一句话：基于微软两年云服务事件管理实践，指出依赖不完整与健康评估不准两大挑战，并提出 AIOps 框架 IcM BRAIN。
论文关注云服务事故管理：尽管多数事故可自动及时处理，仍存在以意外方式发生、既有编排诊断流程无法缓解的关键事故。为理解现代事故管理并加速对未遇事故的认知、给出可操作建议，作者在微软开展了跨越两年的事件管理实践实证研究。研究识别出两个关键挑战：服务/资源依赖不完整、资源健康评估不精确，并从云系统设计与运维角度分析其根因。作者提出面向智能事件管理的 AIOps 框架 IcM BRAIN，并报告其在微软云服务中的实际收益。评估基于工业实践与部署经验，而非受控实验。
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.
DOI 原文 ·
@inproceedings{paperbot5784,
title = {Towards intelligent incident management: why we need it and how we make it},
author = {Zhuangbin Chen and Yu Kang and Liqun Li and Xu Zhang and Hongyu Zhang and Hui Xu and Yangfan Zhou and Li Yang and Jeffrey Sun and Zhangwei Xu and Yingnong Dang and Feng Gao and Pu Zhao and Bo Qiao and Qingwei Lin and Dongmei Zhang and Michael R. Lyu},
booktitle = {FSE},
year = {2020},
doi = {10.1145/3368089.3417055}
}