M. Steinder and A. S. Sethi. Non-deterministic diagnosis of end-to-end service failures in a multilayer communication system. In Proc. of ICCCN, pp. 374--379, Scottsdale, AZ, 2001.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....very affective. The work in [20] can localize a fault to a portion of the multicast tree that is between two branch points. Similarly, our approach can localize performance problems between two branching points on the network topology. Dependency graphs are commonly used in fault isolation [15] [24]. These approaches are based on first forming a dependency graph that models the propagation of faults in the network and then searching this graph to isolate the smallest set of nodes that are the likely culprits. There are parallels between the approach we used in this paper to localize the ....

....is the set to which we can suspect for poor call quality. For the example, this computation results with which is a reduction from the initial suspicious set of size 5. 13 The approach above is similar to the fault dependency model graph based techniques such as those in [15] and [24] if the dependency graph is formed as follows: The dependency graph is directed and bipartite where the nodes denote both individual links and end to end paths. There is a directed edge in the dependency graph from each node denoting an end toend path to each node denoting a link on the ....

M. Steinder and A. S. Sethi, "Non-deterministic diagnosis of end-to-end service failures in a multi-layer communication system,", In Proc. of the IEEE Int'l Conf. on Computer Communications and Networks, pp. 374--379, 2001.

....resilient to lost and spurious symptoms [7, 10] event driven [4, 7, 10, 17] incremental [4, 7, 10, 17, 15, 16] and more efficient [15, 16] The paper is structured as follows. Section 2 describes the concept of probabilistic incremental hypothesis updating, which was originally introduced in [13]. In Section 3, incremental hypothesis updating is extended to include positive and lost symptoms in the analysis. Section 4 presents the methodology of dealing with spurious symptoms and discusses the necessary modifications to the original algorithm. In Section 5, the experimental study of the ....

....denoted by . The purpose of fault localization is to find d that maximizes the probability that (1) all faults in d occur and (2) each symptom in is explained by at least one fault from d . The technique we present in this section, which is called incremental hypothesis updating [13] (IHU) creates a set of most likely hypotheses, which may all be presented to the system administrator. Rather than waiting for a specific period of time before presenting a solution, the technique makes all these hypotheses available on a continuous basis, and constantly upgrades them with the ....

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M. Steinder and A. S. Sethi. Non-deterministic diagnosis of end-to-end service failures in a multilayer communication system. In Proc. of ICCCN, pp. 374--379, Scottsdale, AZ, 2001.

....of the algorithm. Section 4 presents illustrative examples of cooperation among managers. In Section 5, preliminary results of a simulation study are presented. The related work is described in Section 6. 2 Centralized end to end service failure diagnosis End to end service failure diagnosis [14] uses a directed bipartite graph as a fault propagation model, whose parentless vertices represent host to host (link) failures, and childless vertices represent the resultant end to end service (path) failures. Host to host and end to end service failures are considered faults and symptoms, ....

....scheme [17, 16] in fault localization performed by the managers on all layers of the hierarchy. Other non deterministic fault localization algorithms could be considered for this purpose: maximum mutual dependency heuristics [9] statistical methods [4] or the incremental algorithm proposed in [14]. Belief networks have previously been applied to the problem of fault diagnosis, but the reported solutions are limited to narrow applications [3, 5, 18] These solutions either assume a tree shaped belief network model [18] or disregard uncertainty involved in causal relationships between ....

M. Steinder and A. S. Sethi. Non-deterministic diagnosis of end-to-end service failures in a multi-layer communication system. In Proc. of ICCCN, Scottsdale, AZ, 2001. pp. 374--379.

....does not include conditional probabilities, and therefore is suitable for the diagnosis of availability related problems only. In our previous work on fault localization, we proposed an incremental algorithm for diagnosing faults whose propagation pattern may be modeled by bipartite graphs [31]. The algorithm has lower computational complexity than the algorithms proposed in this paper but is not suitable for other fault management tasks such as the prediction of affected services or test planning. The system model used in this paper constitutes a refinement of the layered model ....

M. Steinder and A. S. Sethi. Non-deterministic diagnosis of end-to-end service failures in a multi-layer communication system. In Proc. of ICCCN, Scottsdale, AR, 2001. pp. 374--379.

....and protocols. Current research at the University of Delaware extensively investigates other probabilistic approaches to event correlation. Recently, several algorithms have been designed and analyzed including the most probable explanation in belief networks and iterative hypothesis update [21]. The views and conclusions contained in this document are those of the authors ....

M. Steinder and A. S. Sethi. Non-deterministic diagnosis of end-to-end service failures in a multi-layer communication system. In Proc. IEEE IC3N: Int'l Conf. Comp. Comm. Networks, Scottsdale, AZ, 2001.

....end to end service provided between two end hosts along the path that includes the malfunctioning host to host link. To diagnose the end to end service problem, one needs to identify the faulty host to host link. We refer to this problem as the problem of end to end service failure diagnosis [3] [10]. Endto end service failure diagnosis uses a bipartite graph, whose parentless nodes represent host to host failures (root problems) and childless nodes represent the resultant end to end service failures (symptoms) To build such a graph, the knowledge of network topology and current routing ....

....current routing information are required. Note that, such a reduction is, in general, an NP complete problem on its own. Frequently, an assumption is made with respect to the graph shape that allows the reduction process to be completed in polynomial time. In our work on fault localization [5] [10], we have been using the latter approach to simplifying the fault localization task, because it allows the system to respond easily to the system configuration changes. On the other hand, when the original fault propagation model is reduced to a bipartite graph, every configuration change ....

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M. Steinder and A. S. Sethi, "Non-deterministic diagnosis of end-toend service failures in a multi-layer communication system," in Proc. of ICCCN, Scottsdale, AR, 2001, pp. 374--379.

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