Optimization of Caching Devices with Geometric Constraints

TL;DR

This paper models and optimizes geographically distributed caching in wireless networks using stochastic geometry, demonstrating that average capacity constraints outperform per cache constraints and proposing a near-optimal LRU-based caching policy.

Contribution

It introduces a stochastic geometry model for cache placement, compares capacity constraints, and proposes a practical LRU-based caching policy with near-optimal performance.

Findings

Average capacity constraints reduce cache miss probability more than per cache constraints.

Convex optimization effectively solves cache allocation under average constraints.

LRU-based policy performs close to the optimal solution.

Abstract

It has been recently advocated that in large communication systems it is beneficial both for the users and for the network as a whole to store content closer to users. One particular implementation of such an approach is to co-locate caches with wireless base stations. In this paper we study geographically distributed caching of a fixed collection of files. We model cache placement with the help of stochastic geometry and optimize the allocation of storage capacity among files in order to minimize the cache miss probability. We consider both per cache capacity constraints as well as an average capacity constraint over all caches. The case of per cache capacity constraints can be efficiently solved using dynamic programming, whereas the case of the average constraint leads to a convex optimization problem. We demonstrate that the average constraint leads to significantly smaller cache miss probability. Finally, we suggest a simple LRU-based policy for geographically distributed caching and show that its performance is close to the optimal.