Traffic matrix estimation: A neural network approach with extended input and expectation maximization iteration
Traffic matrix estimation: A neural network approach with extended input and expectation maximization iteration
复制标题
DOI：
10.1016/j.jnca.2015.11.013
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Haifeng Zhou;L. Tan;Qian Zeng;Chunming Wu
中科院分区：
文献类型：
--
作者：
Haifeng Zhou;L. Tan;Qian Zeng;Chunming Wu
IP流量矩阵（TM）的精确估计一直是一个具有挑战性的课题，它在网络管理、负载均衡、流量检测等方面有着广泛的应用。提出了一种基于Moore-Penrose逆的扩展输入期望最大化迭代的IP网络流量矩阵估计神经网络方法，简称MNETME。首先，MNETME采用了扩展的输入组件，即，将路由矩阵的Moore-Penrose逆与链路负载向量的乘积作为神经网络的输入。其次，EM算法被纳入其结构，以处理神经网络的输出数据。因此，MNETME具有输入数据少、估计精度高的优点。我们从理论上分析了该算法，然后使用Abilene网络的真实的数据来研究其性能。仿真结果表明，与以往的方法相比，MNETME方法具有更高的估计精度，同时具有更好的鲁棒性，能够很好地跟踪流量波动。最后，我们将MNETME扩展到随机路由网络，提出了一种新的随机路由模型，克服了现有模型的三个致命缺陷，更简单，更实用，更精确。
Accurately estimating of IP Traffic matrix (TM) is still a challenging task and it has wide applications in network management, load-balancing, traffic detecting and so on. In this paper, we propose an accurate method, i.e., the Moore–Penrose inverse based neural network approach for the estimation of IP network traffic matrix with extended input and expectation maximization iteration, which is termed as MNETME for short. Firstly, MNETME adopts the extended input component, i.e., the product of routing matrix׳s Moore–Penrose inverse and the link load vector, as the input to the neural network. Secondly, the EM algorithm is incorporated into its architecture to deal with the output data of the neural network. Therefore, MNETME manifests itself with the advantages that it needs less input data, but has better accuracy of estimation. We theoretically analyze the algorithm and then study its performance using the real data from the Abilene Network. The simulation results show that MNETME leads to a more accurate estimation in contrast to the previous methods, meanwhile it holds better robustness and can well track the traffic fluctuations. We finally extend MNETME to random routing networks by proposing a new model of random routing which overcomes three fatal deficiencies of the existing model and it is easier, more practical and more precise.