A Novel Adaptive System-Level Fault Self-Diagnosis Algorithm and Its Applications

Abstract

With the application and rapid development of high-performance computing and cloud computing technology, the scale of the interconnection network has appeared to grow exponentially. Network attacks have become increasingly sophisticated and stealthy. To reach a high reliable network system, widespread attention has been paid to fault diagnosis. In this article, we put forward a reliable and adaptive self-diagnosis strategy, the $h$-extra $r$-component conditional diagnosability, denoted by $ct_{r}^{h}(G)$. Then, we provide a theoretical derivation to characterize the $h$-extra $r$-component conditional diagnosability of bubble sort networks $B_{n}$ under the PMC model. Furthermore, we develop a fast and adaptive fault self-diagnosis algorithm FAFD-PMC to detect all faulty units. Extensive experiments are implemented and applied to synthetic networks and real networks in terms of accuracy (ACCR), true negative rate, false positive rate, recall, and precision, which demonstrates the ACCR/efficiency of our algorithm.