Original scientific paper

https://doi.org/10.17559/TV-20190303092125

Cooperative Caching with Content Popularity Prediction for Mobile Edge Caching

Sanshan Sun ; National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, China
Wei Jiang orcid.org/0000-0003-2962-4709 ; National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, China
Gang Feng ; National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, China
Shuang Qin ; National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, China
Ye Yuan ; School of Economics and Management, Chongqing University of Posts and Telecommunications, Chongqing, China

Abstract

Mobile Edge Caching (MEC) can be exploited for reducing redundant data transmissions and improving content delivery performance in mobile networks. However, under the MEC architecture, dynamic user preference is challenging the delivery efficiency due to the imperfect match between users' demands and cached content. In this paper, we propose a learning-based cooperative content caching policy to predict the content popularity and cache the desired content proactively. We formulate the optimal cooperative content caching problem as a 0-1 integer programming for minimizing the average downloading latency. After using an artificial neural network to learn content popularity, we use a greedy algorithm for its approximate solution. Numerical results validate that the proposed policy can significantly increase content cache hit rate and reduce content delivery latency when compared with popular caching strategies.

Keywords

caching; cooperative; mobile edge caching; neural network

Hrčak ID:

219543

URI

https://hrcak.srce.hr/219543

Publication date:

24.4.2019.

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