The g-Good-Neighbor Conditional Diagnosability of Exchanged Crossed Cube under the MM* Model
Xinyang Wang, Haozhe Li, Qiao Sun, Chen Guo, Hu Zhao, Xinyu Wu, Anqi Wang
Abstract
Diagnosability plays an important role in appraising the reliability and fault tolerance of symmetrical multiprocessor systems. The novel g-good-neighbor conditional diagnosability restrains that every fault-free node contains at least g fault-free neighbors and is suitable for large scale multiprocessor systems, attracting a lot of research attention. The relationships between the g-good-neighbor connectivity and g-good-neighbor diagnosability of graphs under the MM* model are separately studied, but only applicable in regular graphs or just ranges rather than exact values. As a promising network structure, in 2019, Guo et al. obtained that the g-good-neighbor diagnosability of the exchanged crossed cube (ECQ(s,t)) under the PMC model is 2g(s+2−g)−1 (t≥s>g). We noticed that the exact value of the g-good-neighbor diagnosability of ECQ(s,t) under the MM* model is still to be determined. In this paper, by proving the upper and lower bounds of the g-good-neighbor diagnosability of ECQ(s,t), for the first time, we derive that the exact value of its g-good-neighbor diagnosability under the MM* model is tgm(ECQ(s,t))=2g(s+2−g)−1 (t≥s>g), achieving the unity of the g-good-neighbor diagnosability of ECQ(s, t) under both the PMC model and MM* model. Towards the end, simulation experiments are conducted to evaluate the correctness and effectiveness of our conclusion. Our research provides an important supplement to the g-good-neighbor diagnosability of ECQ(s,t).