Online Caching with Optimal Switching Regret

TL;DR

This paper introduces an online caching policy that optimally balances cache-hit rewards and switching costs, achieving order-optimal regret bounds and improving existing regret bounds significantly.

Contribution

It provides the first switching regret analysis for an online caching policy based on Follow the Perturbed Leader, with improved bounds over previous results.

Findings

The proposed policy achieves order-optimal switching regret.

The regret bound is improved by a factor of a0(\u221a C) over previous bounds.

Performance comparison on real CDN data demonstrates effectiveness.

Abstract

We consider the classical uncoded caching problem from an online learning point-of-view. A cache of limited storage capacity can hold $C$ files at a time from a large catalog. A user requests an arbitrary file from the catalog at each time slot. Before the file request from the user arrives, a caching policy populates the cache with any $C$ files of its choice. In the case of a cache-hit, the policy receives a unit reward and zero rewards otherwise. In addition to that, there is a cost associated with fetching files to the cache, which we refer to as the switching cost. The objective is to design a caching policy that incurs minimal regret while considering both the rewards due to cache-hits and the switching cost due to the file fetches. The main contribution of this paper is the switching regret analysis of a Follow the Perturbed Leader-based anytime caching policy, which is shown to have an order optimal switching regret. In this pursuit, we improve the best-known switching regret bound for this problem by a factor of $\Theta(\sqrt{C}).$ We conclude the paper by comparing the performance of different popular caching policies using a publicly available trace from a commercial CDN server.