基于渐进网络断层成像的静态和动态故障定位
Static and Dynamic Failure Localization through Progressive Network Tomography
摘要：
我们的目标是通过端到端的监控路径来评估网络中节点的状态。本文的贡献是双重的。首先，我们考虑一个静态故障场景。在这种情况下，我们的目标是尽量减少探针的数量，以获得故障识别。针对这一问题，本文提出了一种基于随机优化的渐进式故障定位方法，其解为待探测的监测路径的最优序列。我们解决问题的复杂性，提出了一个贪婪的策略，在两个变种：一个考虑精确计算节点故障的后验概率，给定的观察，而另一个近似这些值通过一个新的故障中心度量。其次，我们适应这两种策略的动态故障情况下，节点状态可以在整个监控期间发生变化。通过在真实网络拓扑上进行的数值实验，我们证明了我们的方法的实用性。我们的性能评估证明了我们的算法相对于基于经典布尔网络断层扫描的最先进的解决方案以及基于顺序组测试的方法的优越性。
英文摘要：
We aim at assessing the states of the nodes in a network by means of end-to-end monitoring paths. The contribution of this paper is twofold. First, we consider a static failure scenario. In this context, we aim at minimizing the number of probes to obtain failure identification. To face this problem, we propose a progressive approach to failure localization based on stochastic optimization, whose solution is the optimal sequence of monitoring paths to probe. We address the complexity of the problem by proposing a greedy strategy in two variants: one considers exact calculation of posterior probabilities of node failures, given the observation, whereas the other approximates these values by means of a novel failure centrality metric. Secondly, we adapt these two strategies to a dynamic failure scenario where nodes states can change throughout a monitoring period. By means of numerical experiments conducted on real network topologies, we demonstrate the practical applicability of our approach. Our performance evaluation evidences the superiority of our algorithms with respect to state of the art solutions based on classic Boolean Network Tomography as well as approaches based on sequential group testing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
