Digital Twin-Based Optimization of Data Center Optical Interconnection Networks

Abstract

Data center optical interconnection networks are becoming central to the performance, resilience, and energy profile of large-scale digital infrastructure. As workloads diversify across artificial intelligence training, latency-sensitive inference, storage replication, and multi-tenant cloud services, static provisioning and coarse-grained traffic engineering increasingly fail to capture the dynamic coupling between physical optics, packet-level scheduling, workload placement, and facility constraints. This paper examines digital twin-based optimization of data center optical interconnection networks from a system-level perspective. It argues that digital twins can improve optical network operation not merely by simulating components, but by creating a continuously synchronized decision layer that links telemetry, physical models, workload forecasts, and policy constraints. The analysis considers architectural trade-offs among electrical switching, optical circuit switching, wavelength switching, and hybrid fabrics, with attention to observability, control latency, fault isolation, and deployment cost. It then discusses optimization loops that span physical-layer impairment estimation, topology reconfiguration, workload-aware routing, and sustainability-aware scheduling. The paper also explores governance, robustness, fairness, and policy implications, including data sovereignty, accountability for automated decisions, and equitable access to shared infrastructure. Case illustrations from hyperscale, colocation, and edge contexts show that digital twin value depends less on model fidelity alone than on institutional arrangements that determine what may be sensed, changed, and trusted. The conclusion identifies research directions for verifiable twins, multi-domain federation, and human oversight in critical optical infrastructure.