sequence, then it can achieve a competitive ratio that tends to 1 as the prediction error rate tends to 0. Specifically, the competitive ratio is equal to 1+O(q), where q is the prediction error rate. We also design a ``fallback option'' that ensures that the competitive ratio of the algorithm for em any input sequence is at most O(1/q). Our result adds to the recent body of work that uses machine learning to improve the performance of ``classic'' algorithms.