An evolutionary fault diagnosis algorithm for interconnection networks under the PMC model

Fault diagnosis of interconnection networks is vital to ensuring the reliability and maintenance of multiprocessor systems. Based on graph theory, diagnostic algorithms for solving the problem of fault diagnosis in interconnection networks have been widely studied. As the number of processors in mul...

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Vydáno v:The Journal of supercomputing Ročník 79; číslo 9; s. 9964 - 9984
Hlavní autoři: Liang, Jiarong, Guo, Yang, Xie, Yuhao, Liang, Xinyu
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.06.2023
Springer Nature B.V
Témata:
Accuracy
Compilers
Computation
Computer Science
Evolutionary algorithms
Fault diagnosis
Genetic algorithms
Graph theory
Integer programming
Interpreters
Multiprocessing
Networks
Optimization
Processor Architectures
Programming Languages
Fault diagnosis
Zero-one integer program
diagnosable systems
Large-scale network systems
Genetic algorithm
ISSN:0920-8542, 1573-0484
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Shrnutí:Fault diagnosis of interconnection networks is vital to ensuring the reliability and maintenance of multiprocessor systems. Based on graph theory, diagnostic algorithms for solving the problem of fault diagnosis in interconnection networks have been widely studied. As the number of processors in multiprocessor systems has increased in recent years, fault diagnosis algorithms based on graph theory cannot meet the current diagnosis requirements of some interconnection networks, such as t -diagnosable systems. In this paper, we use the evolution diagnosis approach to study fault diagnosis in t -diagnosable systems under the PMC model. First, for a t -diagnosable system G with a syndrome σ , we use a simple centralized algorithm to generate its simplified diagnosis graph G f , which contains all the faulty nodes in G . Based on the graph, we prove that the faulty node set in G is the minimum cover set of G f . Next, we prove that the problem of computing the minimum cover set for G f is equivalent to the problem of computing the optimal solution of a zero-one integer program. Using this result, we propose a novel genetic algorithm to solve the problem of the zero-one integer program. The simulation results show that the diagnostic accuracy of our proposed algorithm is equal to or greater than 96% and that the proposed algorithm outperforms its competitors in terms of diagnostic accuracy, number of iterations and running time.
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-023-05054-0