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t/k-fault diagnosis algorithm of n-dimensional hypercube network based on the MM*model 被引量:4

t/k-fault diagnosis algorithm of n-dimensional hypercube network based on the MM~* model
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摘要 Compared with accurate diagnosis, the system’s selfdiagnosing capability can be greatly increased through the t/kdiagnosis strategy at most k vertexes to be mistakenly identified as faulty under the comparison model, where k is typically a small number. Based on the Preparata, Metze, and Chien(PMC)model, the n-dimensional hypercube network is proved to be t/kdiagnosable. In this paper, based on the Maeng and Malek(MM)*model, a novel t/k-fault diagnosis(1≤k≤4) algorithm of ndimensional hypercube, called t/k-MM*-DIAG, is proposed to isolate all faulty processors within the set of nodes, among which the number of fault-free nodes identified wrongly as faulty is at most k. The time complexity in our algorithm is only O(2~n n~2). Compared with accurate diagnosis, the system's selfdiagnosing capability can be greatly increased through the t/kdiagnosis strategy at most k vertexes to be mistakenly identified as faulty under the comparison model, where k is typically a small number. Based on the Preparata, Metze, and Chien(PMC)model, the n-dimensional hypercube network is proved to be t/kdiagnosable. In this paper, based on the Maeng and Malek(MM)*model, a novel t/k-fault diagnosis(1≤k≤4) algorithm of ndimensional hypercube, called t/k-MM*-DIAG, is proposed to isolate all faulty processors within the set of nodes, among which the number of fault-free nodes identified wrongly as faulty is at most k. The time complexity in our algorithm is only O(2~n n^2).
出处 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2018年第1期216-222,共7页 系统工程与电子技术(英文版)
基金 supported by the National Natural Science Foundation of China(61363002)
关键词 hypercube network t/k-diagnosis algorithm multiprocessor systems the Maeng and Malek(MM)* model Preparata Metze and Chien(PMC) hypercube network t/k-diagnosis algorithm multiprocessor systems the Maeng and Malek(MM)* model Preparata Metze and Chien(PMC)
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