Operations Research Transactions ›› 2019, Vol. 23 ›› Issue (1): 97-103.doi: 10.15960/j.cnki.issn.1007-6093.2019.01.011

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The 1-good-neighbor diagnosability of the Cayley graphs UGn generated by unicyclic graphs under the PMC model and the MM* model

REN Jiamin1, FENG Wei1, ZHAO Lingqi2, WANG Shiying3, JIRIMUTU1,*   

  1. 1. College of Mathematics, Inner Mongolia University for Nationalities, Tongliao 028043, Inner Mongolia Autonomous Region, China;
    2. College of Computer Science and Technology, Inner Mongolia University for Nationalities, Tongliao 028043, Inner Mongolia Autonomous Region, China;
    3. School of Mathematics and Information Science, Henan Normal University, Xinxiang 453007, Henan, China
  • Received:2017-09-11 Online:2019-03-15 Published:2019-03-15

Abstract:

Diagnosability of a multiprocessor system is an important study topic. A new measure for fault diagnosis of the system is called g-good-neighbor diagnosability that restrains every fault-free node containing at least g fault-free neighbors. As a famous topology structure of interconnection networks, the Cayley graph UGn generated by unicyclic graphs has many good properties. In this paper, we prove that the 1-good-neighbor diagnosability of the Cayley graph UGn generated by unicyclic graphs is 2n-1 under the PMC model for n ≥ 4; the 1-good-neighbor diagnosability of the Cayley graph UGn generated by unicyclic graphs is 2n-1 under the MM* model for n ≥ 5.

Key words: interconnection network, diagnosability, Cayley graph, PMC model, MM* model

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