Autonomous network monitoring using LLMs and multi-agent systems

Oluwatosin Oladayo Aramide *

Network Engineer (Network Layers and Storage) – MTS IV, IRELAND.
 
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2024, 13(02), 974–985.
Article DOI: 10.30574/wjaets.2024.13.2.0639
Publication history: 
Received on 16 November 2024; revised on 24 December 2024; accepted on 30 December 2024 
 
Abstract: 
This study researches the combination of the concept of large language models (LLMs) and multi-agent systems implementation into autonomous network monitoring. Dedicated to real-time anomaly detection in network telemetry (NetFlow, SNMP, GNMI), the study analyzes the possibility of GPT-based agents in efficiently identifying and responding to a network problem. The study also explores some details of Lang Graph and Auto Gen in construction of multi-agent systems to triage and remediate the network events. A case study on automating Root Cause Analysis (RCA) in spine-leaf topologies illustrates the practical application of these technologies. The results indicate that the detection of anomaly is quite efficient in speed, accuracy, and scalability and serve as a rather efficient way of simplifying network operations. The paper highlights, as the recognition of the role of AI in NetOps increases, the potential of converting network management with enhancements to automation and decision-making processes that LLMs and multi-agent systems provide. The implications are huge in terms of the future of NetOps where network infrastructures can be smarter and self-healing.
 
Keywords: 
Anomaly Detection; Network Telemetry; Multi-Agent Systems; Event Triage; Root Cause; AI-Driven
 
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Copyright information: 
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0