OFCnetLLM: Large Language Model for Network Monitoring and Alertness

• URL: http://arxiv.org/abs/2507.22711v1
• Date: Wed, 30 Jul 2025 14:22:42 GMT
• Title: OFCnetLLM: Large Language Model for Network Monitoring and Alertness
• Authors: Hong-Jun Yoon, Mariam Kiran, Danial Ebling, Joe Breen,
• Abstract summary: This paper explores the use of Large Language Models (LLMs) to revolutionize network monitoring management.<n>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.<n>Our model is developed as a multi-agent approach and is still evolving, and we present early results here.
• Score: 0.7379838047227086
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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