AI-Driven Resource Allocation for Low-Latency Optical Network Communications
Keywords:
optical networks, resource allocation, low latency, artificial intelligence, reinforcement learning, software-defined networking, sustainability, network governanceAbstract
Low-latency optical network communication has become a foundational requirement for cloud platforms, distributed artificial intelligence, financial trading, industrial automation, and scientific computing. As traffic volumes grow and service-level expectations tighten, static or heuristic resource allocation methods increasingly struggle to reconcile competing demands for bandwidth, determinism, energy efficiency, resilience, and fairness. This paper examines artificial intelligence driven resource allocation for low-latency optical networks from a system-level perspective. It argues that the central challenge is not merely prediction accuracy or algorithmic sophistication, but the structural alignment of learning architectures, optical control planes, hardware constraints, governance models, and deployment incentives. The discussion begins with architectural foundations, including software-defined optical networking, elastic spectrum assignment, and hybrid electrical-optical switching. It then analyzes latency and bandwidth trade-offs, especially the tension between deterministic guarantees and statistical multiplexing gains. Subsequent sections explore machine learning for traffic forecasting, routing, spectrum assignment, and failure response, with attention to reinforcement learning and sequence models. The paper also evaluates governance, policy, sustainability, robustness, fairness, multi-tenancy, and service-level assurance. Case illustrations from data centers, wide-area networks, and cross-domain infrastructures clarify how AI-driven allocation can either reinforce or undermine equitable access. The conclusion emphasizes that successful deployments will depend on explainable control, bounded autonomy, physical-layer awareness, and institutional accountability, not solely on model performance. The paper contributes a conceptual framework for integrating AI into low-latency optical resource allocation as a socio-technical infrastructure problem.
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