AI-Assisted Low-Latency Optical Communication for Distributed Cloud Computing

Abstract

Distributed cloud computing increasingly depends on communication infrastructures that can sustain predictable latency across geographically separated compute, storage, and inference resources. As workloads diversify toward real-time analytics, collaborative artificial intelligence, and latency-sensitive edge services, electrical packet switching alone becomes structurally inadequate for meeting stringent tail-latency requirements. Optical communication offers high capacity, low loss, and favorable energy-per-bit characteristics, but its integration into cloud systems introduces complex trade-offs involving switching granularity, multiplexing, control-plane responsiveness, and deployment economics. This paper examines artificial intelligence assisted low-latency optical communication as a socio-technical infrastructure for distributed cloud computing. It analyzes architectural foundations, including hybrid electrical-optical fabrics, programmable optical transport, and telemetry-rich control planes. It then evaluates latency sources and structural trade-offs across propagation, switching, queuing, and computation. The discussion extends to AI-driven resource allocation, frequency-referenced multiplexing, and guaranteed bandwidth provisioning. Particular attention is given to governance, policy, fairness, robustness, and sustainability because optical cloud infrastructures are not merely engineering artifacts but also public-interest systems that mediate access to computation. The paper argues that AI assistance can improve optical network responsiveness and utilization only when paired with transparent control, resilient fallback mechanisms, equitable capacity allocation, and lifecycle-aware sustainability practices. The conclusion identifies deployment pathways and research priorities for integrating optical low-latency transport into distributed cloud ecosystems without reproducing existing digital inequalities.