Name
Abstract | ||
|---|---|---|
In this paper, we present a system-level fault identification algorithm, using a parallel genetic algorithm, for diagnosing faulty nodes in large heterogeneous systems. The algorithm is based on a probabilistic model where individual node fails with an a priori probability p. The assumptions concerning test outcomes are the same as in the PMC model, that is, fault-free testers always give correct test outcomes and faulty testers are totally unpredictable. The parallel diagnosis algorithm was implemented and simulated on randomly generated large systems. Simulations results are provided showing that the parallel diagnosis did improve the efficiency of the evolutionary diagnosis approach, in that it allowed faster diagnosis of faulty situation, making it a contribution to present techniques. |
Year | Venue | Keywords |
|---|---|---|
2005 | PDPTA '05: Proceedings of the 2005 International Conference on Parallel and Distributed Processing Techniques and Applications, Vols 1-3 | multiprocessor systems, system-level diagnosis, probabilistic diagnosis, fault tolerance, parallel genetic algorithms, parallel virtual machine (PVM) |
Field | DocType | Citations |
Computer science,Probabilistic logic,Distributed computing | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mourad Elhadef | 1 | 202 | 21.29 |
| Kaouther Abrougui | 2 | 98 | 11.01 |
| Shantanu Das | 3 | 497 | 41.94 |
| Amiya Nayak | 4 | 1600 | 129.46 |