Towards LLM-Based Failure Localization in Production-Scale Networks
Chenxu Wang, Xumiao Zhang, Runwei Lu, Xianshang Lin, Xuan Zeng, Xinlei Zhang, Zhe An, Gongwei Wu, Jiaqi Gao, Chen Tian, Guihai Chen, G. M. Liu, Yuhong Liao, Tao Lin, Dennis Cai, Ennan Zhai · 2025
Root causing and failure localization are critical to maintain reliability in cloud network operations. When an incident is reported, network operators must review massive volumes of monitoring data and identify the root cause (i.e., error device) as fast as possible, making it extremely challenging even for experienced operators. Large language models (LLMs) have shown great potential in text understanding and reasoning. In this paper, we present BiAn, an LLM-based framework designed to assist operators in efficient incident investigation. BiAn processes monitoring data and generates error device rankings with detailed explanations. To date, BiAn has been deployed in our network infrastructure for 10 months and it has successfully assisted operators in identifying error devices more quickly, reducing time to root causing by 20.5% (55.2% for high-risk incidents). Extensive performance evaluations based on 17 months of real cases further demonstrate that BiAn achieves accurate and fast failure localization. It improves accuracy by 9.2% compared to the baseline approach.