Applications and Lessons Learned from AI/ML-Driven SDN Control and Management in Optical Transport Networks

Effective autonomous Software-Defined Networking (SDN) control and management functions are crucial to effectively manage the lifecycle of optical connectivity services, including the creation, restoration, updates, and termination, while continuously meeting stringent requirements such as bandwidth, latency tolerance, and reliability. These autonomous functions aim to optimize network performance objectives, such as efficient resource utilization (e.g., optical spectrum and transceivers) and minimizing power consumption. This work introduces a comprehensive architecture, encompassing building blocks, APIs, and workflows, that fully integrates AI/ML models (such as Deep Reinforcement Learning, DRL) to support automated SDN control decisions, including dynamic routing and resource selection. We then explore how this architecture addresses key challenges in traditional optical transport networks, such as latency-aware service provisioning, online restoration of connectivity services, energy-efficient resource management, and joint cloud and network resource allocation. Through the exploration of various applications, we present selected results demonstrating how AI/ML-driven solutions can enhance network operations in these areas. Additionally, we share key lessons learned from these applications, providing insights into the benefits and limitations of adopting AI/ML models for autonomous SDN operations in optical transport networks. These lessons focus on practical considerations, challenges in adapting DRL solutions to specific networking problems, and recommendations for optimizing their integration into real-world optical network infrastructures.