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Yihong Wang

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4 papers

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TCS Journal 2023 Journal Article

Reliability evaluation of complete graph-based recursive networks

Yihong Wang
Jianxi Fan
Yuejuan Han
Yan Wang
Baolei Cheng

With the development of cloud computing and high-performance computing technologies, the scales of data center networks and interconnection networks become more and more large. As a result, the reliability evaluation of the two kinds of networks is very important. And most scholars study the reliability of specific networks. In this paper, we put forward a kind of complete graph-based recursive networks (short as CGRNs) which includes data center networks—DCell and generalized DCell, as well as the interconnection network dragonfly, etc. Then we further investigate the reliability of such networks—the connectivity, the diagnosability under the pessimistic diagnosis strategy based on the PMC model, g-restrict connectivity, and the g-good-neighbor conditional diagnosabilities under the PMC model and the MM⁎ model. As applications, previous results of DCell networks can be directly obtained and new results on the connectivity, diagnosability under the pessimistic strategy diagnosis based on the PMC model, g-restrict connectivity, and the g-good-neighbor conditional diagnosabilities under the PMC model and the MM⁎ model of networks (such as generalized DCell networks and dragonfly networks), are derived. Even these reliability results can be obtained for some other networks different from DCell networks, generalized DCell networks, and dragonfly networks. In addition, it can be seen that the diagnosability under the pessimistic diagnosis strategy based on the PMC model, the g-restrict connectivity, and the g-good-neighbor conditional diagnosabilities under the PMC model and the MM⁎ model of G r are about 2 times of its diagnosability, g + 1 times of its connectivity, and g + 1 times of its diagnosability, respectively.

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TCS Journal 2021 Journal Article

Note on R-conditional diagnosability of hypercube

Yihong Wang
Cheng-Kuan Lin
Qianru Zhou
Shuming Zhou

The g-good-neighbor conditional diagnosability, which specifies at least g fault-free neighbors for each fault-free node, is a very important metric in system-level diagnosis. Recently, the g-good-neighbor conditional diagnosabilities of many networks have been investigated. To enhance the g-good-neighbor conditional diagnosability, the R g -conditional diagnosability, which requires at least g fault-free neighbors for each node, has been proposed by Guo et al. [1] (2020) recently. And they establish the R g -conditional diagnosability of the hypercubes under the PMC model. In this paper, we present some counterexamples for the proof of lower bound on the R g -conditional diagnosability of the hypercubes under the PMC model, which is crucial to the original main result. And we further utilize known results to give a reasonable lower bound for the R g -conditional diagnosability of hypercubes.

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TCS Journal 2020 Journal Article

Diagnosability for two families of composition networks

Yihong Wang
Cheng-Kuan Lin
Xiaoyan Li
Shuming Zhou

Diagnosability is an important parameter to assess the reliability of multiprocessor systems. A multiprocessor system is called t-diagnosable if all faulty processors can be identified without replacement as long as the number of faulty processors does not exceed t. The diagnosability is the maximum value of t such that multiprocessors system is t-diagnosable. Matching composition networks (MCNs) and cycle composition networks (CCNs) are two typical network structures to construct multiprocessor systems. Let G be a MCN which is obtained by adding a perfect matching between two graph G 0 and G 1, and let H be a CCN obtained by adding a perfect matching between G i and G i + 1 ( mod m ) for each i ∈ { 0, 1, …, m − 1 } with m ≥ 3. In this paper, we show that the diagnosability of G under the PMC ( δ ( G ) ≥ 2 ) model and MM⁎ model ( δ ( G ) ≥ 5 ) is n − 1 if G 0 ≅ G 1 ≅ K n; otherwise, δ ( G ). Furthermore, we show that the diagnosability of H under the PMC model ( δ ( H ) ≥ 4 ) and MM⁎ model ( δ ( H ) ≥ 5 ) is δ ( H ).

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TCS Journal 2019 Journal Article

Performance evaluation on hybrid fault diagnosability of regular networks

Guanqin Lian
Shuming Zhou
Sun-Yuan Hsieh
Jiafei Liu
Gaolin Chen
Yihong Wang

Diagnosability is an important metric to the capability of fault identification for multiprocessor systems. However, most researches on diagnosability focus on vertex fault. In real circumstances, not only vertex faults take place but also edge malfunctions may arise. Recently, a kind of new diagnosability under hybrid fault circumstances, called h-edge tolerable diagnosability, has been proposed and the h-edge tolerable diagnosability of n-dimensional hypercube under the PMC model and MM ⁎ model is determined to be n − h for n ≥ 4 and 1 ≤ h ≤ n − 1. In this work, we propose a general approach to determine the h-edge tolerable diagnosability of general regular networks. We show that the h-edge tolerable diagnosability of a t-regular t-connected network with N processors under the PMC model (resp. , MM ⁎ model) is t − h for t ≥ 2 and 1 ≤ h ≤ t − 1 if N ≥ 2 ( t − h ) + 1 (resp. , N ≥ 2 ( t − h ) + 3 ). Moreover, if G = ( V, E ) is a t-regular t-diagnosable network under the PMC (resp. , MM ⁎ ) model (where t ≥ 2 ), then t h e ( G ) = t − h for 1 ≤ h ≤ t − 1 under the PMC (resp. , MM ⁎ ) model.

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