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

Title: The 2-good-neighbor diagnosability of Cayley graphs generated by transposition trees under the PMC model and MM* model
Creator: Wang, Mujiangshan; Lin, Yuqing; Wang, Shiying
Relation: Theoretical Computer Science Vol. 628, p. 92-100
Publisher Link: http://dx.doi.org/10.1016/j.tcs.2016.03.019
Publisher: Elsevier
Resource Type: journal article
Date: 2016
Description: 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 CΓ_n generated by the transposition tree Γ_n has many good properties. In this paper, we give that the 2-good-neighbor diagnosability of CΓ_n under the PMC model and MM⁎ model is g(n−2)−1, where n≥4and g is the girth of CΓ_n.
Subject: interconnection network; graph; diagnosability; PMC model; MM* model; Cayley graph; 2-good-neighbor diagnosability
Identifier: http://hdl.handle.net/1959.13/1323706
Identifier: uon:24872
Identifier: ISSN:0304-3975
Language: eng
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