Extra Conditional Diagnosability of Hypercubes under the Bounded PMC Model
Yongcui Tian, Qiang Zhu, Chaofeng Lv · 2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) · 2022
The h-extra conditional diagnosability is different from the traditional diagnosability, which restricts that each component has no fewer than$h+1$processors after the deletion of the faulty sets in the system. The$(f_{1}, f_{2})$-BPMC model is a combination of the PMC model and BGM model, assuming that the upper bound number of failed processors is$f_{1}$and no more than$f_{2}$failed processors that can evaluate a faulty processor as non-faulty. In this paper, inspired by the$(f_{1}, f_{2})$- BPMC model, we propose a diagnosis model called$f$-BPMC model by relaxing the restriction of$f_{1}$. In this model, it only assumes that at most$f$failed processors for a given system that can evaluate faulty processors as non-faulty. We then study the h-extra conditional diagnosability of interconnection networks under the$f$-BPMC model and explore some of its properties. Finally, the h-extra conditional diagnosability is applied to hypercubes under the$f$-BPMC model.