Leveraging Machine Learning for Efficient XML-Based IP Network Configuration

EasyChair Preprint 15765

Abstract

As IP network infrastructures grow increasingly complex, innovative and adaptive configuration management solutions are essential. This paper presents an advanced framework driven by artificial intelligence, integrating machine learning and reinforcement learning to optimize the configuration of IP network devices. By leveraging a comprehensive dataset of configuration parameters and performance metrics, the framework achieves an impressive 88% accuracy in identifying optimal configurations, outperforming traditional methods. With an average response time of 150 milliseconds for applying changes, the framework ensures swift performance.

A standout feature of the framework is its reinforcement learning agent, which dynamically adapts to changing network conditions and progressively enhances decision-making. To assist network administrators, the framework includes an intuitive interface for real-time monitoring and configuration visualization. Experimental evaluations underscore its potential to streamline configuration management and proactively address network challenges. Future work will focus on enhancing scalability, integrating emerging technologies, and incorporating user feedback to further refine and expand the framework's functionality.

Keyphrases: AI-driven Framework, Configuration Management, Reinforcement Learning, adaptive systems, machine learning

@booklet{EasyChair:15765,
  author    = {Kin Elvard},
  title     = {Leveraging Machine Learning for Efficient XML-Based IP Network Configuration},
  howpublished = {EasyChair Preprint 15765},
  year      = {EasyChair, 2025}}