This paper addresses the problem of inferring link loss rates based on network performance tomography in noisy network systems. Since network tomography emerged, all existing tomography-based methods are limited to the fulfillment of a basic condition: both network topologies and end-to-end routes must be absolutely accurate, which in most cases is impractical, especially for large-scale heterogeneous networks. To overcome the impracticability of tomography-based methods, we propose a robust tomography-based loss inference method capable of accurately inferring all link loss rates even when the given knowledge about the system is unreliable. Rather than computing the link loss rates directly from end-to-end loss rates, it calculates an upper bound for each link loss rate. It then infers all the link loss rates that most closely conform to the measurement results within their upper bounds. For a scenario where noisy measurements are caused by link (or router port) failures, we propose a hidden fault diagnosis approach that utilizes the inferred link loss rates to pinpoint the insidious faults that are hardly detected. It first determines the possible fake routes based on inferred link loss rates. Subsequently, it finds the maximum probable faults that can best explain the fake routes. Through intensive experiments, the results strongly confirm the promising performance of our proposed approaches.

中文翻译：

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存在噪声测量和隐患诊断的鲁棒损失推断

本文解决了在嘈杂的网络系统中基于网络性能层析成像推断链路丢失率的问题。自从网络断层扫描出现以来，所有现有的基于断层扫描的方法都限于满足基本条件：网络拓扑和端到端路由都必须绝对准确，这在大多数情况下是不切实际的，尤其是对于大规模异构网络。为了克服基于层析成像方法的不切实际性，我们提出了一种鲁棒的基于层析成像的损耗推断方法，即使在有关系统的已知知识不可靠的情况下，该方法也能够准确推断所有链路损耗率。与其直接根据端到端的丢失率来计算链路丢失率，不如计算每个链路丢失率的上限。然后，推断所有最接近测量结果上限的链路丢失率。对于由链路（或路由器端口）故障引起噪声测量的情况，我们提出了一种隐藏的故障诊断方法，该方法利用推断的链路丢失率来查明难以检测到的隐患。它首先根据推断的链路丢失率确定可能的伪造路由。随后，它找到了最大可能的故障，可以最好地解释假路线。通过密集的实验，结果强烈证实了我们提出的方法的有希望的性能。我们提出了一种隐式故障诊断方法，该方法利用推断的链路丢失率来查明难以检测到的隐性故障。它首先根据推断的链路丢失率确定可能的伪造路由。随后，它找到了最大可能的故障，可以最好地解释假路线。通过密集的实验，结果强烈证实了我们提出的方法的有希望的性能。我们提出了一种隐式故障诊断方法，该方法利用推断的链路丢失率来查明难以检测到的隐性故障。它首先根据推断的链路丢失率确定可能的伪造路由。随后，它找到了最大可能的故障，可以最好地解释假路线。通过密集的实验，结果强烈证实了我们提出的方法的有希望的性能。