MonitorAssistant: Simplifying Cloud Service Monitoring via Large Language Models-Reference-Cited by-同舟云学术

MonitorAssistant: Simplifying Cloud Service Monitoring via Large Language Models

Published:2024-07-10 Issue: Volume: Page:
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Container-title:Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering
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Author:

Yu Zhaoyang¹^ORCID,Ma Minghua²^ORCID,Zhang Chaoyun³^ORCID,Qin Si³^ORCID,Kang Yu³^ORCID,Bansal Chetan²^ORCID,Rajmohan Saravan²^ORCID,Dang Yingnong²^ORCID,Pei Changhua⁴^ORCID,Pei Dan¹^ORCID,Lin Qingwei³^ORCID,Zhang Dongmei³^ORCID

Affiliation:

1. Tsinghua University, Beijing, China / BNRist, Beijing, China

2. Microsoft, Redmond, USA

3. Microsoft, Beijing, China

4. Computer Network Information Center at Chinese Academy of Sciences, Beijing, China

Funder

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3663529.3663826

Reference46 articles.

1. Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, and Shyamal Anadkat. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774.

2. Toufique Ahmed Supriyo Ghosh Chetan Bansal Thomas Zimmermann Xuchao Zhang and Saravan Rajmohan. 2023. Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Models. arXiv preprint arXiv:2301.03797.

3. Continuous Incident Triage for Large-Scale Online Service Systems

4. How incidental are the incidents?

5. Yinfang Chen Huaibing Xie Minghua Ma Yu Kang Xin Gao Liu Shi Yunjie Cao Xuedong Gao Hao Fan and Ming Wen. 2024. Automatic Root Cause Analysis via Large Language Models for Cloud Incidents.

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