Computing diagnostic explanations of network faults from monitoring data

Network fault diagnosis is an important aspect of network management. Often, a single component failure will result in a cascade of secondary faults that overwhelm simple reasoning approaches. If the network monitoring information is being transmitted through the network to the network management system (NMS), then fault diagnosis is complicated by the fact that the transmission of relevant monitoring information for fault diagnosis may be blocked either by the fault itself, or by the fault’s effects on the network. Without perfect knowledge, the best fault diagnosis algorithm must properly reason about a number of competing diagnostic explanations that are compatible with the ambiguous networking monitoring information known to the NMS. We describe a novel algorithm for computing all such possible diagnostic explanations and their relative likelihoods, thus providing a complete diagnosis of the network state that can be effectively used by an NMS to correct or mitigate faults.
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The algorithm uses a variant of classic Boolean satisfiability to efficiently and compactly represent the space of possible explanations. The proposed approach is well suited for networks with (semi-)autonomous management domains organized into a larger management hierarchy, a feature common to many military networks.