OFCnetLLM: Large Language Model for Network Monitoring and Alertness

TL;DR

This paper introduces OFCnetLLM, a large language model designed for network monitoring and alertness, aiming to improve anomaly detection, root-cause analysis, and incident management using AI in real-world networks.

Contribution

The paper presents a novel multi-agent LLM-based system, OFCnetLLM, tailored for network management tasks, demonstrating practical applications and early results in a real conference network.

Findings

Enhanced anomaly detection capabilities

Automated root-cause analysis demonstrated

Initial positive results in real-world deployment

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.