- Title
- Outage Prediction and Diagnosis for Cloud Service Systems
- Creator
- Chen, Yujun; Zhang, Hongyu; Xu, Zhangwei; Dang, Yingnong; Yang, Xian; Lin, Qingwei; Zhang, Dongmei; Dong, Hang; Xu, Yong; Li, Hao; Kang, Yu; Gao, Feng
- Relation
- WWW '19: The Web Conference. Proceedings of The Web Conference 2019 (San Francisco, CA 13-17 May, 2019) p. 2659-2665
- Publisher Link
- http://dx.doi.org/10.1145/3308558.3313501
- Publisher
- Association for Computing Machinery
- Resource Type
- conference paper
- Date
- 2019
- Description
- With the rapid growth of cloud service systems and their increasing complexity, service failures become unavoidable. Outages, which are critical service failures, could dramatically degrade system availability and impact user experience. To minimize service downtime and ensure high system availability, we develop an intelligent outage management approach, called AirAlert, which can forecast the occurrence of outages before they actually happen and diagnose the root cause after they indeed occur. AirAlert works as a global watcher for the entire cloud system, which collects all alerting signals, detects dependency among signals and proactively predicts outages that may happen anywhere in the whole cloud system. We analyze the relationships between outages and alerting signals by leveraging Bayesian network and predict outages using a robust gradient boosting tree based classification method. The proposed outage management approach is evaluated using the outage dataset collected from a Microsoft cloud system and the results confirm the effectiveness of the proposed approach.
- Subject
- outage prediction; outage diagnosis; cloud system; system of systems; service availability
- Identifier
- http://hdl.handle.net/1959.13/1477995
- Identifier
- uon:50076
- Identifier
- ISBN:9781450366748
- Rights
- This paper is published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) (http://creativecommons.org/licenses/by-nc-nd/4.0/) license. Authors reserve their rights to disseminate the work on their personal and corporate Web sites with the appropriate attribution.
- Language
- eng
- Full Text
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