S. Rao, Minghui Wang, Cuixia Tian, Xin’an Yang, Xiang Ao
{"title":"一种基于层次树的网络诊断Syslog日志聚类方案","authors":"S. Rao, Minghui Wang, Cuixia Tian, Xin’an Yang, Xiang Ao","doi":"10.23919/CNSM52442.2021.9615506","DOIUrl":null,"url":null,"abstract":"With the continuous development of Information Technology, modern networks have been widely utilised. Since the complex network structure causes growing difficulties in maintenance, log analysis has been widely studied in recent years for network diagnosis. System log clustering is mainly focused for root cause analysis. In this paper, a hierarchical tree-based clustering scheme is proposed that could accurately group system logs according to both time and network constraints without any training and parameter settings. Furthermore, it largely accelerates the matching process by reducing matching times and significantly boosts the performance of hit rate (100%) and match efficiency (16%) comparing to other clustering strategies, which greatly helps with precise network diagnosis.","PeriodicalId":358223,"journal":{"name":"2021 17th International Conference on Network and Service Management (CNSM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A Hierarchical Tree-Based Syslog Clustering Scheme for Network Diagnosis\",\"authors\":\"S. Rao, Minghui Wang, Cuixia Tian, Xin’an Yang, Xiang Ao\",\"doi\":\"10.23919/CNSM52442.2021.9615506\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the continuous development of Information Technology, modern networks have been widely utilised. Since the complex network structure causes growing difficulties in maintenance, log analysis has been widely studied in recent years for network diagnosis. System log clustering is mainly focused for root cause analysis. In this paper, a hierarchical tree-based clustering scheme is proposed that could accurately group system logs according to both time and network constraints without any training and parameter settings. Furthermore, it largely accelerates the matching process by reducing matching times and significantly boosts the performance of hit rate (100%) and match efficiency (16%) comparing to other clustering strategies, which greatly helps with precise network diagnosis.\",\"PeriodicalId\":358223,\"journal\":{\"name\":\"2021 17th International Conference on Network and Service Management (CNSM)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 17th International Conference on Network and Service Management (CNSM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/CNSM52442.2021.9615506\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 17th International Conference on Network and Service Management (CNSM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/CNSM52442.2021.9615506","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Hierarchical Tree-Based Syslog Clustering Scheme for Network Diagnosis
With the continuous development of Information Technology, modern networks have been widely utilised. Since the complex network structure causes growing difficulties in maintenance, log analysis has been widely studied in recent years for network diagnosis. System log clustering is mainly focused for root cause analysis. In this paper, a hierarchical tree-based clustering scheme is proposed that could accurately group system logs according to both time and network constraints without any training and parameter settings. Furthermore, it largely accelerates the matching process by reducing matching times and significantly boosts the performance of hit rate (100%) and match efficiency (16%) comparing to other clustering strategies, which greatly helps with precise network diagnosis.