Towards intelligent incident management: why we need it and how we make it
Abstract
The management of cloud service incidents (unplanned interruptions or outages of a service/product) greatly affects customer satisfaction and business revenue. After years of efforts, cloud enterprises are able to solve most incidents automatically and timely. However, in practice, we still observe critical service incidents that occurred in an unexpected manner and orchestrated diagnosis workflow failed to mitigate them. In order to accelerate the understanding of unprecedented incidents and provide actionable recommendations, modern incident management system employs the strategy of AIOps (Artificial Intelligence for IT Operations). In this paper, to provide a broad view of industrial incident management and understand the modern incident management system, we conduct a comprehensive empirical study spanning over two years of incident management practices at Microsoft. Particularly, we identify two critical challenges (namely, incomplete service/resource dependencies and imprecise resource health assessment) and investigate the underlying reasons from the perspective of cloud system design and operations. We also present IcM BRAIN, our AIOps framework towards intelligent incident management, and show its practical benefits conveyed to the cloud services of Microsoft.
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In operations, knowing when to stop is part of the safety case.
“In operations, knowing when to stop is part of the safety case [84], [85], [71].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [71]
Incident response has an implicit structure: detect, triage, diagnose, mitigate, and learn.
“Incident response has an implicit structure that is easy to state and hard to operationalise: detect, triage, diagnose, mitigate, and learn [55], [71].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [71]
AIOps depends heavily on workflow artefacts, such as tickets, runbooks, on-call notes, postmortems, and chat transcripts.
“AIOps depends heavily on workflow artefacts, such as, tickets, runbooks, on-call notes, postmortems, and chat transcripts [71].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [71]
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- Authors
- Chen, Z., Kang, Y., Li, L., Zhang, X., Zhang, H., Xu, H., Zhou, Y., Yang, L., Sun, J., Xu, Z., Dang, Y., Gao, F., Zhao, P., Qiao, B., Lin, Q., Zhang, D., & Lyu, M. R.
- Affiliations
- Chinese University of Hong Kong (HK)
- Microsoft Research Asia (China) (CN)
- University of Newcastle Australia (AU)
- Fudan University (CN)
- Microsoft (United States) (US)
- ORCID
- Venue
- Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
- Publisher
- ACM
- Published
- 2020-11-08
- Pages
- 1487-1497
- OpenAlex
- W3047881323
- Cited by
- 99
- References
- 0
- Citations by year
- 2026: 13 · 2025: 20 · 2024: 21 · 2023: 26 · 2022: 11 · 2021: 8
- FWCI
- 9.44
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) (publishedVersion)
- NOVA (University of Newcastle Australia) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed systems and fault tolerance
- Keywords
- incident management, AIOps, incident diagnosis, IT operations automation, Microsoft cloud services
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