A Survey on Automated Log Analysis for Reliability Engineering
Обзор автоматизированного анализа журналов для обеспечения надежности
2020-09-15
SCID: 54.1/9n7dn9k2
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anomaly detectionautomated log analysisfailure predictionlog parsingreliability engineering
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Abstract (AI)
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.
Key Findings
1
Automated log analysis is essential for reliability engineering because logs are often the only available records of software runtime behavior.
2
Recent advances are reviewed to identify promising directions for real-world and next-generation automated log analysis.
3
The rapid growth of log volume in large-scale software systems motivates efficient and effective automation throughout the log-analysis pipeline.
4
The survey covers open-source automated log-analysis toolkits and datasets, supporting reproducible research and practical adoption.
5
The survey organizes research across automated logging assistance, log compression, structured event-template parsing, anomaly detection, failure prediction, and diagnosis.
Research Object
software logs used in reliability engineering
Research Subject
automated log analysis for reliability assurance, including log compression, parsing, anomaly detection, failure prediction, and diagnosis
Publication Details
Publication Date
2020-09-15
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