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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Published in:	The Journal of supercomputing Vol. 79; no. 9; pp. 9964 - 9984
Main Authors:	Liang, Jiarong, Guo, Yang, Xie, Yuhao, Liang, Xinyu
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.06.2023 Springer Nature B.V
Subjects:	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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Abstract	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.
AbstractList	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. 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 Gf, 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 Gf. Next, we prove that the problem of computing the minimum cover set for Gf 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.
Author	Liang, Jiarong Xie, Yuhao Guo, Yang Liang, Xinyu
Author_xml	– sequence: 1 givenname: Jiarong surname: Liang fullname: Liang, Jiarong email: 19980003@gxu.edu.cn organization: College of Information Engineering, Nanning University, School of Computer, Electronics and Information, Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University – sequence: 2 givenname: Yang surname: Guo fullname: Guo, Yang organization: School of Computer, Electronics and Information, Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University – sequence: 3 givenname: Yuhao surname: Xie fullname: Xie, Yuhao organization: School of Computer, Electronics and Information, Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University – sequence: 4 givenname: Xinyu surname: Liang fullname: Liang, Xinyu organization: School of Computer, Electronics and Information, Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University
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Snippet	Fault diagnosis of interconnection networks is vital to ensuring the reliability and maintenance of multiprocessor systems. Based on graph theory, diagnostic...
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StartPage	9964
SubjectTerms	Accuracy Compilers Computation Computer Science Evolutionary algorithms Fault diagnosis Genetic algorithms Graph theory Integer programming Interpreters Multiprocessing Networks Optimization Processor Architectures Programming Languages
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Title	An evolutionary fault diagnosis algorithm for interconnection networks under the PMC model
URI	https://link.springer.com/article/10.1007/s11227-023-05054-0 https://www.proquest.com/docview/3256584664
Volume	79
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