Placing Dynamic Content in Caches with Small Population

TL;DR

This paper introduces a new caching framework for wireless networks with small populations, using an age-based policy and cluster coordination to improve hit rates and address challenges of dynamic content popularity estimation.

Contribution

It proposes an Age-Based Threshold policy for single caches and a cluster-based coordination mechanism to enhance cache performance in small population settings.

Findings

ABT policy is asymptotically optimal in many contents regime.

Global cache aggregation speeds up learning by a factor of L.

Coordination mechanisms improve hit rates despite traffic correlation issues.

Abstract

This paper addresses a fundamental limitation for the adoption of caching for wireless access networks due to small population sizes. This shortcoming is due to two main challenges: (i) making timely estimates of varying content popularity and (ii) inferring popular content from small samples. We propose a framework which alleviates such limitations. To timely estimate varying popularity in a context of a single cache we propose an Age-Based Threshold (ABT) policy which caches all contents requested more times than a threshold $\widetilde N(\tau)$, where $\tau$ is the content age. We show that ABT is asymptotically hit rate optimal in the many contents regime, which allows us to obtain the first characterization of the optimal performance of a caching system in a dynamic context. We then address small sample sizes focusing on $L$ local caches and one global cache. On the one hand we show that the global cache learns L times faster by aggregating all requests from local caches, which improves hit rates. On the other hand, aggregation washes out local characteristics of correlated traffic which penalizes hit rate. This motivates coordination mechanisms which combine global learning of popularity scores in clusters and LRU with prefetching.