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Online Paging with a Vanishing Regret

Authors Yuval Emek, Shay Kutten, Yangguang Shi

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Author Details

Yuval Emek
  • Faculty of Industrial Engineering and Management, Technion - Israel Institute of Technology, Haifa, Israel
Shay Kutten
  • Faculty of Industrial Engineering and Management, Technion - Israel Institute of Technology, Haifa, Israel
Yangguang Shi
  • Faculty of Industrial Engineering and Management, Technion - Israel Institute of Technology, Haifa, Israel

Funding

  • Emek, Yuval: The work of Yuval Emek was supported in part by an Israeli Science Foundation grant number 1016/17.
  • Kutten, Shay: The work of Shay Kutten was supported in part by a grant from the ministry of science in the program that is joint with JSPS and in part by the BSF.
  • Shi, Yangguang: The work of Yangguang Shi was partially supported at the Technion by a fellowship of the Israel Council for Higher Education.

Cite As Get BibTex

Yuval Emek, Shay Kutten, and Yangguang Shi. Online Paging with a Vanishing Regret. In 12th Innovations in Theoretical Computer Science Conference (ITCS 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 185, pp. 67:1-67:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/LIPIcs.ITCS.2021.67

Abstract

This paper considers a variant of the online paging problem, where the online algorithm has access to multiple predictors, each producing a sequence of predictions for the page arrival times. The predictors may have occasional prediction errors and it is assumed that at least one of them makes a sublinear number of prediction errors in total. Our main result states that this assumption suffices for the design of a randomized online algorithm whose time-average regret with respect to the optimal offline algorithm tends to zero as the time tends to infinity. This holds (with different regret bounds) for both the full information access model, where in each round, the online algorithm gets the predictions of all predictors, and the bandit access model, where in each round, the online algorithm queries a single predictor.
While online algorithms that exploit inaccurate predictions have been a topic of growing interest in the last few years, to the best of our knowledge, this is the first paper that studies this topic in the context of multiple predictors for an online problem with unbounded request sequences. Moreover, to the best of our knowledge, this is also the first paper that aims for (and achieves) online algorithms with a vanishing regret for a classic online problem under reasonable assumptions.

Subject Classification

ACM Subject Classification
  • Theory of computation → Online learning algorithms
  • Theory of computation → Caching and paging algorithms
Keywords
  • online paging
  • inaccurate predictions
  • multiple predictors
  • vanishing regret
  • full information vs. bandit access

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