Sharing LRU Cache Resources among Content Providers: A Utility-Based Approach

TL;DR

This paper introduces a utility-based approach for allocating cache resources among multiple content providers, optimizing cache partitioning to improve hit rates and overall utility in various content overlap scenarios.

Contribution

It proposes a novel utility-driven cache partitioning method with online algorithms that dynamically optimize resource allocation among content providers.

Findings

Cache partitioning outperforms sharing as cache size grows.

Separate partitions for overlapped content can be beneficial.

Online algorithms effectively maximize utility.

Abstract

In this paper, we consider the problem of allocating cache resources among multiple content providers. The cache can be partitioned into slices and each partition can be dedicated to a particular content provider, or shared among a number of them. It is assumed that each partition employs the LRU policy for managing content. We propose utility-driven partitioning, where we associate with each content provider a utility that is a function of the hit rate observed by the content provider. We consider two scenarios: i)~content providers serve disjoint sets of files, ii)~there is some overlap in the content served by multiple content providers. In the first case, we prove that cache partitioning outperforms cache sharing as cache size and numbers of contents served by providers go to infinity. In the second case, It can be beneficial to have separate partitions for overlapped content. In the case of two providers, it is usually always beneficial to allocate a cache partition to serve all overlapped content and separate partitions to serve the non-overlapped contents of both providers. We establish conditions when this is true asymptotically but also present an example where it is not true asymptotically. We develop online algorithms that dynamically adjust partition sizes in order to maximize the overall utility and prove that they converge to optimal solutions, and through numerical evaluations, we show they are effective.