Fundamental Limits of Online Network-Caching

TL;DR

This paper investigates the fundamental performance limits of online network caching algorithms, deriving tight regret bounds and proposing near-optimal policies for different network settings, with practical evaluations on real data.

Contribution

It provides the first tight regret lower bounds for online network caching and introduces a new near-optimal caching policy based on Follow-the-Perturbed-Leader.

Findings

Proved tight non-asymptotic regret lower bounds for caching.

Proposed a new caching policy with near-optimal regret.

Validated policies using MovieLens dataset experiments.

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.