Root Cause Analysis for Microservice Systems via Hierarchical Reinforcement Learning from Human Feedback-Reference-Cited by-同舟云学术

Root Cause Analysis for Microservice Systems via Hierarchical Reinforcement Learning from Human Feedback

Published:2023-08-04 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
language:
Short-container-title:

Author:

Wang Lu¹^ORCID,Zhang Chaoyun¹^ORCID,Ding Ruomeng²^ORCID,Xu Yong¹^ORCID,Chen Qihang³^ORCID,Zou Wentao⁴^ORCID,Chen Qingjun⁴^ORCID,Zhang Meng⁴^ORCID,Gao Xuedong⁴^ORCID,Fan Hao⁴^ORCID,Rajmohan Saravan⁵^ORCID,Lin Qingwei¹^ORCID,Zhang Dongmei¹^ORCID

Affiliation:

1. Microsoft, Beijing, China

2. Georgia Tech, Atlanta, GA, USA

3. Peking University, Beijing, China

4. Microsoft, Suzhou, China

5. Microsoft 365, Seattle, WA, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599934

Reference45 articles.

1. Jacopo Soldani and Antonio Brogi . Anomaly detection and failure root cause analysis in (micro) service-based cloud applications: A survey. ACM Computing Surveys (CSUR), 55(3):1--39 , 2022 . Jacopo Soldani and Antonio Brogi. Anomaly detection and failure root cause analysis in (micro) service-based cloud applications: A survey. ACM Computing Surveys (CSUR), 55(3):1--39, 2022.

2. Haifeng Liu , Jinjun Zhang , Huasong Shan , Min Li , Yuan Chen , Xiaofeng He , and Xiaowei Li. Jcallgraph : tracing microservices in very large scale container cloud platforms. In Cloud Computing-CLOUD 2019: 12th International Conference, Held as Part of the Services Conference Federation , SCF 2019, San Diego, CA, USA, June 25-30, 2019, Proceedings 12 , pages 287 -- 302 . Springer , 2019 . Haifeng Liu, Jinjun Zhang, Huasong Shan, Min Li, Yuan Chen, Xiaofeng He, and Xiaowei Li. Jcallgraph: tracing microservices in very large scale container cloud platforms. In Cloud Computing-CLOUD 2019: 12th International Conference, Held as Part of the Services Conference Federation, SCF 2019, San Diego, CA, USA, June 25-30, 2019, Proceedings 12, pages 287--302. Springer, 2019.

3. Fault Analysis and Debugging of Microservice Systems: Industrial Survey, Benchmark System, and Empirical Study

4. TraceArk: Towards Actionable Performance Anomaly Alerting for Online Service Systems

5. HALO: Hierarchy-aware Fault Localization for Cloud Systems

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Illuminating the Gray Zone: Non-intrusive Gray Failure Localization in Server Operating Systems;Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering;2024-07-10

2. MULAN: Multi-modal Causal Structure Learning and Root Cause Analysis for Microservice Systems;Proceedings of the ACM Web Conference 2024;2024-05-13

3. Xpert: Empowering Incident Management with Query Recommendations via Large Language Models;Proceedings of the IEEE/ACM 46th International Conference on Software Engineering;2024-04-12

4. Elevating Strategy with Cloud-Based Reinforcement Learning for Dynamic Decision-Making in Modern Enterprises;2024 3rd International Conference for Innovation in Technology (INOCON);2024-03-01

5. TraceDiag: Adaptive, Interpretable, and Efficient Root Cause Analysis on Large-Scale Microservice Systems;Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering;2023-11-30