Structural properties and conditional diagnosability of star graphs by using the PMC model

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54 Citations (Scopus)

Abstract

Processor fault diagnosis has played an important role in measuring the reliability of a multiprocessor system; the diagnosability of many well-known multiprocessor systems has been widely investigated. Conditional diagnosability is a novel measure of diagnosability. It includes a condition whereby any fault set cannot contain all the neighbors of any node in a system. In this paper, the conditional diagnosability of star graphs by using the PMC model is evaluated. Several new structural properties of star graphs are derived. Based on these properties, the conditional diagnosability of an n-dimensional star graph is determined to be 8n-21 for n≥ 5.

Original languageEnglish
Article number6671606
Pages (from-to)3002-3011
Number of pages10
JournalIEEE Transactions on Parallel and Distributed Systems
Volume25
Issue number11
DOIs
Publication statusPublished - 2014 Nov 1

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics

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