Content Placement in Networks of Similarity Caches

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.

Online Learning for Network Resource Allocation

“…Finally, ML model allocation in an IDN is closely related to similarity caching considered in Chapter 3. To the best of our knowledge, the literature on similarity caching has restricted itself to (1) a single cache (except for [110,168,223]), and (2) homogeneous items with identical resource requirements. A consequence is that in our setting similarity caching policies would only allocate models based on their accuracy, ignoring the trade-offs imposed by their resource requirements.…”