Caching with Reserves

TL;DR

This paper introduces two generalized caching models for multi-user or multi-processor systems, proves their NP-hardness, and provides approximation and online algorithms with provable guarantees.

Contribution

It defines caching with reserves and public-private caching models, establishes their equivalence, and develops approximation and online algorithms for these complex caching problems.

Findings

Caching with reserves is NP-hard offline.

A 2-approximation algorithm is designed for offline caching.

An O(ln k)-competitive online fractional algorithm is developed.

Abstract

Caching is a crucial component of many computer systems, so naturally it is a well-studied topic in algorithm design. Much of traditional caching research studies cache management for a single-user or single-processor environment. In this paper, we propose two related generalizations of the classical caching problem that capture issues that arise in a multi-user or multi-processor environment. In the caching with reserves problem, a caching algorithm is required to maintain at least $k_i$ pages belonging to user $i$ in the cache at any time, for some given reserve capacities $k_i$. In the public-private caching problem, the cache of total size $k$ is partitioned into subcaches, a private cache of size $k_i$ for each user $i$ and a shared public cache usable by any user. In both of these models, as in the classical caching framework, the objective of the algorithm is to dynamically maintain the cache so as to minimize the total number of cache misses. We show that caching with reserves and public-private caching models are equivalent up to constant factors, and thus focus on the former. Unlike classical caching, both of these models turn out to be NP-hard even in the offline setting, where the page sequence is known in advance. For the offline setting, we design a 2-approximation algorithm, whose analysis carefully keeps track of a potential function to bound the cost. In the online setting, we first design an $O(\ln k)$-competitive fractional algorithm using the primal-dual framework, and then show how to convert it online to a randomized integral algorithm with the same guarantee.