The Exact Rate-Memory Tradeoff for Caching with Uncoded Prefetching

TL;DR

This paper precisely characterizes the optimal rate-memory tradeoff in cache networks with uncoded prefetching, introducing a novel scheme that exploits demand commonality and proving its optimality.

Contribution

It provides the first exact rate-memory tradeoff characterization for uncoded prefetching, including a new caching scheme and its optimality proof, applicable to both centralized and decentralized settings.

Findings

Proposed a caching scheme that exploits demand commonality.

Established the exact optimal rate-memory tradeoff for uncoded prefetching.

Demonstrated the scheme's universal optimality across demand types.

Abstract

We consider a basic cache network, in which a single server is connected to multiple users via a shared bottleneck link. The server has a database of files (content). Each user has an isolated memory that can be used to cache content in a prefetching phase. In a following delivery phase, each user requests a file from the database, and the server needs to deliver users' demands as efficiently as possible by taking into account their cache contents. We focus on an important and commonly used class of prefetching schemes, where the caches are filled with uncoded data. We provide the exact characterization of the rate-memory tradeoff for this problem, by deriving both the minimum average rate (for a uniform file popularity) and the minimum peak rate required on the bottleneck link for a given cache size available at each user. In particular, we propose a novel caching scheme, which strictly improves the state of the art by exploiting commonality among user demands. We then demonstrate the exact optimality of our proposed scheme through a matching converse, by dividing the set of all demands into types, and showing that the placement phase in the proposed caching scheme is universally optimal for all types. Using these techniques, we also fully characterize the rate-memory tradeoff for a decentralized setting, in which users fill out their cache content without any coordination.