Collective behavior aware collaborative caching for mobile edge computing

Abstract

In Mobile Edge Computing (MEC) paradigm, popular and repetitive content can be cached and offloaded from nearby MEC server in order to reduce the backhaul overload. Due to hardware limitation of MEC devices, collaboration among MEC servers can greatly improve the cache performance. In this paper, we propose a Collective Behavior aware Collaborative Caching (CBCC) method. At first, we propose to discover the collective behavior of users by using content-location similarity network fusion algorithm. our analysis is based on real dataset of usage detail records and explore the heterogeneity and predictability of collective behavior during content access. Based on it, we propose a collaborative relationship model that relies on the collective behavior. Then, the collaborative caching placement is formulated by solving a multi-objective optimization problem. Our simulations are based on the real dataset from cellular systems. The numerical results show that the proposed method achieves performance gains in terms of both hit rate and transmission cost.