Hybrid fault diagnosis capability analysis of regular graphs under the PMC model
Hong Zhang, Laijiang Zhang, Jixiang Meng · International Journal of Computer Mathematics Computer Systems Theory · 2020
Diagnosabilty is an important metric to the capability of fault identification for multiprocessor systems. However, most researches on diagnosability focus on vertex fault. In real circumstances, not only vertex faults take place but also malfunctions may arise. In this paper, we study the diagnosability of k-regular 2-cn graph with missing edges. Let Fe be a set of missing edges in graph G with |Fe|≤ k−5. We prove that the diagnosability of G−Fe is at most δ(G−Fe) for k≥5. Furthermore, we obtain that the worst-case diagnosability (h-edge tolerable diagnosability), denoted by the(G), is maximum number of faulty nodes that a system G can guarantee to locate when the number of faulty links does not exceed h. As applications, the diagnosabilities of many networks with missing edges are determined under the PMC model.