Performance evaluation on hybrid fault diagnosability of regular networks

Guanqin Lian, Shuming Zhou, Sun Yuan Hsieh, Jiafei Liu, Gaolin Chen, Yihong Wang

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Diagnosability 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 edge malfunctions may arise. Recently, a kind of new diagnosability under hybrid fault circumstances, called h-edge tolerable diagnosability, has been proposed and the h-edge tolerable diagnosability of n-dimensional hypercube under the PMC model and MM model is determined to be n−h for n≥4 and 1≤h≤n−1. In this work, we propose a general approach to determine the h-edge tolerable diagnosability of general regular networks. We show that the h-edge tolerable diagnosability of a t-regular t-connected network with N processors under the PMC model (resp., MM model) is t−h for t≥2 and 1≤h≤t−1 if N≥2(t−h)+1 (resp., N≥2(t−h)+3). Moreover, if G=(V,E) is a t-regular t-diagnosable network under the PMC (resp., MM) model (where t≥2), then th e(G)=t−h for 1≤h≤t−1 under the PMC (resp., MM) model.

Original languageEnglish
Pages (from-to)147-153
Number of pages7
JournalTheoretical Computer Science
Publication statusPublished - 2019 Dec 3

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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