基于MGU的大规模IP骨干网络实时流量预测  

Real-time traffic prediction based on MGU for large-scale IP backbone networks

作　　者：郭芳 陈蕾[1,2,3] 杨子文 GUO Fang;CHEN Lei;YANG Ziwen(School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, Jiangsu, China;Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210023, Jiangsu, China;College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu, China)

机构地区：[1]南京邮电大学计算机学院,江苏南京210023 [2]江苏省无线传感网高技术研究重点实验室,江苏南京210023 [3]南京航空航天大学计算机科学与技术学院,江苏南京210016

出　　处：《山东大学学报（工学版）》2019年第2期88-95,共8页Journal of Shandong University（Engineering Science）

基　　金：江苏省自然科学基金(BK20161516);中国博士后科学基金(2015M581794);国家自然科学基金(61872190)

摘　　要：为克服长短时记忆网络(long short-term memory, LSTM)计算成本相当大的弊端,提出基于最小门控单元(minimal gated unit, MGU)的大规模IP骨干网络实时流量预测方法。试验结果表明,与基于LSTM的流量预测方法相比,该方法以较少的模型训练时间获得了相当甚至略优的流量预测性能,在流量预测精度和实时性方面也优于已有的前馈神经网络(feed forward neural network, FFNN)和门控循环单元神经网络(gated recurrent unit, GRU)方法。In order to overcome the shortcomings of long short-term memory(LSTM) computing cost, a real-time traffic prediction method based on minimum gated unit(MGU) for large-scale IP backbone networks was proposed. The experimental results showed that compared with the LSTM-based traffic prediction method, the proposed method achieved fairly or even better traffic prediction performance with less model training time, meanwhile it outperformed the most advanced feed forward neural network(FFNN), LSTM and gated recurrent unit(GRU) in terms of prediction accuracy and real-time performance.

关 键 词：网络流量预测 大规模IP骨干网 循环神经网络 LSTM MGU 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]