System-Level Fault Diagnosis Using Comparison Models: An Artificial-Immune-Systems-Based Approach

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Provided by: Academy Publisher
Topic: Hardware
Format: PDF
The design of large dependable multiprocessor systems requires quick and precise mechanisms for detecting the faulty nodes. The problem of system-level fault diagnosis is computationally difficult and no efficient and generic deterministic solutions are known, motivating the use of heuristic algorithms. In this paper, the authors show how Artificial Immune Systems (AIS) can be used for fault diagnosis in large multiprocessor systems containing several hundred nodes. They consider two models - the simple comparison model and the Generalized Comparison Model (GCM), and they propose AIS-based algorithms for identifying faults in diagnosable systems, based on comparisons among units.
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