Relating diagnosability, strong diagnosability and conditional diagnosability of strong networks

Qiang Zhu, Guodong Guo, Dajin Wang

Research output: Contribution to journalArticleResearchpeer-review

17 Citations (Scopus)

Abstract

An interconnection network's diagnosability is an important measure of its self-diagnostic capability. Based on the classical notion of diagnosability, strong diagnosability and conditional diagnosability were proposed later to better reflect the networks' self-diagnostic capability under more realistic assumptions. In this paper, we study a class of interconnection networks called strong networks, which are n -regular, (n - 1) -connected, and with cn -number no more than n - 3. We build a relationship among the three diagnosability measures for strong networks. Under both PMC and MM* models, given a strong network G with diagnosability t , we prove that G is strongly t -diagnosable if and only if G 's conditional diagnosability is greater than t. A simple check can show that almost all well-known regular interconnection networks are strong networks. The significance of this paper's result is that it reveals an important relationship between strong and conditional diagnosabilities, and the proof of strong diagnosability for many interconnection networks under MM* or PMC model is not necessary if their conditional diagnosability can be shown to be strictly larger than their diagnosability.

Original languageEnglish
Article number6484056
Pages (from-to)1847-1851
Number of pages5
JournalIEEE Transactions on Computers
Volume63
Issue number7
DOIs
StatePublished - 1 Jan 2014

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Diagnosability
Interconnection Networks
Diagnostics
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Keywords

  • Interconnection networks
  • PMC model
  • conditional diagnosability
  • strong diagnosability

Cite this

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Relating diagnosability, strong diagnosability and conditional diagnosability of strong networks. / Zhu, Qiang; Guo, Guodong; Wang, Dajin.

In: IEEE Transactions on Computers, Vol. 63, No. 7, 6484056, 01.01.2014, p. 1847-1851.

Research output: Contribution to journalArticleResearchpeer-review

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