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A novel dual-attribute fault diagnosis measure for hypercube networks.

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Title: A novel dual-attribute fault diagnosis measure for hypercube networks.
Authors: Zhuo, Nengjin1 (AUTHOR) 15038792269@163.com, Zhang, Shumin1,2,3 (AUTHOR) zhangshumin@qhnu.edu.cn, Chang, Jou-Ming4 (AUTHOR) spade@ntub.edu.tw, Ma, Haoyu5 (AUTHOR) mahaoyu_22@bupt.edu.cn
Source: Discrete Applied Mathematics. Sep2026, Vol. 390, p40-56. 17p.
Subjects: Fault diagnosis, Hypercube networks (Computer networks), Multiprocessors, Fault tolerance (Engineering)
Abstract: Fault diagnosis technology is a key technique for maintaining the stable operation of multi-processor systems. By designing and applying diagnostic models and algorithms, multi-processor systems can execute specific instructions and utilize diagnostic algorithms to analyze the result, thereby accurately identifying faulty processors in the system. Different types of fault sets cause varying degrees of damage to a system. Therefore, many scholars have conducted in-depth research on those fault sets that may cause significant damage to a system and proposed various diagnosability metrics to evaluate the diagnostic capability of a system, such as component diagnosability and H -structure diagnosability. This paper introduces a novel diagnosability measure c t r g (G) to evaluate the diagnostic capability of a system G regarding g -extra r -component fault sets. Through an in-depth analysis of the hypercube network topology, we determine that c t 3 1 (Q n) = 6 n − 15 under the PMC model and c t 3 1 (Q n) = 5 n − 11 under the MM* model. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Fault diagnosis technology is a key technique for maintaining the stable operation of multi-processor systems. By designing and applying diagnostic models and algorithms, multi-processor systems can execute specific instructions and utilize diagnostic algorithms to analyze the result, thereby accurately identifying faulty processors in the system. Different types of fault sets cause varying degrees of damage to a system. Therefore, many scholars have conducted in-depth research on those fault sets that may cause significant damage to a system and proposed various diagnosability metrics to evaluate the diagnostic capability of a system, such as component diagnosability and H -structure diagnosability. This paper introduces a novel diagnosability measure c t r g (G) to evaluate the diagnostic capability of a system G regarding g -extra r -component fault sets. Through an in-depth analysis of the hypercube network topology, we determine that c t 3 1 (Q n) = 6 n − 15 under the PMC model and c t 3 1 (Q n) = 5 n − 11 under the MM* model. [ABSTRACT FROM AUTHOR]
ISSN:0166218X
DOI:10.1016/j.dam.2026.04.005