A Machine Learning-Driven Approach to XML-Based IP Network Configuration Optimization

EasyChair Preprint 15560

Abstract

Managing the ever-growing complexity of IP network infrastructures calls for innovative and adaptive configuration management solutions. This paper introduces an AI-powered framework that combines machine learning and reinforcement learning to optimize the configuration of IP network devices. The framework utilizes a rich dataset of configuration parameters and performance metrics to train predictive models, achieving an accuracy of 88% in identifying optimal configurations—surpassing traditional approaches. Moreover, it delivers swift performance, with an average response time of 150 milliseconds for applying configuration changes. A key component of the framework is a reinforcement learning agent, which adapts to dynamic network conditions and enhances decision-making capabilities over time. To support network administrators, the framework features a user-friendly interface for real-time monitoring and visualization of configurations. Experimental results highlight the framework's potential to simplify configuration management while enabling proactive solutions to network challenges. Future enhancements will explore scalability, seamless integration with emerging technologies, and the incorporation of user feedback to refine and expand the framework’s capabilities.

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

@booklet{EasyChair:15560,
  author    = {Hoo Wang and Che Leo and John Davis and Sarah Smith and Daniel Taylor and Michael Lornwood},
  title     = {A Machine Learning-Driven Approach to XML-Based IP Network Configuration Optimization},
  howpublished = {EasyChair Preprint 15560},
  year      = {EasyChair, 2024}}