Proceedings ArticleDOI
Fundamental Limits on the Regret of Online Network-Caching
Rajarshi Bhattacharjee,Subhankar Banerjee,Abhishek Sinha +2 more
- Vol. 4, Iss: 1, pp 15-16
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TLDR
The question of the optimality of a Follow-the-Perturbed-Leader-based uncoded caching policy with near-optimal regret in two settings is resolved by deriving tight non-asymptotic regret lower bounds in the above settings.Abstract:
Optimal caching of files in a content distribution network (CDN) is a problem of fundamental and growing commercial interest. Although many different caching algorithms are in use today, the fundamental performance limits of network caching algorithms from an online learning point-of-view remain poorly understood to date. In this paper, we resolve this question in the following two settings: (1) a single user connected to a single cache, and (2) a set of users and a set of caches interconnected through a bipartite network. Recently, an online gradient-based coded caching policy was shown to enjoy sub-linear regret. However, due to the lack of known regret lower bounds, the question of the optimality of the proposed policy was left open. In this paper, we settle this question by deriving tight non-asymptotic regret lower bounds in both of the above settings. In addition to that, we propose a new Follow-the-Perturbed-Leader-based uncoded caching policy with near-optimal regret. Technically, the lower-bounds are obtained by relating the online caching problem to the classic probabilistic paradigm of balls-into-bins. Our proofs make extensive use of a new result on the expected load in the most populated half of the bins, which might also be of independent interest. We evaluate the performance of the caching policies by experimenting with the popular MovieLens dataset and conclude the paper with design recommendations and a list of open problems.read more
Citations
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Proceedings ArticleDOI
No-Regret Caching via Online Mirror Descent
TL;DR: In this article, the authors study an online caching problem in which requests can be served by a local cache to avoid retrieval costs from a remote server, where the cache can update its state after a batch of requests and store an arbitrarily small fraction of each content.
Proceedings ArticleDOI
Online Caching with Optimal Switching Regret
TL;DR: In this paper, the authors consider the classical uncoded caching problem from an online learning point-of-view and design a caching policy that incurs minimal regret while considering both the rewards due to cache-hits and the switching cost due to file fetches.
Journal ArticleDOI
Asymptotically Optimal Online Caching on Multiple Caches With Relaying and Bypassing
TL;DR: An O(logK)-competitive randomized online multiple caching algorithm ( named Camul) and an O(K)- competitive deterministic algorithm (named Camul-Det) that achieve asymptotically optimal competitive ratios and can be implemented efficiently such that each request is processed in amortized constant time.
Proceedings ArticleDOI
Optimistic No-regret Algorithms for Discrete Caching
TL;DR: The first comprehensive optimistic Follow-the-Perturbed leader policy is designed, to the best of the knowledge, which generalizes beyond the caching problem and offers sublinear regret bounds commensurate with the accuracy of the oracle predictions.
Journal ArticleDOI
Caching in Dynamic Environments: A Near-Optimal Online Learning Approach
TL;DR: Wang et al. as mentioned in this paper designed an online optimization framework, which aims to minimize the http://www.w3.org/1998/Math/MathML" XMLns:xlink="http:// www.w 3.org.1999/xlink">sublinear dynamic regret, from which it is guaranteed to be nearly optimal.
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