面向主权AI基础设施的可持续性约束工作负载编排：一种联合计算-网络优化框架
Sustainability-Constrained Workload Orchestration for Sovereign AI Infrastructure: A Joint Compute-Network Optimization Framework
AI总结：
本文提出一种联合计算与光网络路由优化框架，通过将碳强度、用水量和电力容量作为严格约束条件，提升主权AI基础设施的可持续性与环境影响控制。
摘要：
AI基础设施已从以软件为中心的范式转向受物理和环境限制紧密绑定的系统。能源可用性、冷却能力及网络连通性现在对运营施加了硬性边界，无法仅通过软件优化来缓解。本文提出了一种可持续性约束编排框架，将碳强度、用水量和电力容量视为严格可行性约束，而非可调惩罚项，并在单一闭环系统中联合优化计算放置和光网络路由。我们引入了可行主权操作区域（FSOR）——一个概念和操作构造，用于表征给定基础设施在其物理和监管资源下所能实际支持的工作负载集合。基于场景分析表明，联合优化相比基线方法能降低环境影响。不可行事件，而非优化器失败，构成精确的 telemetry 基础信号，主权AI操作需要基础设施投资或工作负载减少。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
