Unified Optical Networks and AI Computing Orchestration (UONACO) Problem Statement, Use Cases and Requirements

Distributed artificial intelligence (AI) computing is increasingly deployed across geographically dispersed AI data centers (AIDCs) to meet the scale and performance demands of modern AI workloads. In such environments, the efficiency of distributed training, inference, and remote service access depends critically on tight coordination between optical transport networks and compute orchestration systems. However, today's infrastructure operates with isolated control planes: optical networks lack awareness of dynamic compute requirements, while compute schedulers have no visibility into real- time network conditions such as latency, bandwidth, or congestion. This decoupling leads to suboptimal resource utilization, degraded job performance, and inefficient scaling. This document presents the problem statement, outlines three representative use cases—distributed AI training, distributed AI inference, and remote AI service access—and specifies the requirements for Unified Optical Networks and AI Computing Orchestration (UONACO). The goal is to enable bidirectional awareness, joint resource abstraction, and synchronized control across the compute-optical boundary, thereby supporting intent- driven, end-to-end provisioning of AI services over wide-area optical infrastructures.