Miyuru Dayarathna, Prabhash Akmeemana, S. Perera, Malith Jayasinghe
{"title":"Solution Recommender for System Failure Recovery via Log Event Pattern Matching on a Knowledge Graph: Demo","authors":"Miyuru Dayarathna, Prabhash Akmeemana, S. Perera, Malith Jayasinghe","doi":"10.1145/3093742.3095094","DOIUrl":null,"url":null,"abstract":"System anomalies such as network interruptions, operating system halt, disk crash could result in significant financial losses to organizations. In this demonstration we describe a novel log event analysis framework called Solution Recommender which provides a ranked list of solutions to overcome such system errors. The solution recommender gathers log events via a publisher/subscriber mechanism and indexes them inside the WSO2 Data Analytics Server (DAS). Collected information is analyzed using a knowledge graph which conducts log event pattern matching to identify solutions for system failures. We have implemented the proposed approach on WSO2 Log Analyzer for WSO2 API Manager and tested its functionality. In this paper we describe our experience of implementing the log event recommender interlace, the first such recommender developed in a log event analyzer system. The insights presented here will assist practitioners with implementing such Log Event analysis solutions for real world scenarios.","PeriodicalId":325666,"journal":{"name":"Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems","volume":"51 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3093742.3095094","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
System anomalies such as network interruptions, operating system halt, disk crash could result in significant financial losses to organizations. In this demonstration we describe a novel log event analysis framework called Solution Recommender which provides a ranked list of solutions to overcome such system errors. The solution recommender gathers log events via a publisher/subscriber mechanism and indexes them inside the WSO2 Data Analytics Server (DAS). Collected information is analyzed using a knowledge graph which conducts log event pattern matching to identify solutions for system failures. We have implemented the proposed approach on WSO2 Log Analyzer for WSO2 API Manager and tested its functionality. In this paper we describe our experience of implementing the log event recommender interlace, the first such recommender developed in a log event analyzer system. The insights presented here will assist practitioners with implementing such Log Event analysis solutions for real world scenarios.
网络中断、操作系统停止、磁盘崩溃等系统异常可能给组织造成重大的经济损失。在这个演示中,我们描述了一个新的日志事件分析框架,称为解决方案推荐器,它提供了一个解决方案的排序列表,以克服此类系统错误。解决方案推荐器通过发布者/订阅者机制收集日志事件,并在WSO2 Data Analytics Server (DAS)中对它们进行索引。利用知识图对收集到的信息进行分析,并进行日志事件模式匹配,以确定系统故障的解决方案。我们已经在WSO2 API管理器的WSO2日志分析器上实现了所提出的方法,并测试了其功能。在本文中,我们描述了我们实现日志事件推荐器的经验,这是在日志事件分析系统中开发的第一个这样的推荐器。这里提出的见解将帮助从业者为现实世界的场景实现这样的Log Event分析解决方案。