DCoL: Distributed Collaborative Learning for Proactive Content Caching at Edge Networks

Caching popular content at the network edge, such as roadside units (RSUs), is a promising solution that enhances the user's quality-of-experience (QoE) and reduces network traffic. In this regard, the most challenging issue is to correctly predict the future popularity of contents and effectively store them in the cache of edge nodes. Thus, in this paper, we propose a distributed proactive caching scheme at the edge to optimize the content retrieval cost and improve the QoE of the mobile users. This proactive content caching scheme, namely Distributed Collaborative Learning (DCoL), is a non-parametric content popularity prediction mechanism in a distributed setting. Next, we show the advantage of DCoL as two folds: (i) it leverages distributed content popularity information to develop local content caching strategy, and (ii) it exploits the regional database using the long short-term memory (LSTM)-based prediction model to capture the dependency between requested contents. Simulation results using real datasets demonstrate that our scheme yields 8.9% and 18% gains, respectively, in terms of the cache hit efficiency and content retrieval cost, compared with a competitive centralized baseline, and outperforms other traditional caching strategies.

Automated summary

This paper proposes a distributed proactive caching scheme DCoL to optimize content retrieval cost and improve user experience quality. By leveraging distributed content popularity information and a regional database based on LSTM model, the scheme shows significant gains in cache hit efficiency and content retrieval cost compared to centralized baseline and traditional caching strategies.