Hierarchical Edge Caching in Device-to-Device Aided Mobile Networks: Modeling, Optimization, and Design

By Xiuhua Li, Xiaofei Wang, Pengjun Wan, Zhu Han, Victor C. M. Leung. 2018

122 citations recorded by the source corpus.

The explosive growth of content requests from mobile users is stretching the capability of current mobile networking technologies to satisfy users’ demands with acceptable quality of service. An effective approach to address this challenge, which has not yet been thoroughly studied, is to offload network traffic by caching popular content at the edges (e.g., mobile devices and base stations) of mobile networks, thus reducing the massive duplication of content downloads. In this paper, we address the system modeling, large-scale optimization, and framework design of hierarchical edge caching in device-to-device aided mobile networks. In particular, taking into account the analysis of social behavior and preference of mobile users, heterogeneous cache sizes, and the derived system topology, we investigate the maximum capacity of the network infrastructure in terms of offloading network traffic, reducing system costs, and supporting content requests from mobile users locally. Our proposed framework has a low complexity and can be applied in practical engineering implementation. Trace-based simulation results demonstrate the effectiveness of the proposed framework.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Xiaofei Wang
2. Yiwen Han
3. Min Chen
4. Xu Chen
5. Khaled Ben Letaief
6. Jun Zhang
7. Dario Pompili
8. Tuyen X. Tran

In Electrical and Electronic Engineering

1. Min Chen
2. Xu Chen

Citations in the corpus

• In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning (2018, methodology): “We capture Xender’s trace for one month (from 01/09/2016 to 30/09/2016), including 9,514 active mobile users, conveying 188,447 content files, and 2,107,100 content requests [13], of which the content popularity distribution can be well fitted by a Zipf distribution with ∂ = 1.”