Version 1
: Received: 25 October 2024 / Approved: 28 October 2024 / Online: 29 October 2024 (02:43:20 CET)
How to cite:
Amin, M. AI-Driven Configuration Management for IP Network Devices Using XML and Machine Learning. Preprints2024, 2024102197. https://doi.org/10.20944/preprints202410.2197.v1
Amin, M. AI-Driven Configuration Management for IP Network Devices Using XML and Machine Learning. Preprints 2024, 2024102197. https://doi.org/10.20944/preprints202410.2197.v1
Amin, M. AI-Driven Configuration Management for IP Network Devices Using XML and Machine Learning. Preprints2024, 2024102197. https://doi.org/10.20944/preprints202410.2197.v1
APA Style
Amin, M. (2024). AI-Driven Configuration Management for IP Network Devices Using XML and Machine Learning. Preprints. https://doi.org/10.20944/preprints202410.2197.v1
Chicago/Turabian Style
Amin, M. 2024 "AI-Driven Configuration Management for IP Network Devices Using XML and Machine Learning" Preprints. https://doi.org/10.20944/preprints202410.2197.v1
Abstract
The increasing complexity of IP network infrastructures necessitates efficient and adaptive configuration management solutions. This paper presents an AI-driven framework that integrates machine learning and reinforcement learning techniques to enhance the management of configurations for IP network devices. The proposed approach leverages a comprehensive dataset comprising configuration parameters and performance metrics to train predictive models. Experimental results demonstrate that the framework achieves an accuracy of 88% in predicting optimal configurations, significantly outperforming traditional methods. Additionally, the framework exhibits an average response time of 150 milliseconds for applying configuration changes, underscoring its efficiency. A reinforcement learning agent is implemented to adapt to dynamic network conditions, yielding improved decision- making over time. The user interface designed for the framework facilitates real-time monitoring and visualization of network configurations. The findings suggest that the AI- driven framework not only streamlines configuration management processes but also empowers network administrators to proactively address network challenges. Future work will focus on scalability, integration with emerging technologies, and user feedback mechanisms to further enhance the framework's effectiveness.
Keywords
AI-driven Framework; Configuration Management; Reinforcement Learning; Machine Learning; Adaptive Systems
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
