Identifying Impactful Service System Problems via Log Analysis
Refereed conference paper presented and published in conference proceedings


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AbstractLogs are often used for troubleshooting in large-scale software systems. For a cloud-based online system that provides 24/7 service, a huge number of logs could be generated every day. However, these logs are highly imbalanced in general, because most logs indicate normal system operations, and only a small percentage of logs reveal impactful problems. Problems that lead to the decline of system KPIs (Key Performance Indicators) are impactful and should be fixed by engineers with a high priority. Furthermore, there are various types of system problems, which are hard to be distinguished manually. In this paper, we propose Log3C, a novel clustering-based approach to promptly and precisely identify impactful system problems, by utilizing both log sequences (a sequence of log events) and system KPIs. More specifically, we design a novel cascading clustering algorithm, which can greatly save the clustering time while keeping high accuracy by iteratively sampling, clustering, and matching log sequences. We then identify the impactful problems by correlating the clusters of log sequences with system KPIs. Log3C is evaluated on real-world log data collected from an online service system at Microsoft, and the results confirm its effectiveness and efficiency. Furthermore, our approach has been successfully applied in industrial practice.
All Author(s) ListShilin He, Qingwei Lin, Jian-Guang Lou, Hongyu Zhang, Michael R.Lyu, Dongmei Zhang
Name of Conference26th ACM Joint Meeting on European Software Engineering Conference (ESEC) / Symposium on the Foundations of Software Engineering (FSE)
Start Date of Conference04/11/2018
End Date of Conference09/11/2018
Place of ConferenceFlorida, United States
Country/Region of ConferenceUnited States of America
Proceedings TitleProceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
Year2018
Month11
PublisherACM
Pages60 - 70
ISBN978-1-4503-5573-5
LanguagesEnglish-United States

Last updated on 2020-03-06 at 00:18