A Survey on Automated Log Analysis for Reliability Engineering

Author:

He Shilin1,He Pinjia2,Chen Zhuangbin3,Yang Tianyi3,Su Yuxin3,Lyu Michael R.3

Affiliation:

1. Microsoft Research, Beijing, China

2. Department of Computer Science, ETH Zurich, Zürich, Switzerland

3. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

Abstract

Logs are semi-structured text generated by logging statements in software source code. In recent decades, software logs have become imperative in the reliability assurance mechanism of many software systems, because they are often the only data available that record software runtime information. As modern software is evolving into a large scale, the volume of logs has increased rapidly. To enable effective and efficient usage of modern software logs in reliability engineering, a number of studies have been conducted on automated log analysis. This survey presents a detailed overview of automated log analysis research, including how to automate and assist the writing of logging statements, how to compress logs, how to parse logs into structured event templates, and how to employ logs to detect anomalies, predict failures, and facilitate diagnosis. Additionally, we survey work that releases open-source toolkits and datasets. Based on the discussion of the recent advances, we present several promising future directions toward real-world and next-generation automated log analysis.

Funder

Research Grants Council of the Hong Kong Special Administrative Region, China

Key-Area Research and Development Program of Guangdong Province

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

Reference203 articles.

1. Performance debugging for distributed systems of black boxes

2. Using finite-state models for log differencing

3. Testing using log file analysis: tools, methods, and issues

4. AspectJ. 2020. Eclipse AspectJ. Retrieved from https://www.eclipse.org/aspectj/. AspectJ. 2020. Eclipse AspectJ. Retrieved from https://www.eclipse.org/aspectj/.

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