基于回声状态网络的移动边缘缓存网络内容预测

Zengyu Cai, Xi Chen, Jianwei Zhang, Liang Zhu, Xinhua Hu
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引用次数: 0

摘要

随着互联网通信的快速发展和智能终端的广泛应用,将缓存移动到网络边缘是缩短用户访问内容延迟的有效解决方案。然而,现有的缓存工作缺乏对用户和内容的综合考虑,导致整个系统的缓存命中率低,准确率低。本文提出了一种同时考虑用户请求内容和内容预测的协同缓存模型,以提高整个网络的缓存性能。该模型首先采用基于Akike信息准则的聚类算法对用户进行聚类;然后,结合聚类结果,将回声状态网络作为机器学习框架进行内容预测。最后根据预测结果选择缓存内容,缓存到小型基站的缓存单元中。仿真结果表明,与现有的缓存算法相比,该方法在缓存命中率、准确率和查全率等方面都有明显提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Echo State Network-Based Content Prediction for Mobile Edge Caching Networks
With the rapid development of internet communication and the wide application of intelligent terminal, moving the cache to the edge of the network is an effective solution to shorten the delay of users accessing content. However, the existing cache work lacks the comprehensive consideration of users and content, resulting in low cache hit ratio and low accuracy of the whole system. In this paper, the authors propose a collaborative caching model that considers both user request content and content prediction, so as to improve the caching performance of the whole network. Firstly, the model uses the clustering algorithm based on Akike information criterion to cluster users. Then, combined with the clustering results, echo state network is used as the machine learning framework to predict the content. Finally, the cache contents are selected according to the prediction results and cached in the cache unit of the small base station. Simulation results show that compared with the existing cache algorithms, the proposed method has obvious improvement in cache hit ratio, accuracy, and recall rate.
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