The 2-good-neighbor diagnosability of Cayley graphs generated by transposition trees under the PMC model and MM* model

被引：41

作者：

Wang, Mujiangshan ^{[1
]}

Lin, Yuqing ^{[1
]}

Wang, Shiying ^{[2
]}

机构：

[1] Univ Newcastle, Sch Elect Engn & Comp Sci, Callaghan, NSW 2308, Australia

[2] Henan Normal Univ, Sch Math & Informat Sci, Henan Engn Lab Big Data Stat Anal & Optimal Contr, Xinxiang 453007, Henan, Peoples R China

来源：

THEORETICAL COMPUTER SCIENCE | 2016年 / 628卷

基金：

美国国家科学基金会;

关键词：

Interconnection network; Graph; Diagnosability; PMC model; MM* model; Cayley graph; 2-Good-neighbor diagnosability; CONDITIONAL DIAGNOSABILITY; MULTIPROCESSOR SYSTEMS; DIAGNOSIS;

D O I：

10.1016/j.tcs.2016.03.019

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Diagnosability is an important metric for measuring the reliability of multiprocessor systems. In 2012, Peng et al. proposed a new measure for fault diagnosis of the system, which is called g-good-neighbor diagnosability that restrains every fault-free node containing at least g fault-free neighbors. As a favorable topology structure of interconnection networks, the Cayley graph C Gamma(n) generated by the transposition tree Gamma(n) has many good properties. In this paper, we give that the 2-good-neighbor diagnosability of C Gamma(n) under the PMC model and MM* model is g(n - 2) - 1, where n >= 4 and g is the girth of C Gamma(n). (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：92 / 100

页数：9

共 49 条

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