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Symmetric Comparison-Based Fault Diagnosis of Multiprocessor and Distributed Systems Using Nonlinear Support Vector Machines

In this paper, the problem of identifying the set of permanent faulty nodes using partial syndromes, i.e., when not all the comparison outcomes are available prior to initiating the diagnosis phase, is considered. A new diagnosis approach, using nonlinear support vector machines (SVMs), is described. We consider the symmetric comparison diagnosis model which assumes that nodes are assigned a set of tasks and their outcomes are compared, and that at most t nodes can fail simultaneously. Based on the agreements and disagreements among the nodes' outputs, the diagnosis algorithm must identify all faulty nodes. The new nonlinear SVM-based fault identification algorithm is first trained using various syndromes with known fault sets. Then, it is extensively tested using randomly generated diagnosable systems of different sizes and under various fault scenarios. Simulations showed that the nonlinear SVM-based diagnosis performed efficiently, i.e. the diagnosis algorithm correctly identified almost all the faulty nodes even when at most half of the comparison outcomes are missing. In addition, results from the thorough simulation study demonstrate the effectiveness of the nonlinear SVM-based fault identification algorithm, in terms of diagnosis correctness, latency, and scalability.