Large-scale IP Traffic Matrix Estimation Based on Backpropagation Neural Network

摘要：

Traffic matrix estimation is significantly important for operators.However,it is difficult to estimate accurately traffic matrix.This paper proposes a novel method of large-scale IP traffic matrix estimation,termed the backpropagation neural network and iterative proportional fitting procedure(BPNNIPFP).Firstly,we model the large-scale IP traffic matrix estimation using the backpropagation neural network(BPNN)that has been studied widely.By training the BPNN,we can build the model of large-scale IP traffic matrix estimation.Secondly,combined with the model and iterative proportional fitting procedure(IPFP),the good estimations of the large-scale IP traffic matrix are attained.Finally,we use the real data from the Abilene network to validate BPNNIPFP.Simulation results show that BPNNIPFP can perform the accurate estimation of large-scale IP traffic matrix,and track well its dynamics.

作者: Dingde Jiang Guangmin Hu

作者单位: Key Lab of Broadband Optical Fiber Transmission and Communication Networks University of Electronic Science and Technology of China,Chengdu,China

会议类型: 国际会议

会议名称: 第一届智能网络与智能系统国际会议(ICINIS 2008)(The First International Conference on Intelligent Networks and Intelligent Systems)

会议地点: 武汉

会议语种:英文

在线出版日期: 2008-11-01（万方平台首次上网日期，不代表论文的发表时间）