Network traffic matrix (TM) is a critical input for capacity planning, anomaly detection and many other network management related tasks. The TMs are often computed from link load measurements. The TM estimation problem is the determination of the TM from link load measurements. The relationship between the link loads and the TM that generated the link loads can be modeled as an under-determined linear system and has multiple feasible solutions. Therefore, prior knowledge of the traffic demand pattern has to be used in order to find a potentially feasible TM. In this paper, we consider the TM estimation problem with limited prior information. Unlike previous methods that require past measurements of complete TMs, which are hard to obtain or protected by regulations, our method works even if only the distribution of TMs is known. We develop an iterative projection based algorithm to solve this problem. If large number of past TMs can be measured, we propose a Generative Adversarial Network (GAN) based approach for solving the problem. We compare the strengths of the two approaches and evaluate their performance for several networks using varying amounts of past data.

中文翻译：

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基于学习的基于链路测量的流量矩阵估计方法

网络流量矩阵（TM）是容量规划，异常检测和许多其他网络管理相关任务的关键输入。TM通常是根据链路负载测量来计算的。TM估计问题是根据链路负载测量来确定TM。链接负载与生成链接负载的TM之间的关系可以建模为欠定线性系统，并具有多种可行的解决方案。因此，必须使用对交通需求模式的先验知识，以找到可能可行的TM。在本文中，我们考虑具有有限先验信息的TM估计问题。与以前的方法要求过去对完整的TM进行测量而又难以通过法规来获取或保护的方法不同，即使仅知道TM的分布，我们的方法也可以工作。我们开发了一种基于迭代投影的算法来解决此问题。如果可以测量大量过去的TM，我们建议使用基于生成对抗网络（GAN）的方法来解决该问题。我们比较两种方法的优势，并使用大量过去的数据评估它们在多个网络中的性能。