Machine Learning-Enabled Performance Optimization of Wavelength-Switched Networks

Abstract

Wavelength-switched optical networks have long offered deterministic capacity, low latency, and high spectral efficiency, but their static provisioning models struggle with the dynamic traffic patterns of cloud, data center, and edge infrastructures. Machine learning introduces a promising control and optimization layer that can infer traffic behavior, predict quality of transmission, allocate wavelengths, and reconfigure optical paths with limited human intervention. This paper examines machine learning enabled performance optimization of wavelength-switched networks from a system-level perspective. It argues that optimization is not merely a computational problem but a socio-technical challenge involving architecture, operations, governance, sustainability, robustness, and fairness. The discussion first reviews the architectural foundations of wavelength switching, including reconfigurable optical add-drop multiplexers, elastic spectrum assignment, and intra-data center interconnects. It then analyzes how supervised learning, reinforcement learning, and probabilistic inference can improve routing, spectrum allocation, failure recovery, and energy management. Particular attention is given to structural trade-offs among latency, blocking probability, spectral efficiency, capital expenditure, and operational complexity. The paper also considers deployment constraints, telemetry requirements, model lifecycle management, and cross-domain lessons from software-defined networking and AI safety. It concludes that machine learning can enhance wavelength-switched network performance only when embedded within accountable governance frameworks, robust monitoring systems, and sustainability-aware planning. The central contribution is a critical synthesis that connects algorithmic optimization to infrastructure policy, fairness, and long-term resilience.