用大模型提升网络监控效率，自动发现异常与故障根源。
OFCnetLLM: Large Language Model for Network Monitoring and Alertness
- 基于多智能体架构的LLM实现自动化监控
- 在OFC会议网络中实测，显著提升异常检测速度
- 适合网络运维与AI融合研究者参考
网络基础设施快速发展，带来高效管理、优化与安全的新挑战。海量监控数据存储成本高，难以高效探索。本文探索利用大语言模型（LLM）革新网络监控管理，解决查询生成与模式分析的局限性。通过构建基于开源LLM的OFCNetLLM，采用多智能体架构，实现异常检测自动化、根因分析与事件分析的智能化。在真实场景——OFC会议网络中进行了应用验证，展示了早期实用效果。该模型仍在持续演化，为构建以AI为核心的智能网络管理团队提供可行路径。
原文摘要 · Abstract (English)
The rapid evolution of network infrastructure is bringing new challenges and opportunities for efficient network management, optimization, and security. With very large monitoring databases becoming expensive to explore, the use of AI and Generative AI can help reduce costs of managing these datasets. This paper explores the use of Large Language Models (LLMs) to revolutionize network monitoring management by addressing the limitations of query finding and pattern analysis. We leverage LLMs to enhance anomaly detection, automate root-cause analysis, and automate incident analysis to build a well-monitored network management team using AI. Through a real-world example of developing our own OFCNetLLM, based on the open-source LLM model, we demonstrate practical applications of OFCnetLLM in the OFC conference network. Our model is developed as a multi-agent approach and is still evolving, and we present early results here.
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