{"title":"Reliability of Divide-and-Swap Cube Based on r-Component Connectivity and Diagnosability","authors":"Qianru Zhou, Shuming Zhou, Xiaoqing Liu, Zhengqi Yu","doi":"10.1142/s0219265921420214","DOIUrl":null,"url":null,"abstract":"When performing big data processing and high-performance computing, large-scale multiprocessor systems have always been committed to ensuring high reliability and fault tolerance. Connectivity and diagnosability are two important metrics to evaluate the fault tolerance and reliability of interconnection networks. In this paper, we determine [Formula: see text]-component connectivity and diagnosability of divide-and-swap cube [Formula: see text]. In detail, we show that, for [Formula: see text] and [Formula: see text], [Formula: see text], [Formula: see text] under the PMC and MM[Formula: see text] models.","PeriodicalId":153590,"journal":{"name":"J. Interconnect. Networks","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"J. Interconnect. Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/s0219265921420214","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
When performing big data processing and high-performance computing, large-scale multiprocessor systems have always been committed to ensuring high reliability and fault tolerance. Connectivity and diagnosability are two important metrics to evaluate the fault tolerance and reliability of interconnection networks. In this paper, we determine [Formula: see text]-component connectivity and diagnosability of divide-and-swap cube [Formula: see text]. In detail, we show that, for [Formula: see text] and [Formula: see text], [Formula: see text], [Formula: see text] under the PMC and MM[Formula: see text] models.