Structural Properties and Conditional Diagnosability of Star Graphs by Using the PMC Model
Nai-Wen Chang, Sun‐Yuan Hsieh · IEEE Transactions on Parallel and Distributed Systems · 2014
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$n\geq 5$.