Optimizing IP Network Configuration Management with XML and Machine Learning Techniques

EasyChair Preprint 15454

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.

Keyphrases: Configuration Management, Optimization, Reinforcement Learning, adaptive systems, machine learning

@booklet{EasyChair:15454,
  author    = {Maria Kin and Elvard John and Mehmmet Amin},
  title     = {Optimizing IP Network Configuration Management with XML and Machine Learning Techniques},
  howpublished = {EasyChair Preprint 15454},
  year      = {EasyChair, 2024}}