Sustainability-Constrained Workload Orchestration for Sovereign AI Infrastructure: A Joint Compute-Network Optimization Framework
For operators of sovereign AI infrastructure, this work provides a framework to ensure operations remain within physical and environmental limits, but the results are scenario-based without concrete numbers, making it incremental.
This paper proposes a sustainability-constrained orchestration framework for AI infrastructure that treats carbon intensity, water usage, and power capacity as strict constraints, jointly optimizing compute placement and optical network routing. Scenario-based analysis shows joint optimization reduces environmental impact compared to baselines.
AI infrastructure has transitioned from a software-centric paradigm to a system tightly bound by physical and environmental limits. Energy availability, cooling capacity, and network connectivity now impose hard operational boundaries that cannot be relaxed through software optimization alone. This paper proposes a sustainability-constrained orchestration framework that treats carbon intensity, water usage, and power capacity as strict feasibility constraints rather than tunable penalties, and that jointly optimizes compute placement and optical network routing in a single closed-loop system. We introduce the Feasible Sovereign Operating Region (FSOR) - a conceptual and operational construct that characterizes the set of workloads a given infrastructure can actually sustain under its physical and regulatory endowment. Scenario-based analysis demonstrates that joint optimization yields lower environmental impact relative to baseline formulations. Infeasibility events, rather than being optimizer failures, constitute precise, telemetry-grounded signals that sovereign AI operation requires infrastructure investment or workload reduction.