The Component Diagnosability of General Networks

Zhuang, Hongbin; Guo, Wenzhong; Li, Xiao-Yan; Liu, Ximeng; Lin, Cheng-Kuan

doi:10.1142/s0129054121500374

Cited by 5 publications

(2 citation statements)

References 42 publications

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“…Next, we study the effects of s and n on the s-ED of the complete cubic network CCN (n), and then compare the s-ED with other existing diagnosabilities including classical diagnosability and s-component diagnosability [13] of CCN (n). Fig.…”

Section: Simulation and Discussionmentioning

confidence: 99%

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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: Simulation and Discussionmentioning

confidence: 99%

“…In the MM* model, if any two nodes share a common neighbor, that common neighbor must act as the arbitrator for the two nodes, undergoing testing and judgment. Numerous works on the MM* model have been developed [6,13,14,16,21,29].…”

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

Lin,

Zhuang,

et al. 2024

J Supercomput

View full text Add to dashboard Cite

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

The Component Diagnosability of General Networks

Cited by 5 publications

References 42 publications

Reliability analysis of complete cubic networks based on extra conditional fault

Reliability analysis of complete cubic networks based on extra conditional fault

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

Reliability analysis of complete cubic networks based on extra conditional fault

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