Gibbsian On-Line Distributed Content Caching Strategy for Cellular Networks

TL;DR

This paper introduces a Gibbs sampling-based distributed algorithm for optimal content placement in cellular networks, which adapts to unknown content popularities and topology, significantly improving cache hit rates.

Contribution

It presents a novel Gibbs sampling approach for distributed content caching that converges to optimal placement and adapts to unknown network parameters.

Findings

Algorithm converges to optimal content placement

Significant improvement in cache hit rate demonstrated

Method adapts to unknown content popularities and topology

Abstract

We develop Gibbs sampling based techniques for learning the optimal content placement in a cellular network. A collection of base stations are scattered on the space, each having a cell (possibly overlapping with other cells). Mobile users request for downloads from a finite set of contents according to some popularity distribution. Each base station can store only a strict subset of the contents at a time; if a requested content is not available at any serving base station, it has to be downloaded from the backhaul. Thus, there arises the problem of optimal content placement which can minimize the download rate from the backhaul, or equivalently maximize the cache hit rate. Using similar ideas as Gibbs sampling, we propose simple sequential content update rules that decide whether to store a content at a base station based on the knowledge of contents in neighbouring base stations. The update rule is shown to be asymptotically converging to the optimal content placement for all nodes. Next, we extend the algorithm to address the situation where content popularities and cell topology are initially unknown, but are estimated as new requests arrive to the base stations. Finally, improvement in cache hit rate is demonstrated numerically.