Online Caching with Optimistic Learning

The design of effective online caching policies is an increasingly important problem for content distribution networks, online social networks and edge computing services, among other areas. This paper proposes a new algorithmic toolbox for tackling this problem through the lens of optimistic online...

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Bibliographic Details
Published in:arXiv.org
Main Authors: Mhaisen, Naram, Iosifidis, George, Leith, Douglas
Format: Paper
Language:English
Published: Ithaca Cornell University Library, arXiv.org 20.04.2022
Subjects:
Algorithms
Caching
Decision theory
Design optimization
Distance learning
Edge computing
Machine learning
Policies
Recommender systems
Social networks
ISSN:2331-8422
Online Access:Get full text
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Summary:The design of effective online caching policies is an increasingly important problem for content distribution networks, online social networks and edge computing services, among other areas. This paper proposes a new algorithmic toolbox for tackling this problem through the lens of optimistic online learning. We build upon the Follow-the-Regularized-Leader (FTRL) framework which is developed further here to include predictions for the file requests, and we design online caching algorithms for bipartite networks with fixed-size caches or elastic leased caches subject to time-average budget constraints. The predictions are provided by a content recommendation system that influences the users viewing activity, and hence can naturally reduce the caching network's uncertainty about future requests. We prove that the proposed optimistic learning caching policies can achieve sub-zero performance loss (regret) for perfect predictions, and maintain the best achievable regret bound \(O(\sqrt T)\) even for arbitrary-bad predictions. The performance of the proposed algorithms is evaluated with detailed trace-driven numerical tests.
Bibliography:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
ISSN:2331-8422
DOI:10.48550/arxiv.2202.10590