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| Auteurs principaux: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2402.17531 |
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| _version_ | 1866913345612808192 |
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| author | An, Kaikai Yang, Fangkai Lu, Junting Li, Liqun Ren, Zhixing Huang, Hao Wang, Lu Zhao, Pu Kang, Yu Ding, Hua Lin, Qingwei Rajmohan, Saravan Zhang, Dongmei Zhang, Qi |
| author_facet | An, Kaikai Yang, Fangkai Lu, Junting Li, Liqun Ren, Zhixing Huang, Hao Wang, Lu Zhao, Pu Kang, Yu Ding, Hua Lin, Qingwei Rajmohan, Saravan Zhang, Dongmei Zhang, Qi |
| contents | Effective incident management is pivotal for the smooth operation of enterprises-level cloud services. In order to expedite incident mitigation, service teams compile troubleshooting knowledge into Troubleshooting Guides (TSGs) accessible to on-call engineers (OCEs). While automated pipelines are enabled to resolve the most frequent and easy incidents, there still exist complex incidents that require OCEs' intervention. However, TSGs are often unstructured and incomplete, which requires manual interpretation by OCEs, leading to on-call fatigue and decreased productivity, especially among new-hire OCEs. In this work, we propose Nissist which leverages TSGs and incident mitigation histories to provide proactive suggestions, reducing human intervention. Leveraging Large Language Models (LLM), Nissist extracts insights from unstructured TSGs and historical incident mitigation discussions, forming a comprehensive knowledge base. Its multi-agent system design enhances proficiency in precisely discerning user queries, retrieving relevant information, and delivering systematic plans consecutively. Through our user case and experiment, we demonstrate that Nissist significant reduce Time to Mitigate (TTM) in incident mitigation, alleviating operational burdens on OCEs and improving service reliability. Our demo is available at https://aka.ms/nissist_demo. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_17531 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Nissist: An Incident Mitigation Copilot based on Troubleshooting Guides An, Kaikai Yang, Fangkai Lu, Junting Li, Liqun Ren, Zhixing Huang, Hao Wang, Lu Zhao, Pu Kang, Yu Ding, Hua Lin, Qingwei Rajmohan, Saravan Zhang, Dongmei Zhang, Qi Software Engineering Artificial Intelligence Computation and Language Effective incident management is pivotal for the smooth operation of enterprises-level cloud services. In order to expedite incident mitigation, service teams compile troubleshooting knowledge into Troubleshooting Guides (TSGs) accessible to on-call engineers (OCEs). While automated pipelines are enabled to resolve the most frequent and easy incidents, there still exist complex incidents that require OCEs' intervention. However, TSGs are often unstructured and incomplete, which requires manual interpretation by OCEs, leading to on-call fatigue and decreased productivity, especially among new-hire OCEs. In this work, we propose Nissist which leverages TSGs and incident mitigation histories to provide proactive suggestions, reducing human intervention. Leveraging Large Language Models (LLM), Nissist extracts insights from unstructured TSGs and historical incident mitigation discussions, forming a comprehensive knowledge base. Its multi-agent system design enhances proficiency in precisely discerning user queries, retrieving relevant information, and delivering systematic plans consecutively. Through our user case and experiment, we demonstrate that Nissist significant reduce Time to Mitigate (TTM) in incident mitigation, alleviating operational burdens on OCEs and improving service reliability. Our demo is available at https://aka.ms/nissist_demo. |
| title | Nissist: An Incident Mitigation Copilot based on Troubleshooting Guides |
| topic | Software Engineering Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2402.17531 |