Conditional Diagnosability of $(n,k)$ -Star Networks Under the Comparison Diagnosis Model

The (n,k)-star graph, denoted by S_n,k, is an enhanced version of n-dimensional star graphs Sn, that has better scalability than Sn, 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 ≥ 3 and k=1) and ( n ≥ 4 and 2 ≤ k ≤ n) under the comparison diagnosis model.