As a technique to investigate behaviours of a computer network with low operational cost, network tomography has received considerable attentions in recent years. Most studies in this area assume that the topology of the network of interest is known, and try to propose computationally and/or statistically efficient methods to estimate link-level properties such as loss rate, delay distribution, bandwidth etc., or global traffic properties such as point-to-point traffic matrix. Little progresses have been made for scenarios when topology of the target network is unknown, although it is often the case in many practical applications. The few published works for topology tomography resolved the problem primarily by clustering analysis, which works for tree-like networks only and often suffers from unstable performance for large networks of complicated structure. In this article, we study the classic problem of network tomography from a new perspective. By connecting the problem of topology tomography to the classic machine learning problem of “market basket analysis,” we find that simultaneous topology and loss tomography can be achieved by discovering association patterns of loss records collected at receivers, which can be efficiently resolved with light modifications of a recently developed statistical method known as the “theme dictionary model”. Both theoretical analysis and simulation studies demonstrate that the proposed approach enjoys improved effectiveness for networks of tree as well as general topology with slightly higher computational costs.

中文翻译：

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通过改进的主题字典模型同时进行拓扑和损耗断层扫描


作为一种以低运营成本研究计算机网络行为的技术，网络断层扫描近年来受到了广泛的关注。该领域的大多数研究假设感兴趣的网络的拓扑是已知的，并尝试提出计算和/或统计上有效的方法来估计链路级属性，例如丢失率、延迟分布、带宽等，或全局流量属性例如点对点的流量矩阵。尽管在许多实际应用中经常出现这种情况，但在目标网络拓扑未知的情况下，进展甚微。少数已发表的拓扑断层扫描著作主要通过聚类分析来解决该问题，该分析仅适用于树状网络，并且对于结构复杂的大型网络通常会出现性能不稳定的问题。在这篇文章中，我们从一个新的角度研究网络层析成像的经典问题。通过将拓扑断层扫描问题与“购物篮分析”的经典机器学习问题联系起来，我们发现可以通过发现接收器处收集的丢失记录的关联模式来实现同时拓扑和损失断层扫描，并且可以通过轻微修改来有效解决最近开发的一种称为“主题词典模型”的统计方法。理论分析和仿真研究都表明，所提出的方法对树形网络以及一般拓扑具有更高的有效性，但计算成本略高。