标题： Green-LLM：环境感知分布式推理的最优工作负载分配 显示英文标题

标题： Green-LLM: Optimal Workload Allocation for Environmentally-Aware Distributed Inference

摘要： 这封信件研究了在时间上跨异构边缘数据中心（DCs）的大语言模型（LLM）推理工作负载的最优分配。 每个DC都有现场可再生能源发电，并面临动态电价以及可再生能源可用性的时空变化。 核心问题是：如何将推理工作负载最优地分配给DCs，以最小化能源消耗、碳排放和用水量，同时提升用户体验？ 这封信件提出了一种新的优化模型，供LLM服务提供商使用以降低运营成本和环境影响。 数值结果验证了所提方法的有效性。 显示英文摘要

摘要： This letter investigates the optimal allocation of large language model (LLM) inference workloads across heterogeneous edge data centers (DCs) over time. Each DC features on-site renewable generation and faces dynamic electricity prices and spatiotemporal variability in renewable availability. The central question is: how can inference workloads be optimally distributed to the DCs to minimize energy consumption, carbon emissions, and water usage while enhancing user experience? This letter proposes a novel optimization model for LLM service providers to reduce operational costs and environmental impacts. Numerical results validate the efficacy of the proposed approach.