Towards Autonomous Network Management: AI-Driven Framework for Intelligent Log Analysis, Troubleshooting and Documentation
Abstract
As modern network management grows increasingly complex, administrators are tasked with navigating vast volumes of log data, often resulting in inefficiencies, errors, and operational challenges. My doctoral research addresses these pressing issues by leveraging advanced AI techniques to minimize human intervention and pave the way for fully automated network operations. I propose a novel AI-driven framework that integrates Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) and a human-in-the-loop process to effectively automate key network management tasks, including log analysis, troubleshooting recommendations, and documentation generation. By enhancing the accuracy and efficiency of these tasks, this study aims to improve network reliability, reduce operational complexity, and contribute to the evolution of self-running networks.
Cite
Text
Shajarian. "Towards Autonomous Network Management: AI-Driven Framework for Intelligent Log Analysis, Troubleshooting and Documentation." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I28.35226Markdown
[Shajarian. "Towards Autonomous Network Management: AI-Driven Framework for Intelligent Log Analysis, Troubleshooting and Documentation." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/shajarian2025aaai-autonomous/) doi:10.1609/AAAI.V39I28.35226BibTeX
@inproceedings{shajarian2025aaai-autonomous,
title = {{Towards Autonomous Network Management: AI-Driven Framework for Intelligent Log Analysis, Troubleshooting and Documentation}},
author = {Shajarian, Shaghayegh},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2025},
pages = {29297-29298},
doi = {10.1609/AAAI.V39I28.35226},
url = {https://mlanthology.org/aaai/2025/shajarian2025aaai-autonomous/}
}