基于动态运行维护图和常见报警点分析的故障定位方法

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Algorithms Pub Date : 2024-05-16 DOI:10.3390/a17050217
Sheng Wu, Jihong Guan
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引用次数: 0

摘要

在分布式信息系统下,应用程序、操作系统、数据库、服务器、网络等各种运行组件规模庞大,访问关系错综复杂。各专业 "孤岛 "效应突出,联动机制不足,很难定位导致特定应用出现异常的基础设施组件。目前的研究只能在局部场景发挥作用,其准确性和普适性还非常有限。本文提出了一种基于动态运行图和报警公共点分析的新型故障定位方法。在故障期间,将各种告警实体与动态运行图关联起来,基于图搜索寻址方法获得告警公共点,涵盖部署关系公共点、连接公共点(物理和逻辑)和访问流公共点。与知识图谱方法相比,该方法省去了复杂的知识图谱构建过程,更加简洁高效。此外,与指标相关性分析方法相比,该方法补充了配置相关性信息,定位更加精确。通过实际验证,其故障命中率超过 82%,明显优于现有的主要方法。
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Fault Location Method Based on Dynamic Operation and Maintenance Map and Common Alarm Points Analysis
Under a distributed information system, the scale of various operational components such as applications, operating systems, databases, servers, and networks is immense, with intricate access relationships. The silo effect of each professional is prominent, and the linkage mechanism is insufficient, making it difficult to locate the infrastructure components that cause exceptions under a particular application. Current research only plays a role in local scenarios, and its accuracy and generalization are still very limited. This paper proposes a novel fault location method based on dynamic operation maps and alarm common point analysis. During the fault period, various alarm entities are associated with dynamic operation maps, and alarm common points are obtained based on graph search addressing methods, covering deployment relationship common points, connection common points (physical and logical), and access flow common points. This method, compared with knowledge graph approaches, eliminates the complex process of knowledge graph construction, making it more concise and efficient. Furthermore, in contrast to indicator correlation analysis methods, this approach supplements with configuration correlation information, resulting in more precise positioning. Through practical validation, its fault hit rate exceeds 82%, which is significantly better than the existing main methods.
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来源期刊
Algorithms
Algorithms Mathematics-Numerical Analysis
CiteScore
4.10
自引率
4.30%
发文量
394
审稿时长
11 weeks
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