Online Page Migration with ML Advice
This work addresses performance improvement for classic algorithms in computer science, specifically for online page migration, by integrating ML advice, though it is incremental as it builds on prior competitive analysis frameworks.
The paper tackles the online page migration problem by using machine learning predictions to improve algorithm performance, achieving a competitive ratio of 1+O(q) where q is the prediction error rate, and ensures a fallback with a ratio of O(1/q) for any input.
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.