JOURNAL OF NETWORKS (JNW)
ISSN : 1796-2056
Volume : 1 Issue : 5 Date : September/October 2006
System-Level Fault Diagnosis Using Comparison Models: An Artificial-Immune-Systems-Based
Mourad Elhadef, Shantanu Das, and Amiya Nayak
Full Text: PDF (521 KB)
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.
Fault tolerance, System-level diagnosis, Multiprocessor and multicomputer systems, Comparison
models, Artificial Immune Systems.