2020/06/09 by Piotr Indyk, Indyk, Piotr, Frederik Mallmann-Trenn +5
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Auction Theory and Applications #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Optimization and Search Problems
paper · pdf · doi:10.48550/arxiv.2006.05028
openalex publication_date 2020/06/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.