Narrow Results

The growing size of the multiprocessor system increases its vulnerability to component failures. It is crucial to locate and replace the faulty processors to maintain a system's high reliability. The fault diagnosis is the process of identifying faulty processors in a system through testing. This paper shows that the largest connected component of the survival graph contains almost all of the remaining vertices in the dual-cube DC_n when the number of faulty vertices is up to twice or three times of the traditional connectivity. Based on this fault resiliency, this paper determines that the conditional diagnosability of DC_n (n ≥ 3) under the comparison model is 3n − 2, which is about three times of the traditional diagnosability.

The conditional diagnosability of interconnection networks has been studied in a number of ad-hoc methods resulting in various conditional diagnosability results. In this paper, we utilize these existing results to give an unified approach in studying this problem. Following this approach, we derive the exact value of the conditional diagnosability for a number of interconnection networks including Cayley graphs generated by 2-trees (which generalize alternating group graphs), arrangement graphs (which generalize star graphs and alternating group graphs), hyper Petersen networks, and dual-cube like networks (which generalize dual-cubes.)