Hybrid fault diagnosis capability analysis of regular graphs under the PMC model

IF 0.9 Q3 COMPUTER SCIENCE, THEORY & METHODS International Journal of Computer Mathematics: Computer Systems Theory Pub Date : 2020-02-26 DOI:10.1080/23799927.2020.1735523
Hong Zhang, Laijiang Zhang, J. Meng
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引用次数: 3

Abstract

Diagnosabilty 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 malfunctions may arise. In this paper, we study the diagnosability of k-regular 2-cn graph with missing edges. Let be a set of missing edges in graph G with . We prove that the diagnosability of is at most for . Furthermore, we obtain that the worst-case diagnosability (h-edge tolerable diagnosability), denoted by , is maximum number of faulty nodes that a system G can guarantee to locate when the number of faulty links does not exceed h. As applications, the diagnosabilities of many networks with missing edges are determined under the PMC model.
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PMC模型下正则图的混合故障诊断能力分析
可诊断性是衡量多处理机系统故障识别能力的重要指标。然而,大多数关于可诊断性的研究都集中在顶点故障上。在实际情况下,不仅会发生顶点故障,还可能出现故障。本文研究了缺边k-正则2-cn图的可诊断性。设为图G中缺失边的集合。我们证明的可诊断性最多为。进一步,我们得到了最坏情况可诊断性(h边可容忍可诊断性),用表示为当故障链路数不超过h时,系统G能保证定位的最大故障节点数。作为应用,在PMC模型下确定了许多缺边网络的可诊断性。
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来源期刊
International Journal of Computer Mathematics: Computer Systems Theory
International Journal of Computer Mathematics: Computer Systems Theory Computer Science-Computational Theory and Mathematics
CiteScore
1.80
自引率
0.00%
发文量
11
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