Machine Learning for Traffic-Driven Services Provisioning in Optical Networks

The increasing demand for high-capacity and dynamic services in optical networks necessitates intelligent and adaptive provisioning mechanisms. This study investigates the application of machine learning (ML) techniques for traffic-driven service provisioning in optical networks, addressing challenges such as traffic prediction, resource allocation, and fault management. The proposed framework employs supervised and unsupervised learning models, including Long Short-Term Memory (LSTM) networks, Random Forests, and K-Means clustering, to analyze traffic patterns and optimize service deployment. The system leverages real-time and historical traffic data to predict demand, enabling proactive resource allocation and dynamic wavelength assignment. Furthermore, reinforcement learning is explored to automate decision- making for adaptive routing and spectrum management under varying network conditions. Performance evaluation on simulated and real-world datasets demonstrates that ML-based approaches significantly improve network efficiency, reduce latency, and enhance the quality of service (QoS). This research highlights the potential of machine learning in revolutionizing service provisioning in optical networks, offering scalable, data-driven solutions to meet the growing demands of next-generation communication systems.