Partial syndrome-based system-level fault diagnosis using game theory
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This paper introduces a novel diagnosis approach, using game theory, to solve the comparison-based system-level fault identiﬁcation problem in distributed and parallel systems based on the asymmetric comparison model. Under this diagnosis model tasks are assigned to pairs of nodes and the results of executing these tasks are compared. Using the agreements and disagreements among the nodes’ outputs, i.e. the input syndrome, the fault diagnosis algorithm identiﬁes the fault status of the system’s nodes, under the assumption that at most t of these nodes can permanently fail simultaneously. Since the introduction of the comparison model, signiﬁcant progress has been made in both theory and practice associated with the original model and its oﬀshoots. Nevertheless, the problem of eﬃciently identifying the set of faulty nodes when not all the comparison outcomes are available to the fault identiﬁcation algorithm prior to initiating the diagnosis phase, i.e. partial syndromes, remains an outstanding research issue. In this paper, we ﬁrst show how game theory can be adapted to solve the fault diagnosis problem by maximising the payoﬀs of all players (nodes). We then demonstrate, using results from a thorough simulation, the eﬀectiveness of this approach in solving the fault identiﬁcation problem using partial syndromes from randomly generated diagnosable systems of diﬀerent sizes and under various fault scenarios. We have considered large diagnosable systems, and we have experimented extreme faulty situations by simulating all possible fault sets even those that are less likely to occur in practice. Over all the extensive simulations we have conducted, the new game-theory-based diagnosis algorithm performed very well and provided good diagnosis results, in terms of correctness, latency, and scalability, making it a viable addition or alternative to existing diagnosis algorithms.