Probabilistic fault diagnosis in communication systems through incremental hypothesis updating

Abstract

This paper presents a probabilistic event-driven fault localization technique, which uses a probabilistic symptomfault map as a fault propagation model. The technique isolates the most probable set of faults through incremental updating of a symptom-explanation hypothesis. At any time, it provides a set of alternative hypotheses, each of which is a complete explanation of the set of symptoms observed thus far. The hypotheses are ranked according to a measure of their goodness. The technique allows multiple simultaneous independent faults to be identified and incorporates both negative and positive symptoms in the analysis. As shown in a simulation study, the technique offers close-to-optimal accuracy and is resilient both to noise in the symptom data and to inaccuracies of the probabilistic fault propagation model.

Cross-domain fault localization: A case for a graph digest approach

“…A current trend attacks the problem by modeling network dependencies in a directed acyclic graph having root causes as parentless nodes, observations as childless nodes, and dependencies represented as directed edges in the graphs with uncertainties captured in conditional probability distributions associated with each node [5], [9], [10]. This graph structure is also known as a causal (or causality) graph [6].…”

“…This graph structure is also known as a causal (or causality) graph [6]. Approaches typically perform probabilistic (Bayesian) inference on bipartite causal graphs [5], [10].…”

Modeling and Analysis of Probabilistic Timed Systems

“…On the other hand the analysis from biological systems can help researchers develop a better understanding of the complex physiological dynamics of the systems they study. Probabilistic models are also useful in modeling effect of possible failures [22].…”

Active Probing Approach for Fault Localization in Computer Networks

“…This information is gathered by route discovery agents and is stored in a dependency model. The dependency model represents the dependency relationships between probe paths and the managed network components that it probes [13,21]. The nature of this dependency information affects the probe selection decision:…”