$k$-ary $n$-cubes, but also data center network BCube, as well as some future networks. Connectivity and diagnosability of networks have garnered significant attention, as they suffice for analyzing and measuring networks' fault tolerance. This article focuses primarily on conditional connectivity and diagnosability under the good neighbor fault pattern. In this work, we explore the conditional connectivity and diagnosability (built on $g$-good neighbor fault pattern) of the $f$-dimensional $r$-order CRN under the PMC model and MM* model, respectively. These values are nearly $g$ times greater than the traditional connectivity and diagnosability of CRNs, respectively, implying that they can further improve fault tolerance of CRNs. Furthermore, it is worth noting that the results can be effectively utilized in BCube and other future networks given that they are both subclasses of CRNs.">

Fault Tolerance of Circulant-Based Recursive Networks Built on $g$-Good Neighbor Fault Pattern (original) (raw)

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