UiLog： Improving Log-Based Fault Diagnosis by Log Analysis

• Published in: Journal of computer science and technology Vol. 31; no. 5; pp. 1038 - 1052
• Main Authors: Zou, De-Qing, Qin, Hao, Jin, Hai
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2016; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Analysis; Artificial Intelligence; Classification; Cloud computing; Computer programs; Computer Science; Computers; Correlation analysis; Data Structures and Information Theory; Fault detection; Fault diagnosis; Faults; Information Systems Applications (incl.Internet); Laboratories; Logs; Manuals; Regular Paper; Root cause analysis; Roots; Software; Software Engineering; Studies; Systems analysis; Systems management; Theory of Computation; 事件日志; 故障诊断; 日志信息; 日志分析; 检查系统; 相关分析方法; 管理员; 计算机系统; log classification; fault correlation analysis; fault diagnosis; system event log
• ISSN: 1000-9000; 1860-4749
• DOI: 10.1007/s11390-016-1678-7

In modern computer systems, system event logs have always been the primary source for checking system status. As computer systems become more and more complex, the interaction between software and hardware increases frequently. The components will generate enormous log information, including running reports and fault information. The sheer quantity of data is a great challenge for analysis relying on the manual method. In this paper, we implement a management and analysis system of log information, which can assist system administrators to understand the real-time status of the entire system, classify logs into different fault types, and determine the root cause of the faults. In addition, we improve the existing fault correlation analysis method based on the results of system log classification. We apply the system in a cloud computing environment for evaluation. The results show that our system can classify fault logs automatically and effectively. With the proposed system, administrators can easily detect the root cause of faults.