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

Nai Wen Chang; Sun Yuan Hsieh

doi:10.1109/TPDS.2013.290

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

Nai Wen Chang, Sun Yuan Hsieh

Department of Computer Science and Information Engineering

Research output: Contribution to journal › Article › peer-review

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 language	English
Article number	6671606
Pages (from-to)	3002-3011
Number of pages	10
Journal	IEEE Transactions on Parallel and Distributed Systems
Volume	25
Issue number	11
DOIs	https://doi.org/10.1109/TPDS.2013.290
Publication status	Published - 2014 Nov 1

All Science Journal Classification (ASJC) codes

Signal Processing
Hardware and Architecture
Computational Theory and Mathematics

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