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Poster: Intelligent Network Management: RAG-Enhanced LLMs for Log Analysis, Troubleshooting, and Documentation

Published: 09 December 2024 Publication History

Abstract

Modern network management is increasingly complex, requiring administrators to handle vast amounts of log data from diverse sources, leading to inefficiencies, errors, and operational challenges. In this work, we propose a novel AI-driven framework that integrates Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) and human-in-the-loop process to automate network management tasks such as log analysis, troubleshooting recommendations, and documentation generation. This study aims to enhance network reliability, reduce operational complexity, and move forward to autonomous network management.

References

[1]
Nick Feamster and Jennifer Rexford. 2017. Why (and how) networks should run themselves. arXiv preprint arXiv:1710.11583 (2017).
[2]
Shilin He, Xu Zhang, Pinjia He, Yong Xu, Liqun Li, Yu Kang, Minghua Ma, Yining Wei, Yingnong Dang, Saravanakumar Rajmohan, et al. 2022. An empirical study of log analysis at Microsoft. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 1465--1476.
[3]
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems, Vol. 33 (2020), 9459--9474.

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                  cover image ACM Conferences
                  CoNEXT '24: Proceedings of the 20th International Conference on emerging Networking EXperiments and Technologies
                  December 2024
                  80 pages
                  ISBN:9798400711084
                  DOI:10.1145/3680121
                  Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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                  Publication History

                  Published: 09 December 2024

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                  Author Tags

                  1. large language models (llms)
                  2. network management
                  3. retrieval-augmented generation (rag).

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                  Overall Acceptance Rate 198 of 789 submissions, 25%

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