Content Placement in Networks of Similarity Caches

TL;DR

This paper explores content placement strategies in similarity caching networks, proposing polynomial algorithms for discrete cases and a continuous formulation for large content spaces, validated through synthetic and real data.

Contribution

It introduces the novel concept of similarity caching networks, analyzes the NP-hard offline placement problem, and proposes scalable solutions for large content spaces.

Findings

Polynomial algorithms approach optimal placement in discrete cases.

A continuous problem formulation reveals simple solutions in tree topologies.

Validation with synthetic and real request traces confirms effectiveness.

Abstract

Similarity caching systems have recently attracted the attention of the scientific community, as they can be profitably used in many application contexts, like multimedia retrieval, advertising, object recognition, recommender systems and online content-match applications. In such systems, a user request for an object $o$, which is not in the cache, can be (partially) satisfied by a similar stored object $o$', at the cost of a loss of user utility. In this paper we make a first step into the novel area of similarity caching networks, where requests can be forwarded along a path of caches to get the best efficiency-accuracy tradeoff. The offline problem of content placement can be easily shown to be NP-hard, while different polynomial algorithms can be devised to approach the optimal solution in discrete cases. As the content space grows large, we propose a continuous problem formulation whose solution exhibits a simple structure in a class of tree topologies. We verify our findings using synthetic and realistic request traces.