Application of Machine Learning to Optical Network Traffic-Driven Provisioning Services

Intelligent and adaptable provisioning techniques are required due to the growing demand for dynamic and high capacity services in optical networks. In order to solve issues like fault management, resource allocation, and traffic prediction, this research explores the use of machine learning (ML) approaches for traffic-driven service provisioning in optical networks. In order to assess traffic patterns and improve service deployment, the suggested framework makes use of both supervised and unsupervised learning models, such as Random Forests, K-Means clustering, and Long Short-Term Memory (LSTM) networks. The system uses historical and real-time traffic data to forecast demand, allowing for dynamic wavelength assignment and proactive resource allocation. Additionally, decision-making for adaptive routing and spectrum management under various network situations is automated via the use of reinforcement learning. Evaluation of performance on both simulated and real-world datasets shows that ML-based methods greatly increase network efficiency, lower latency, and improve QoS. This study demonstrates how machine learning has the ability to completely transform optical network service provisioning by providing scalable, data-driven solutions that can satisfy the expanding needs of next-generation communication systems.