大尺度IP骨干网络流量矩阵估计方法研究

流量矩阵估计是当前的热点研究问题,它被网络操作员用来进行负载均衡、路由最优化、流量侦测、网络规划等等.然而,流量矩阵估计本身固有的高度病态特性,使得精确地估计流量矩阵成为具有挑战性的研究课题.本文研究大尺度IP骨干网络的流量矩阵估计;基于RBF(Radial Basis Function)神经网络,提出一种新的估计方法...

Approach of Traffic Matrix Estimation in Large-scale IP Backbone Networks

Traffic matrix estimation is an interesting research problem at present.Network operators use it to conduct load balancing,route optimization,traffic detecting,network dimensioning and so on.However,the highly ill-pose nature of traffic matrix estimation itself makes it being a challenging research subject to estimate accurately traffic matrix.This paper studies traffic matrix estimation in large-scale IP backbone networks.Based on RBF (radial basis function) neural network,a novel estimation method,namely TMRI (traffic matrix recurrence inference),is proposed.TMRI exploits the powerful modeling ability of RBF neural network to model traffic matrix estimation.The ill-pose nature of this problem will be overcome in the process of training the RBF neural network.Accordingly,the complex process of mathematic modeling can be avoided.Built on this estimation model,traffic matrix estimation is described into the optimal process under the constraints.By seeking the recurrent optimal solution,TMRI can further get rid of the ill-pose nature of this problem.Simulation results show that TMRI can accurately estimate traffic matrix and track its dynamics,and in contrast to previous methods,it holds the stronger robustness to noise and more evident performance improvement.