Strong Diagnosability and Conditional Diagnosability of Augmented Cubes Under the Comparison Diagnosis Model

The problem of fault diagnosis has been discussed widely, and the diagnosability of many well-known networks has been explored. Strong diagnosability, and conditional diagnosability are both novel measurements for evaluating reliability and fault tolerance of a system. In this paper, some useful sufficient conditions are proposed to determine strong diagnosability, and the conditional diagnosability of a system. We then apply them to show that an n-dimensional augmented cube AQ_n is strongly (2n -1)-diagnosable for n ≥ 5, and the conditional diagnosability of AQ_n is 6n - 17 for n ≥ 6. Our result demonstrates that the conditional diagnosability of AQ_n is about three times larger than the classical diagnosability.