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Distributional Online Weighted Paging with Limited Horizon

Authors Yaron Fairstein , Joseph (Seffi) Naor, Tomer Tsachor

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Author Details

Yaron Fairstein
  • Amazon.com, Haifa, Israel
Joseph (Seffi) Naor
  • Computer Science Department, Technion, Haifa, Israel
Tomer Tsachor
  • Computer Science Department, Technion, Haifa, Israel

Funding

  • Naor, Joseph (Seffi): Supported in part by Israel Science Foundation grant 2233/19 and United States - Israel Binational Science Foundation (BSF) grant 2033185.

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Yaron Fairstein, Joseph (Seffi) Naor, and Tomer Tsachor. Distributional Online Weighted Paging with Limited Horizon. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 317, pp. 15:1-15:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2024.15

Abstract

In this work we study the classic problem of online weighted paging with a probabilistic prediction model, in which we are given additional information about the input in the form of distributions over page requests, known as distributional online paging (DOP). This work continues a recent line of research on learning-augmented algorithms that incorporates machine-learning predictions in online algorithms, so as to go beyond traditional worst-case competitive analysis, thus circumventing known lower bounds for online paging. We first provide an efficient online algorithm that achieves a constant factor competitive ratio with respect to the best online algorithm (policy) for weighted DOP that follows from earlier work on the stochastic k-server problem. Our main contribution concerns the question of whether distributional information over a limited horizon suffices for obtaining a constant competitive factor. To this end, we define in a natural way a new predictive model with limited horizon, which we call Per-Request Stochastic Prediction (PRSP). We show that we can obtain a constant factor competitive algorithm with respect to the optimal online algorithm for this model.

Subject Classification

ACM Subject Classification
  • Theory of computation → Caching and paging algorithms
  • Theory of computation → Probabilistic computation
  • Theory of computation → Linear programming
Keywords
  • Online algorithms
  • Caching
  • Stochastic analysis
  • Predictions

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