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Hybrid fault diagnosability of the exchanged crossed cube under the HPMC model
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DOI：10.1080/02533839.2026.2619706.png)
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Abstract
En 中文
The rapid growth of data-intensive and AI-driven applications has heightened the reliability and diagnosability requirements of high-performance computing systems, in which hybrid faults involving both nodes and links occur frequently. Although the exchanged crossed cube $ECQ\left({s,t} \right)$ECQs,t is a cost-effective interconnection network with strong topological properties, its hybrid fault diagnosability under contemporary hybrid models remains insufficiently studied. Recently, the concept of h-edge g-good-neighbor conditional diagnosability has been proposed to significantly enhance the accuracy of hybrid fault diagnosis. Motivated by this gap, this paper investigates the $h$h-edge $g$g-good-neighbor conditional diagnosability of $ECQ\left({s,t} \right)$ECQs,t under the $HPMC$HPMC model, and proves that $t_g<^>h\left({ECQ\left({s,t} \right)} \right) = {2<^>g}\left({s - g+ 2} \right) - h- 1$tghECQs,t=2gs-g+2-h-1, under the constraints $2 \le s\le t$2 <= s <= t and $0 \le h\le g\le s$0 <= h <= g <= s. Simulation experiments verify the correctness of the proposed theoretical results. This study not only establishes the exact hybrid diagnosability of $ECQ\left({s,t} \right)$ECQs,t but also demonstrates its superior capability in supporting hybrid fault diagnosis for large-scale parallel systems.
Keywords:
Hybrid fault diagnosability
the exchanged crossed cube
h-edge g-good-neighbor
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