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g-Good-Neighbor Diagnosability of Arrangement Graphs under the PMC Model and MM* Model

College of Mathematics and Information Science, Henan Normal University, Xinxiang 453007, Henan, China
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Information 2018, 9(11), 275; https://doi.org/10.3390/info9110275
Received: 17 September 2018 / Revised: 27 October 2018 / Accepted: 5 November 2018 / Published: 7 November 2018
(This article belongs to the Special Issue Graphs for Smart Communications Systems)
Diagnosability of a multiprocessor system is an important research topic. The system and interconnection network has a underlying topology, which usually presented by a graph G = ( V , E ) . In 2012, a measurement for fault tolerance of the graph was proposed by Peng et al. This measurement is called the g-good-neighbor diagnosability that restrains every fault-free node to contain at least g fault-free neighbors. Under the PMC model, to diagnose the system, two adjacent nodes in G are can perform tests on each other. Under the MM model, to diagnose the system, a node sends the same task to two of its neighbors, and then compares their responses. The MM* is a special case of the MM model and each node must test its any pair of adjacent nodes of the system. As a famous topology structure, the ( n , k ) -arrangement graph A n , k , has many good properties. In this paper, we give the g-good-neighbor diagnosability of A n , k under the PMC model and MM* model. View Full-Text
Keywords: interconnection network; diagnosability; arrangement graph interconnection network; diagnosability; arrangement graph
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MDPI and ACS Style

Wang, S.; Ren, Y. g-Good-Neighbor Diagnosability of Arrangement Graphs under the PMC Model and MM* Model. Information 2018, 9, 275. https://doi.org/10.3390/info9110275

AMA Style

Wang S, Ren Y. g-Good-Neighbor Diagnosability of Arrangement Graphs under the PMC Model and MM* Model. Information. 2018; 9(11):275. https://doi.org/10.3390/info9110275

Chicago/Turabian Style

Wang, Shiying; Ren, Yunxia. 2018. "g-Good-Neighbor Diagnosability of Arrangement Graphs under the PMC Model and MM* Model" Information 9, no. 11: 275. https://doi.org/10.3390/info9110275

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