基于决策树的企业信息系统故障自动诊断分析方法

doi:10.11959/j.issn.1000-0801.2017058

Abstract

Abstract:

With the rapid growth of the scale and complexity of enterprise information systems, traditional fault diagnosis and analysis methods relying on human experiences and manual operations cost more and more labor and time. To solve this problem, an automatic algorithm was proposed. The algorithm exploits information from system operation monitoring indicators and alarm data, based on decision tree, to automatically diagnose and analyze faults of enterprise information systems. The algorithm was verified, and the decision tree model and training method was simulated and analyzed comparatively under R language environment, using alarm data extracted from real operation data of a typical large-scale enterprise system. The experiment results show that this algorithm is able to achieve fast automatic fault diagnosis accurately, and is much helpful on improving efficiencies of information system fault processing.

Key words: automatic diagnosis analysis, fault of information system, decision tree, R language

CLC Number:

TP319

Xin JIN,Longchuan YAN,Jun LIU,Shulin ZHANG. Enterprise information system automatic fault diagnosis and analysis method based on decision tree[J]. Telecommunications Science, 2017, 33(3): 163-167.

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序号	层次	构成组件及运行状态指标	故障点
1	业务层	在线用户数和健康运行时长指标、URL地址探测、系统可访问性、业务使用情况等	应用服务业务量、用户权限、监控工具运行等
2	应用服务层	应用节点连通性、应用节点可用性、端口、进程、线程、事件、内存、日志、CPU、TCP连接、磁盘、文件使用率、会话连接、代码效率、数据库可连接性、JVM内存、中间件线程等	应用服务 CPU、应用服务 TCP连接、应用服务磁盘、应用服务进程、应用服务内存、应用服务线程、应用服务代码质量、应用服务电源、中间件数据库连接池、中间件内存、负载均衡配置、隔离装置运行等
3	数据层	数据库节点连通性、dbtime、I/O性能、集群等待事件、行锁情况、监听服务、内存、会话数、swap空间、日志空间、数据库表空间、归档空间、数据库密码、集群 RPC通信时长、SQL语句等	数据库 SQL语句、数据库归档空间、数据库监听服务、数据库密码、数据库内存、数据库存储过程、数据库响应时长等

复杂度	C4.5算法正确率	CART算法正确率
cp=0.1	61.5%	50%
cp=0.05	61.5%	65.4%
cp=0.001	84.6%	76.9%

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Enterprise information system automatic fault diagnosis and analysis method based on decision tree

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