🤖 AI Summary
This work addresses the physical constraints—such as energy availability, cooling capacity, and network bandwidth—that challenge the sustainable operation of AI infrastructure, which traditional software-level optimizations alone cannot resolve. The authors propose a joint compute-network optimization framework that explicitly incorporates carbon intensity, water usage, and power capacity as hard constraints within a closed-loop system to co-schedule computing and optical networking resources. A key innovation is the introduction of the “Feasible Sovereign Operating Region” (FSOR), which transforms infeasible solutions into precise decision signals for infrastructure expansion or load curtailment. By integrating task scheduling with optical circuit routing through a scenario-driven approach and embedding multidimensional sustainability constraints, the framework significantly reduces environmental impact, demonstrating its effectiveness in enhancing the sustainability of AI infrastructure under real-world physical limitations.
📝 Abstract
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.