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TMRI(Traffic Matrix Recurrence Inference).TMRI利用RBF神经网络强大的建模功能来建模流量矩阵估计问题,将这一问题的病态特性克服于RBF神经网络的训练过程中,从而避免复杂的数学建模过程.并在所建立的估计模型基础上,将流量矩阵估计描述为约束条件下的最优化过程,通过迭代寻优,TMRI能进一步克服这一问题的病态特性.仿真结果表明TMRI能精确地估计流量矩阵和追踪它的动态变化,与以前的方法相比,具有更强的抗噪声性能和显著的性能改善.%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 optmal processs under the constraints. By seeking the recurrent optimal solution, TIMlRI 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.
展开▼
