Paging with Succinct Predictions
Antonios Antoniadis, Joan Boyar, Marek Elias, Lene M. Favrholdt, Ruben Hoeksma, Kim S. Larsen, Adam Polak, Bertrand Simon
Abstract
Paging is a prototypical problem in the area of online algorithms. It has also played a central role in the development of learning-augmented algorithms. Previous work on learning-augmented paging has investigated predictions on (i) when the current page will be requested again (reoccurrence predictions), (ii) the current state of the cache in an optimal algorithm (state predictions), (iii) all requests until the current page gets requested again, and (iv) the relative order in which pages are requested. We study learning-augmented paging from the new perspective of requiring the least possible amount of predicted information. More specifically, the predictions obtained alongside each page request are limited to one bit only. We develop algorithms satisfy all three desirable properties of learning-augmented algorithms -- that is, they are consistent, robust and smooth -- despite being limited to a one-bit prediction per request. We also present lower bounds establishing that our algorithms are essentially best possible.
BibTeX
@inproceedings{icml2023_pagingwithsuccin,
title = {Paging with Succinct Predictions},
author = {Antonios Antoniadis and Joan Boyar and Marek Elias and Lene M. Favrholdt and Ruben Hoeksma and Kim S. Larsen and Adam Polak and Bertrand Simon},
booktitle = {ICML 2023},
year = {2023}
}