As critical reliability metrics for multiprocessor systems, connectivity and diagnosability respectively determine network robustness against node failures and the capability to accurately identify faulty units. This article presents a comprehensive reliability analysis of bubble-sort networks (Bn), a class of Cayley graphs that implement adjacent-node swap operations, offering inherent advantages for distributed sorting systems. First, we investigate theh-extrar-component connectivity and diagnosability ofBnunder the Preparata-Metze-Chien (PMC) diagnostic model through rigorous topological analysis. Then, we characterize theg-good-neighborr-component connectivity and diagnosability ofBnunder the PMC model by establishing tight bounds through fault pattern analysis. In addition, we develop a three-round fault identification algorithm, TRFI-PMC, that achieves robust diagnostic performance in simulated experiments. Specifically, for the networkB8with 40320 nodes, the algorithm maintains superior performance (fault density achieving 25%) across five metrics: accuracy (98.81%), true negative rate (97.81%), false positive rate (2.18%), true positive rate (99.3%), and precision (98.98%). The theoretical results establish fundamental reliability limits forBnarchitectures, while the practical algorithm provides an efficient fault diagnosis solution forn-dimensional bubble-sort network.

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.