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首页> 外文期刊>Journal of network and computer applications >Traffic matrix estimation: A neural network approach with extended input and expectation maximization iteration
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Traffic matrix estimation: A neural network approach with extended input and expectation maximization iteration

机译：流量矩阵估算：具有扩展输入和期望最大化迭代的神经网络方法

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摘要

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. (C) 2015 Elsevier Ltd. All rights reserved.
机译：准确估计IP流量矩阵（TM）仍然是一项艰巨的任务，它在网络管理，负载平衡，流量检测等方面具有广泛的应用。在本文中，我们提出了一种精确的方法，即基于Moore-Penrose逆的神经网络方法，用于具有扩展输入和期望最大化迭代的IP网络流量矩阵估计，简称为MNETME。首先，MNETME采用扩展的输入组件，即路由矩阵的Moore-Penrose逆和链接负载矢量的乘积，作为神经网络的输入。其次，将EM算法集成到其体系结构中以处理神经网络的输出数据。因此，MNETME具有以下优点：需要较少的输入数据，但估计精度更高。我们从理论上分析算法，然后使用来自Abilene网络的真实数据研究其性能。仿真结果表明，与以前的方法相比，MNETME导致更准确的估计，同时它具有更好的鲁棒性并且可以很好地跟踪流量波动。最后，我们提出了一种新的随机路由模型，将MNETME扩展到随机路由网络，该模型克服了现有模型的三个致命缺陷，并且更加简单，实用和精确。 （C）2015 Elsevier Ltd.保留所有权利。

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