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Distributional Online Weighted Paging with Limited Horizon

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

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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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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.

Cite As Get BibTex

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

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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