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

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.