The distributed nature of the Internet makes it difficult for a single service provider to troubleshoot the disruptions experienced by its customers. We propose NetDiagnoser, a troubleshooting algorithm to identify the location of failures in an internetwork environment. First, we adapt the wellknown Boolean tomography technique to work in this environment. Then, we significantly extend this technique to improve the diagnosis accuracy in the presence of multiple link failures, logical failures (for instance, misconfigurations of route export filters), and incomplete topology inference. In particular, NetDiagnoser takes advantage of rerouted paths, routing messages collected at one provider’s network and Looking Glass servers. We evaluate each feature of Net-Diagnoser separately using C-BGP simulations on realistic topologies. Our results show that NetDiagnoser can successfully identify a small set of links, which almost always includes the actually failed/misconfigured links.

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Probabilistic inference was shown effective in non-deterministic diagnosis of end-to-end service failures. To overcome the exponential complexity of the exact inference algorithms in fault propagation models represented by graphs with undirected loops, Pearl’s iterative algorithms for polytrees were used as an approximation schema. The approximation made it possible to diagnose end-to-end service failures in network topologies composed of tens of nodes. This paper proposes a distributed algorithm that increases the admissible network size by an order of magnitude. The algorithm divides the computational effort and system knowledge among multiple, hierarchically organized managers. The cooperation among managers is illustrated with examples, and the results of a preliminary performance study are presented. 1 1

Future battlefield networks require extensive fault management mechanism. Most existing fault localization algorithms assume availability of complete and/or deterministic dependency model. Such assumptions can not be made in the dynamic environment of a battlefield network where nodes may move, causing periodic changes in routes. This paper is aimed at developing a fault diagnosis architecture and algorithm, which address the issue of dynamically changing dependencies in battlefield networks. 1.

Abstract — We consider an end-to-end approach of inferring network faults that manifest in multiple protocol layers, with an optimization goal of minimizing the expected cost of correcting all faulty nodes. Instead of first checking the most likely faulty nodes as in conventional fault localization problems, we prove that an optimal strategy should start with checking one of the candidate nodes, which are identified based on a potential function that we develop. We propose several efficient heuristics for inferring the best node to be checked in large-scale networks. By extensive simulation, we show that we can infer the best node in at least 95%, and that checking first the candidate nodes rather than the most likely faulty nodes can decrease the checking cost of correcting all faulty nodes by up to 25%. Index Terms — network management, network diagnosis and correction, fault localization and repair, reliability engineering. I.

This article is an editorial note submitted to CCR. It has NOT been peer reviewed. This paper investigates the practical issues in applying network tomography to monitor failures. We outline an approach for selecting paths to monitor, detecting and confirming the existence of a failure, correlating multiple independent observations into a single failure event, and applying existing binary networking tomography algorithms to identify failures. We evaluate the ability of network tomography algorithms to correctly detect and identify failures in a controlled environment on the VINI testbed.

Abstract. This paper investigates an approach to improving the scalability and feasibility of probabilistic fault localization in communication systems by exploiting the domain semantics of computer networks. The proposed technique divides the computational effort and system knowledge among multiple, hierarchically organized managers. Each manager performs fault localization in the domain it manages and requires only the knowledge of its own domain. Since failures propagate among domains, domain managers cooperate with each other to find a consensus explanation of the observed disorder. We show through simulation that the proposed approach increases the effectiveness of probabilistic diagnosis and makes it feasible in networks of considerable size 1. 1

Graphs provide powerful abstractions of relational data, and are widely used in fields such as network management, web page analysis and sociology. While many graph representations of data describe dynamic and time evolving relationships, most graph mining work treats graphs as static entities. Our focus in this paper is to discover regions of a graph that are evolving in a similar manner. To discover regions of correlated spatio-temporal change in graphs, we propose an algorithm called cSTAG. Whereas most clustering techniques are designed to find clusters that optimise a single distance measure, cSTAG addresses the problem of finding clusters that optimise both temporal and spatial distance measures simultaneously. We show the effectiveness of cSTAG using a quantitative analysis of accuracy on synthetic data sets, as well as demonstrating its utility on two large, real-life data sets, where one is the routing topology of the Internet, and the other is the dynamic graph of files accessed together on the 1998 World Cup official website.

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