Social-Aware Graph-Based Collaborative Caching in Edge-User Networks

Abstract

The soaring data traffic brings tremendous pressure on core networks. Collaborative caching among edge servers and user devices has been regarded as a promising technology for releasing backhaul pressure and reducing content download delay in the next-generation communication network. However, given the capacity constraint of edge servers and caching devices, a challenge is how to exploit these storage resources efficiently. In this paper, we introduce a three-tier collaborative caching architecture for edge-user networks and propose a social-aware graph-based collaborative caching (SGCC) strategy to minimize content download delay. Specifically, the small base station tier and user equipment tier are modeled as a one-tier directed graph. Moreover, we model the physical attributes and social attributes as the weight of the directed edges in the physical link graph and social graph, respectively. Then, we construct a social-aware graph by merging the two-tier topology of the physical link graph and social graph to simplify the caching decision. Further, we design SGCC to cache the most popular content according to the weighted content popularity. Simulation results demonstrate that the proposed SGCC can effectively reduce the average download delay and has a 26.8% lower average download delay, compared with the uniform popular caching baseline.