发表机构
Instituto de Física de Cantabria (IFCA), CSIC-UC; Aix Marseille Univ, CNRS/IN2P3, CPPM（坎塔布里亚物理研究所 (IFCA)，西班牙国家研究委员会-坎塔布里亚大学; 艾克斯马赛大学，法国国家科学研究中心/法国国家粒子物理研究所，普罗旺斯粒子与核物理中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对数据中心仅考虑碳足迹的不足，提出环境评分和绿色评分指标，并集成到GreenDirector调度器中，在真实系统中实现水碳联合优化，减少约40%碳排放并明确权衡。
AI 中文摘要
人工智能加速了数据中心电力需求的快速增长，这使得仅以碳作为唯一核算指标无法全面衡量计算对环境的影响：低碳电力组合往往耗水量大，且由此产生的损害取决于当地季节性的水资源稀缺程度，而非消耗的水量。我们提出了环境评分（ES），这是一个统一的、无量纲的指标，取值范围为$[0,100]$，它同时捕捉了工作负载所消耗电力的碳足迹以及时空加权的、基于稀缺性的水影响。该指标将实时的跨境电力流追踪与月度AWARE2.0水资源稀缺表征因子相结合，将全球温室气体排放与当地季节性水资源压力的严重程度进行加权。在此基础上，我们定义了绿色评分（GS），这是一个调度指标，与单位实际环境影响所产生的有用计算工作量成正比，同时考虑了数据中心电力和硬件效率。我们将这两个指标作为绿色亲和特性添加到两个生产级联邦基础设施的GreenDirector调度器中，即AI4EOSC科学云和DIRAC工作负载管理系统。在AI4EOSC中，一项覆盖四个泛欧提供商的集群填充实验表明，更绿色的站点会优先被填充，且不会降低调度延迟或最终用户体验。在DIRAC中，针对133,631个作业的基于轨迹的模拟以及为KM3NeT社区进行的初步生产部署，将碳排放减少了约40%并提高了碳效率，同时在最低碳站点也面临更高水压力的情况下，明确展示了碳-水权衡。结果表明，在实时多租户系统中，水文压力可以动态地加权到工作负载调度中。
英文摘要
The rapid growth of data center electricity demand, accelerated by AI, makes carbon-only accounting an incomplete measure of computing's environmental impact: low-carbon electricity mixes are often water-intensive, and the resulting harm depends on local, seasonal scarcity rather than on the volume of water consumed. We propose the Environmental Score (ES), a unified, dimensionless index in $[0, 100]$ that jointly captures the carbon footprint and the spatial-temporal, scarcity-weighted water impact of the electricity a workload consumes. It combines real-time, cross-border electricity flow tracing with monthly AWARE2.0 water-scarcity characterization factors, weighting global greenhouse-gas emissions together with the local, seasonal severity of water stress. Building on it, we define the Green Score (GS), a scheduling metric proportional to the useful computational work delivered per unit of real environmental impact, which also accounts for data center power and hardware efficiency. We add both metrics as a green-affinity feature to the GreenDirector schedulers of two production federated infrastructures, the AI4EOSC scientific cloud and the DIRAC workload management system. In AI4EOSC, a cluster-filling experiment over four pan-European providers shows that greener sites are filled first without degrading scheduling latency or end-user experience. In DIRAC, a trace-driven simulation of 133,631 jobs and a preliminary production deployment for the KM3NeT community reduce carbon emissions and improve carbon efficiency by about 40\%, while making the carbon-water trade-off explicit when the lowest-carbon site also carries higher water stress. The results show that hydrological stress can be dynamically weighted into workload placement in live, multi-tenant systems
