Characterization of cyclic local diagnosability of interconnection networks

Abstract With the growing scale and complexity of high-performance computing systems, ensuring reliability through robust fault diagnosis becomes increasingly critical. System-level diagnosis plays a key role in identifying faulty processors and maintaining system stability of multiprocessor systems. However, traditional diagnosability, as a global reliability metric for multiprocessor systems, overlooks local diagnostic capability, topological criticality, and fault distribution. In order to better capture the local characteristics of a system around a given node, this work proposes a novel fault diagnosis strategy, called cyclic local diagnosability, where the cyclic fault pattern requires that at least two components contain cycles. We propose some characterizations of cyclic local diagnosability of interconnection networks under PMC and MM* models. As applications, we determine the cyclic local diagnosabilities of data center network DCell ($D_{k,n}$), $(n,k)$-star graph ($S_{n,k}$) and $(n,k)$-bubble-sort graph ($B_{n,k}$) under PMC and MM* models. Finally, we show the superiority of the cyclic local diagnosability through comparison with other conditional diagnosabilities.