PMC模型下星图的g-好邻局部诊断度  

The g-Good-Neighbor Local Diagnosability of Star Graphs under the PMC Model

作　　者：乔慧娟 原军[1] QIAO Hui-juan;YUAN Jun(School of Applied Sciences,Taiyuan University of Science and Technology,Taiyuan 030024,China)

机构地区：[1]太原科技大学应用科学学院,太原030024

出　　处：《太原科技大学学报》2022年第4期346-350,共5页Journal of Taiyuan University of Science and Technology

基　　金：国家自然科学基金(61402317);山西省自然科学基金(201901D111253)。

摘　　要：故障诊断度对于多处理系统的可靠性至关重要,是多处理器系统互连网络能够诊断出的最大故障点的数量。研究表明,系统的诊断度总小于其最小度,然而这严重地低估了系统的诊断能力。2019年,Yin和Liang提出了g-好邻局部诊断度的定义,它可以表征系统在g-好邻条件下的局部故障诊断能力。文章证明了PMC模型下星图S_(n)的每个结点的g-好邻局部诊断度为(n-g)(g+1)!-1,其中0≤g≤n-2,n≥4.根据诊断度与局部诊断度之间的关系,可以推出星图的g-好邻诊断度。The fault diagnosis is very important to the reliability of the multiprocessing systems.It is the maximum number of fault vertices that can be diagnosed by the interconnection network of multiprocessor systems.Studies have shown that the system’s diagnosability is always less than its minimum degree,but this seriously underestimates the system’s diagnostic capability.In 2019,Yin and Liang proposed the definition of g-good-neighbor local diagnosability,which can characterize the local fault diagnosis ability of the system under g-good-neighbor conditions.This paper shows that the g-good-neighbor local diagnosability of each node of the star graph S_(n) under the PMC model is(n-g)(g+1)!-1,where 0≤g≤n-2,n≥4.According to the relationship between diagnosability and local diagnosability,the g-good-neighbor conditional diagnosability of the star graph can be obtained.

关 键 词：多处理器系统 g-好邻局部诊断度 PMC模型 星图 

分 类 号：O157.5[理学—数学]