Optical Network Resource Management for Large-Scale AI Training Clusters

Abstract

Large-scale artificial intelligence training clusters increasingly depend on optical networking to sustain the bandwidth, latency, and synchronization requirements of distributed model training. This paper examines optical network resource management as a socio-technical problem that spans physical-layer transmission, switching architectures, scheduler design, operational governance, energy sustainability, robustness, and fairness. The analysis begins by characterizing training workloads, including collective communication, parameter synchronization, pipeline and tensor parallelism, and the sensitivity of convergence to tail latency. It then evaluates optical resource management architectures, including hybrid electrical-optical fabrics, optical circuit switching, wavelength switching, and software-defined control planes, with attention to structural trade-offs among reconfiguration latency, port count, buffering, circuit utilization, and failure isolation. The paper argues that resource management for AI clusters cannot be reduced to bandwidth provisioning because optical reconfiguration interacts with job placement, checkpointing, collective algorithms, and failure recovery. Governance mechanisms must therefore coordinate admission control, topology-aware scheduling, tenant fairness, observability, and environmental accounting. The discussion also considers deployment constraints in multi-tenant and geo-distributed environments, the risks of overfitting infrastructure to a narrow class of model architectures, and the policy implications of concentrating advanced optical capacity in a small number of institutions. The conclusion proposes a research agenda around adaptive control, disaggregated optical fabrics, energy-aware scheduling, robust circuit restoration, and transparent reporting of infrastructure performance and sustainability.