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作者机构:College of Mathematics and Informatics Fujian Normal University Fuzhou Fujian China Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University Fuzhou Fujian China School of Mathematics and Physics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou Fujian China
出 版 物:《International Journal of Computer Mathematics: Computer Systems Theory》 (Int. J. Comput. Math.: Comput. Syst. Theory)
年 卷 期:2018年第3卷第4期
页 面:230-243页
学科分类:07[理学] 0714[理学-统计学(可授理学、经济学学位)] 070102[理学-计算数学] 0701[理学-数学] 0812[工学-计算机科学与技术(可授工学、理学学位)]
基 金:National Natural Science Foundation of China, NSFC, (61572010, 61702100, 61702103, U1405255) China Postdoctoral Science Foundation, (2017M612107, 2018T110636) Natural Science Foundation of Fujian Province, (2016J01289) Department of Education, Fujian Province, (JAT170397) Fujian University of Technology, FJUT, (GY-Z17008) Fujian Normal University Specialized Research Fund for the Doctoral Program of Higher Education of China, SRFDP, (20113219120019)
主 题:-Diagnosability balanced hypercube body fault tolerance PMC model
摘 要:The network s diagnosability implies its self-diagnostic capability. Compared with classical sequential diagnosis that has been widely used to identify faulty nodes, the (t, k)- -diagnosis is considered as an improvement of sequential diagnosis, where t ≥ k.. To be more specific, the (t, k)- -diagnosis can identify and replace at least k faulty nodes in each iteration until all t faulty nodes are found. Meanwhile, compared with the other hypercube graphs, the balanced hypercube (denoted BHn) has many good properties, such as a better fault-tolerant embedding capability and a smaller diameter. In this paper, we first propose a body discover algorithm (named BD) of BHn, where a body consists of node sets that meet specific conditions. Based on bodies found by BD, we verify that the BHn is (t, k)- -diagnosable. Then we prove that the (t, k)-diagnosability of BHn is (2n−1, 2n) under the PMC model. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.