Conditional Diagnosability of $(n,k)$ -Star Networks Under the Comparison Diagnosis Model
Autor: | Nai-Wen Chang, Wei-Hao Deng, Sun-Yuan Hsieh |
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Rok vydání: | 2015 |
Předmět: | |
Zdroj: | IEEE Transactions on Reliability. 64:132-143 |
ISSN: | 1558-1721 0018-9529 |
DOI: | 10.1109/tr.2014.2354912 |
Popis: | The $(n,k)$ -star graph, denoted by $S_{n,k}$ , is an enhanced version of $n$ -dimensional star graphs $S_{n}$ , that has better scalability than $S_{n}$ , and possesses several good properties, compared with hypercubes. Diagnosis has been one of the most important issues for maintaining multiprocessor-system reliability. Conditional diagnosability, which is more general than classical diagnosability, measures the multiprocessor-system diagnosability under the assumption that all neighbors of any processor in the system cannot fail simultaneously. In this paper, we investigate the conditional diagnosability of $S_{n,k}$ for ( $n\geq 3$ and $k=1$ ) and ( $n\geq 4$ and $2\leq k\leq n$ ) under the comparison diagnosis model. |
Databáze: | OpenAIRE |
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