Proactive Caching at the Edge Leveraging Influential User Detection in Cellular D2D Networks

Anwar Said,Hasan Farooq,Ali Imran,Jon Crowcroft,Syed Shah,Adnan Mian

doi:10.3390/fi10100093

Anwar Said, Hasan Farooq + Show 4 more

Open Access

https://doi.org/10.3390/fi10100093

Copy DOI

Abstract

Caching close to users in a radio access network (RAN) has been identified as a promising method to reduce a backhaul traffic load and minimize latency in 5G and beyond. In this paper, we investigate a novel community detection inspired by a proactive caching scheme for device-to-device (D2D) enabled networks. The proposed scheme builds on the idea that content generated/accessed by influential users is more probable to become popular and thus can be exploited for pro-caching. We use a Clustering Coefficient based Genetic Algorithm (CC-GA) for community detection to discover a group of cellular users present in close vicinity. We then use an Eigenvector Centrality measure to identify the influential users with respect to the community structure, and the content associated to it is then used for pro-active caching using D2D communications. The numerical results show that, compared to reactive caching, where historically popular content is cached, depending on cache size, load and number of requests, up to 30% more users can be satisfied using a proposed scheme while achieving significant reduction in backhaul traffic load.

Highlights

Generational shifts in the world of mobile networks have been driven by the unprecedented growth in mobile data traffic
The results reveal that 48% of backhaul traffic load can be reduced (48% satisfied requests) when users generating requests are 100% using the proposed approach
In order to evaluate the performance of proposed approach, results are compared with a reactive caching approach, in which files are stored in the cache if they were repeatedly requested in the past based on the history of files accessed

Summary

Introduction

Generational shifts in the world of mobile networks have been driven by the unprecedented growth in mobile data traffic. It is predicted that 75% of global mobile data will be video content in which 6.7% will be machine-to-machine (M2M) communication. Such massive growth of multimedia traffic was further fueled by social media feeds, such as Facebook and Twitter (representing 15% of the traffic [1]). This trend is undoubtedly going to stress the capacity of core networks, wireless links and mobile backhauls to their limits eventually leading to poor quality-of-experience (QoE)

Methods

Results

Conclusion