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

Qianru Zhou, Hai Liu, Baolei Cheng, Yan Wang, Yuejuan Han and Jianxi Fan

Applied Mathematics and Computation, 2024, vol. 461, issue C

Abstract: 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.

Keywords: RMN; g-good-neighbor connectivity; g-good-neighbor conditional diagnosability (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:461:y:2024:i:c:s0096300323004873

DOI: 10.1016/j.amc.2023.128318

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