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JNW
2006

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

13 years 10 months ago
System-Level Fault Diagnosis Using Comparison Models: An Artificial-Immune-Systems-Based Approach
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, we show how artificial immune systems (AIS) can be used for fault diagnosis in large multiprocessor systems containing several hundred nodes. We consider two models--the simple comparison model and the generalized comparison model (GCM), and we propose AIS-based algorithms for identifying faults in diagnosable systems, based on comparisons among units. We performed experimental analysis of these algorithms by simulating them on randomly generated diagnosable systems of various sizes under various fault scenarios. The simulation results indicate that the AIS-based approach provides an effective solution to the system-level fault diagnosis problem.
Mourad Elhadef, Shantanu Das, Amiya Nayak
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2006
Where JNW
Authors Mourad Elhadef, Shantanu Das, Amiya Nayak
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