Paging with Succinct Predictions
Antonios Antoniadis, Joan Boyar, Marek Eli\'a\v{s}, Lene M. Favrholdt,, Ruben Hoeksma, Kim S. Larsen, Adam Polak, Bertrand Simon

TL;DR
This paper introduces one-bit prediction models for paging algorithms, designing algorithms that are consistent, robust, and smooth, and establishing their near-optimality in this minimal prediction setting.
Contribution
It proposes novel one-bit prediction schemes for paging, along with algorithms that achieve all key properties and matching lower bounds, advancing learning-augmented paging theory.
Findings
Algorithms are consistent, robust, and smooth with one-bit predictions.
Matching lower bounds show near-optimality of the algorithms.
Two natural prediction models are effectively utilized.
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 -- a recent line of research that aims to ameliorate the shortcomings of classical worst-case analysis by giving algorithms access to predictions. Such predictions can typically be generated using a machine learning approach, but they are inherently imperfect. 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.…
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Taxonomy
TopicsOptimization and Search Problems · Advanced Bandit Algorithms Research · Age of Information Optimization
