基于实测数据集预测用户请求行为对主动边缘缓存的影响  

Impacts of Learning User Request Behavior with a Real Dataset on Proactive Wireless-edge Caching

作　　者：戚凯强 杨晨阳[1] 韩圣千[1] Qi Kaiqiang;Yang Chenyang;Han Shengqian(School of Electronic and Information Engineering,Beihang University,Beijing 100191,China)

机构地区：[1]北京航空航天大学电子信息工程学院

出　　处：《信号处理》2019年第4期531-541,共11页Journal of Signal Processing

基　　金：教育部-中国移动科研基金项目(1-4 MCM2017);国家自然科学基金重点项目(61731002)

摘　　要：由于无线边缘节点的缓存空间很小,在流行度已知时主动缓存策略的性能远优于被动缓存。最近,业界开始研究在文件流行度等用户请求行为未知、需要进行预测时的主动边缘缓存,发现主动缓存依然优于被动缓存。然而,大多数工作基于合成的数据集或者在推荐系统等领域采集的开源数据集,难以反映无线用户的请求行为。本文采用一个在局部区域每秒记录用户请求视频次数的实测数据集、利用神经网络预测用户在未来短期内的个体和群体行为,基于预测的用户行为信息在宏基站或微基站进行主动缓存。研究结果表明,当采用实测数据集时,由于用户请求行为具有很强的时间局部性、甚至是猝发性,所造成的虚警、漏警和加性误差使被动缓存优于主动缓存、且在宏基站缓存时增益更大;一旦采用合成的静态数据集,主动缓存明显优于被动缓存。这意味着不能仅用加性误差刻画预测流行度的不确定性,要实现主动边缘缓存的性能增益,更重要的是降低虚警和漏警。Since the wireless edge node has a small cache size,the performance of proactive caching is much better than passive caching when file popularity is known.Recent studies in the scenario where file popularity is unknown and need to be predicted found that proactive caching is still better than passive caching.However,most of the work is based on synthesized data sets or open source datasets collected in areas such as recommendation systems,which can barely reflect the request behavior of wireless users.In this paper,a dataset recording the number of requests for videos measured in a local area is used,based on which a neural network is employed to predict the short-term behaviors of individual and group user.The predicted user behavior information is then applied for proactive caching at macro or micro base stations.The research results show that when the measured dataset is adopted,the false alarm,missing alarm and additive errors,which are caused by strong temporal locality or even burstiness of user request behavior,make passive caching outperform proactive caching,especially for caching at macro base station.Once a synthesized static dataset is used,proactive caching performs significantly better than passive caching.This means that it is not sufficient to use only additive errors to characterize the uncertainty of popularity prediction,while reducing false and missing alarms is more important in order to achieve the performance gain of proactive edge caching.

关 键 词：实测数据集 用户请求行为 文件流行度 神经网络 

分 类 号：TN929.53[电子电信—通信与信息系统]