Chain-of-event: interpretable root cause analysis for microservices through automatically learning weighted event causal graph
Open accessAbstract
This paper presents Chain-of-Event (CoE), an interpretable model for root cause analysis in microservice systems that analyzes causal relationships of events transformed from multi-modal observation data. CoE distinguishes itself by its interpretable parameter design that aligns with the operation experience of Site Reliability Engineers (SREs), thereby facilitating the integration of their expertise directly into the analysis process. Furthermore, CoE automatically learns event-causal graphs from history incidents and accurately locates root cause events, eliminating the need for manual configuration. Through evaluation on two datasets sourced from an e-commerce system involving over 5,000 services, CoE achieves top-tier performance, with 79.30% top-1 and 98.8% top-3 accuracy on the Service dataset and 85.3% top-1 and 96.6% top-3 accuracy on the Business dataset. An ablation study further explores the significance of each component within the CoE model, offering insights into their individual contributions to the model’s overall effectiveness. Additionally, through real-world case analysis, this paper demonstrates how CoE enhances interpretability and improves incident comprehension for SREs. Our codes are available at https://github.com/NetManAIOps/Chain-of-Event.
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Operational success is inherently multi-objective.
“Operational success is inherently multi-objective and should be represented as a vector rather than a single aggregate score [141], [143], [145].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [143]
Budgets bind autonomy in practice.
“Budgets bind autonomy in practice[20],[21], [143]”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [143]
Cites
MicroRCA: Root Cause Localization of Performance Issues in Microservices
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Soldani, J., & Brogi, A. (2023) · ACM Computing Surveys · doi:10.1145/3501297 · Cited by 256 · see citing works
Eadro: An End-to-End Troubleshooting Framework for Microservices on Multi-source Data
Lee, C., Yang, T., Chen, Z., Su, Y., & Lyu, M. R. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00150 · Cited by 149 · see citing works
Yu, G., Chen, P., Li, Y., Chen, H., Li, X., & Zheng, Z. (2023) · Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3611643.3616249 · Cited by 140 · cited 10× · see citing works
“Nezha [42] converts multi-modal data into events related to user requests and ranks the root causes based on the changes in statistics of the event patterns.”
BackgroundMethodInfluentialGraph-based root cause analysis for service-oriented and microservice architectures
Brandón, Á., Solé, M., Huélamo, A., Solans, D., Pérez, M. S., & Muntés-Mulero, V. (2020) · Journal of Systems and Software · doi:10.1016/j.jss.2019.110432 · Cited by 121 · see citing works
Graph-based trace analysis for microservice architecture understanding and problem diagnosis
Guo, X., Peng, X., Wang, H., Li, W., Jiang, H., Ding, D., Xie, T., & Su, L. (2020) · Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3368089.3417066 · Cited by 118 · see citing works
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Flow-of-action: SOP-enhanced LLM-based multi-agent system for root cause analysis
Pei, C., Wang, Z., Liu, F., Li, Z., Liu, Y., He, X., Kang, R., Zhang, T., Chen, J., Li, J., et al. (2025) · Companion Proceedings of the ACM on Web Conference 2025 · doi:10.1145/3701716.3715225 · Cited by 33 · see citing works
Self-citationZhang, X., Wang, Q., Li, M., Yuan, Y., Xiao, M., Zhuang, F., & Yu, D. (2025) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2025.3629066 · Cited by 15 · see citing works
Wang, J., Chen, R., Wang, Y., Liao, X., Fan, Z., Wu, X., Wu, W., Liu, X., & Zhao, J. (2026) · 2026 IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws72778.2026.00056
“[1]–[3] However, this architectural shift also makes modern systems increasingly complex to operate: failures rarely stay local, and subtle issues can quickly propagate along service dependencies, producing amplified and misleading symptoms downstream [4], [5].”
- Authors
- Yao, Z., Pei, C., Chen, W., Wang, H., Su, L., Jiang, H., Xie, Z., Nie, X., & Pei, D.
- Affiliations
- Tsinghua University (CN)
- Chinese Academy of Sciences (CN)
- eBay Inc., Shanghai, China
- ORCID
- Venue
- Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering
- Publisher
- ACM
- Published
- 2024-07-10
- Pages
- 50-61
- OpenAlex
- W4400484740
- Cited by
- 21
- References
- 6
- Citations by year
- 2026: 13 · 2025: 6 · 2024: 2
- FWCI
- 9.17
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) (publishedVersion, cc-by)
- Funders
- National Natural Science Foundation of China (62072264 and 62202445)
- Beijing National Research Center For Information Science And Technology
- National Key Research and Development Program of China (2022YFB2901800)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Keywords
- root cause analysis, microservices, interpretable models, incident diagnosis, site reliability engineering, e-commerce platforms, AIOps
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