TY - JOUR
T1 - Conditional Diagnosability of (n,k)-Star Graphs under the PMC Model
AU - Chang, Nai Wen
AU - Hsieh, Sun Yuan
N1 - Publisher Copyright:
© 2004-2012 IEEE.
PY - 2018/3/1
Y1 - 2018/3/1
N2 - Fault diagnosis has played a major role in measuring the reliability of multiprocessor systems. The diagnosability of many well-known multiprocessor systems has been widely investigated. Conditional diagnosability is a novel property of measuring diagnosability by adding a further condition that any fault set cannot contain all the neighbors of every node in the system. Several known structural properties of (n,k)-star graphs are exhibited. Based on these properties, we investigate the conditional diagnosability of (n,k)-star graphs under the PMC model, and show that it is 1) n/2 -1 for n≥ 4 and k=1, and 2) n+3k-6 for 2≤ k≤n-3.
AB - Fault diagnosis has played a major role in measuring the reliability of multiprocessor systems. The diagnosability of many well-known multiprocessor systems has been widely investigated. Conditional diagnosability is a novel property of measuring diagnosability by adding a further condition that any fault set cannot contain all the neighbors of every node in the system. Several known structural properties of (n,k)-star graphs are exhibited. Based on these properties, we investigate the conditional diagnosability of (n,k)-star graphs under the PMC model, and show that it is 1) n/2 -1 for n≥ 4 and k=1, and 2) n+3k-6 for 2≤ k≤n-3.
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U2 - 10.1109/TDSC.2016.2562620
DO - 10.1109/TDSC.2016.2562620
M3 - Article
AN - SCOPUS:85029849713
SN - 1545-5971
VL - 15
SP - 207
EP - 216
JO - IEEE Transactions on Dependable and Secure Computing
JF - IEEE Transactions on Dependable and Secure Computing
IS - 2
ER -