Fundamental Limits of Caching

TL;DR

This paper introduces a new information-theoretic framework for caching that leverages both local and global cache sizes, proposing a novel coded caching scheme that significantly improves peak network rates.

Contribution

It presents a new caching scheme exploiting both local and global cache gains, achieving near-optimal performance with a multiplicative rate improvement.

Findings

The proposed scheme outperforms previous methods by a factor proportional to the number of users.

The scheme's performance is within a constant factor of the theoretical optimum.

Exploiting global cache size yields substantial improvements in peak network rates.

Abstract

Caching is a technique to reduce peak traffic rates by prefetching popular content into memories at the end users. Conventionally, these memories are used to deliver requested content in part from a locally cached copy rather than through the network. The gain offered by this approach, which we term local caching gain, depends on the local cache size (i.e, the memory available at each individual user). In this paper, we introduce and exploit a second, global, caching gain not utilized by conventional caching schemes. This gain depends on the aggregate global cache size (i.e., the cumulative memory available at all users), even though there is no cooperation among the users. To evaluate and isolate these two gains, we introduce an information-theoretic formulation of the caching problem focusing on its basic structure. For this setting, we propose a novel coded caching scheme that exploits both local and global caching gains, leading to a multiplicative improvement in the peak rate compared to previously known schemes. In particular, the improvement can be on the order of the number of users in the network. Moreover, we argue that the performance of the proposed scheme is within a constant factor of the information-theoretic optimum for all values of the problem parameters.