Fault tolerance of recursive match networks based on g-good-neighbor fault pattern

The rapid informatization and digitalization of the society heavily rely on the extensive use of parallel and distributed, networked computer systems. It is important for large-scale parallel and distributed systems to be able to detect and tolerate faulty vertices in the network. A network's fault status can often be characterized with the network's connectivity and diagnosability. The connectivity/diagnosability can be defined under various conditions. This paper is concerned with the connectivity/diagnosability under the “g-good-neighbor condition”, which can more accurately measure a network's fault status. In this paper, we propose a new class of recursive networks, named recursive match networks (RMNs), which contain the well-known BCube and BC networks. We determine the RMNs' g-good-neighbor connectivity and g-good-neighbor conditional diagnosability under the classic MM* and PMC diagnostic models for g≥0. Since the RMN is a more general network covering the BCube and BC networks, our results can be directly applied to these two networks. •We proposed a new class of networks, named recursive match networks (RMNs), which contain the BCube, BC networks, and some other networks.•We obtained the g-good-neighbor connectivity and g-good-neighbor conditional diagnosability of RMNs, under the MM* and PMC models.•We got that the g-good-neighbor connectivity/diagnosability of the RMN are both nearly g+1 times of its original, unconditional ones.•The results of this paper can be directly applied to the BCube, BC networks, and any other networks that fall within the definition of the RMN.