发表机构
Computer Science Department Texas State University（德克萨斯州立大学计算机系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对AI数据中心碳排放问题，提出碳感知任务模拟器CATS，通过空间和时间调度在现实约束下分别减少38.4%和16%的CO2排放，主张低碳选址。
AI 中文摘要
生成式人工智能的迅速崛起正在加速云数据中心的扩张，预计到2026年电力需求将翻一番。由于碳强度在不同电网和一天中的不同时间差异超过5.5倍，推理任务执行的地点和时间显著影响运营排放。本文从三个方面解决这一问题。首先，我们编制了一个全球对齐数据集，统一了8家主要提供商的140个运营和规划中的云区域，以及2022年至2024年间145个电网区域的五分钟碳强度轨迹，揭示当前50%的站点位于中高碳强度电网中，表明存在选址与碳强度不匹配的问题以及未实现的碳减排潜力。其次，我们开发了CATS（碳感知任务模拟器），这是一个灵活的基于轨迹的框架，可对多种GPU类型上的六种AI推理任务进行性能分析，合成现实的日变化曲线、地理和任务组合以及SLA约束，并针对两个基线评估空间和时间调度器，同时报告包括碳排放、能耗、运行时间、队列延迟和硬件利用率在内的综合指标。第三，我们量化了在现实约束下可实现的CO2节省：在包含60万个任务、车队利用率为0.37的24小时轨迹中，空间转移相比速度优先基线减少了38.4%的CO2，而时间转移在SLA违规率为3.27%的受限条件下实现了16%的节省。这些结果主张将未来数据中心选址在低碳强度电网中，并证明在当今车队上实施碳感知调度可以实现显著的运营排放减少。
英文摘要
The rapid rise of generative AI is accelerating cloud data center expansion, with electricity demand projected to double by 2026. Because carbon-intensity varies by more than 5.5x across grids and times of day, where and when inference tasks execute significantly affects operational emissions. We address this issue with three aspects in this paper. First, we compile a global alignment dataset unifying 140 operational and planned cloud regions across 8 major providers with five-minute carbon-intensity traces for 145 grid regions from 2022 to 2024, revealing that 50% of current sites lie in medium-to-high carbon-intensity grids, indicating a siting-carbon mismatch and unrealized carbon reduction potential. Second, we develop CATS (Carbon-Aware Task Simulator), a flexible trace-driven framework that profiles six AI inference tasks across multiple GPU types, synthesizes realistic diurnal curve, geographical and task mixes, and SLA constraints, and evaluates spatial and temporal schedulers against two baselines while reporting comprehensive metrics including carbon emissions, energy consumption, runtime, queue delay, and hardware utilization. Third, we quantify achievable CO2 savings under realistic constraints: in a 24-hour trace with 600,000 tasks at fleet utilization of 0.37, spatial shifting reduces CO2 by 38.4% versus speed-first baseline, while temporal shifting yields 16% savings with bounded SLA violations at 3.27%. These results advocate locating future data centers in low carbon-intensity grids and demonstrate that carbon-aware scheduling on today's fleets can achieve substantial operational emissions reduction.
Comments14 pages, 12 figures, 8 tables
