Online Page Migration with ML Advice

Piotr Indyk, Frederik Mallmann-Trenn, Slobodan Mitrovic, Ronitt Rubinfeld
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, PMLR 151:1655-1670, 2022.

Abstract

We consider online algorithms for the 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 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v151-indyk22a, title = { Online Page Migration with ML Advice }, author = {Indyk, Piotr and Mallmann-Trenn, Frederik and Mitrovic, Slobodan and Rubinfeld, Ronitt}, booktitle = {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics}, pages = {1655--1670}, year = {2022}, editor = {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel}, volume = {151}, series = {Proceedings of Machine Learning Research}, month = {28--30 Mar}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v151/indyk22a/indyk22a.pdf}, url = {https://proceedings.mlr.press/v151/indyk22a.html}, abstract = { We consider online algorithms for the 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 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. } }
Endnote
%0 Conference Paper %T Online Page Migration with ML Advice %A Piotr Indyk %A Frederik Mallmann-Trenn %A Slobodan Mitrovic %A Ronitt Rubinfeld %B Proceedings of The 25th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2022 %E Gustau Camps-Valls %E Francisco J. R. Ruiz %E Isabel Valera %F pmlr-v151-indyk22a %I PMLR %P 1655--1670 %U https://proceedings.mlr.press/v151/indyk22a.html %V 151 %X We consider online algorithms for the 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 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.
APA
Indyk, P., Mallmann-Trenn, F., Mitrovic, S. & Rubinfeld, R.. (2022). Online Page Migration with ML Advice . Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 151:1655-1670 Available from https://proceedings.mlr.press/v151/indyk22a.html.

Related Material