Reliability evaluation of generalized exchanged X-cubes under the $$R_g$$-conditional restriction

Lin, Wanling; Zhuang, Hongbin; Li, Xiao-Yan; Zhang, Yufang

doi:10.1007/s11227-023-05861-5

Cited by 2 publications

(1 citation statement)

References 38 publications

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“…Subsequently, the set of faulty nodes is determined based on all test results. To date, numerous studies have concentrated on the PMC model [12,18,19,20,22]. On the other hand, Malek [23] introduced the Malek model, which represents the most basic comparative model.…”

Section: Introductionmentioning

confidence: 99%

Reliability analysis of complete cubic networks based on extra conditional fault

Lv,

Liu,

Dong

et al. 2024

Preprint

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The reliability of multiprocessor systems is now a crucial concern in parallel computing. In fact, thanks to rapid and consistent technological advances in networking hardware and software, multiprocessor (core) systems have been successfully implemented. Consequently, there is a notable importance in examining the structures of interprocess communication, with a particular focus on investigating the fault tolerance and fault diagnosability of these structures to build a functionally efficient system. This paper investigates the $s$-extra connectivity ($s$-EC) and $s$-extra diagnosability ($s$-ED) of the complete cubic network $CCN(n)$. Specifically, we initially demonstrate that the $s$-EC of $CCN(n)$ is $\kappa_s(CCN(n))=(s+1)(n+1)-\frac{s(s+3)}{2}$ for $n\geq 3$ and $0\leq s\leq n-2$. Subsequently, we demonstrate that the $s$-ED under the PMC model is $t_s(CCN(n))=(s+1)(n+1)-\frac{s(s+3)}{2}+s$ for $n\geq 3$ and $1\leq s\leq n-2$. Similarly, under the MM* model, the $s$-ED is $t_s(CCN(n))=(s+1)(n+1)-\frac{s(s+3)}{2}+s$ for $n\geq 6$ and $1\leq s\leq \frac{n-2}{4}$. Finally, we conduct simulation experiments, and the results indicate that the $s$-EC consistently surpasses other known connectivities, including classical connectivity and $s$-component connectivity. Additionally, the $s$-ED consistently outperforms classical diagnosability and $s$-component diagnosability of $CCN(n)$.

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Section: Introductionmentioning

confidence: 99%

Reliability analysis of complete cubic networks based on extra conditional fault

Lv,

Liu,

Dong

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Reliability analysis of complete cubic networks based on extra conditional fault

Lv,

Liu,

Dong

et al. 2024

J Supercomput

View full text Add to dashboard Cite

The reliability of multiprocessor systems is now a crucial concern in parallel computing. In fact, thanks to rapid and consistent technological advances in networking hardware and software, multiprocessor (core) systems have been successfully implemented. Consequently, there is a notable importance in examining the structures of interprocess communication, with a particular focus on investigating the fault tolerance and fault diagnosability of these structures to build a functionally efficient system. This paper investigates the s-extra connectivity (s-EC) and s-extra diagnosability (s-ED) of the complete cubic network CCN (n). Specifically, we initially demonstrate that the s4 . Finally, we conduct simulation experiments, and the results indicate that the s-EC consistently surpasses other known connectivities, including classical connectivity and s-component connectivity. Additionally, the s-ED consistently outperforms classical diagnosability and s-component diagnosability of CCN (n).

show abstract

Reliability evaluation of generalized exchanged X-cubes under the $$R_g$$-conditional restriction

Cited by 2 publications

References 38 publications

Reliability analysis of complete cubic networks based on extra conditional fault

Reliability analysis of complete cubic networks based on extra conditional fault

Reliability analysis of complete cubic networks based on extra conditional fault

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