LogShrink: Effective Log Compression by Leveraging Commonality and Variability of Log Data-Reference-Cited by-同舟云学术

LogShrink: Effective Log Compression by Leveraging Commonality and Variability of Log Data

Published:2024-02-06 Issue: Volume:48 Page:1-12
ISSN:
Container-title:Proceedings of the IEEE/ACM 46th International Conference on Software Engineering
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Author:

Li Xiaoyun¹^ORCID,Zhang Hongyu²^ORCID,Le Van-Hoang³^ORCID,Chen Pengfei¹^ORCID

Affiliation:

1. Sun Yat-sen University, Guangzhou, Guangdong, China

2. Chongqing University, Chongqing, China

3. The University of Newcastle, Newcastle, Australia

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Guangdong Basic and Applied Basic Research Foundation

Australian Research Council Discovery Projects

China Scholarship Council

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3597503.3608129

Reference62 articles.

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3. Vaibhav Agrawal et al. "Log-based cloud monitoring system for OpenStack". In: 2018 IEEE Fourth International Conference on Big Data Computing Service and Applications (BigDataService). IEEE. 2018, pp. 276--281.

4. Kundi Yao et al. "Log4perf: Suggesting logging locations for web-based systems' performance monitoring". In: Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering. 2018, pp. 127--138.

5. Shilin He et al. "Identifying impactful service system problems via log analysis". In: Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 2018, pp. 60--70.

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