发表机构
Villanova University; University of Arkansas at Little Rock（维拉诺瓦大学; 阿肯色大学小石城分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种通过批量工作负载时间平移评估AI数据中心电网需求灵活性的框架，利用平均化资源处理和调度模型，实现短期峰值削减与持续降载，实验证明其有效且干扰小。
AI 中文摘要
人工智能（AI）数据中心的快速增长给电力系统运行带来了新的挑战。随着其电力需求变得更大且更具波动性，定量表征其需求灵活性对于有效的电力系统协调日益重要。然而，异构的工作负载特征和资源需求使得这种灵活性难以直接表征。本文提出了一个通过批量工作负载时间平移来评估AI数据中心电网兼容需求灵活性的框架。开发了一种基于平均的资源使用处理方法，将细粒度的CPU、GPU和内存使用映射到与电力系统运行兼容的统一时间间隔。随后构建了一个工作负载时间调度模型，在保持执行连续性、延迟约束和服务器资源容量的同时平移批量工作负载，并与一个依赖于利用率的服务器功率模型耦合，将工作负载调度决策转化为服务器功率需求。评估了两个互补的灵活性指标：短期峰值需求削减和持续功率降低的最大持续时间。基于真实GPU集群轨迹的数值结果表明，工作负载时间平移可以为AI数据中心提供可量化且电网兼容的需求灵活性，同时对计算工作负载的干扰有限。
英文摘要
The rapid growth of artificial intelligence (AI) data centers has introduced new challenges to power system operation. As their power demand becomes larger and more variable, quantitatively characterizing their demand flexibility is increasingly important for effective power system coordination. However, heterogeneous workload characteristics and resource requirements make this flexibility difficult to characterize directly. This paper proposes a framework for assessing the grid-compatible demand flexibility of AI data centers via batch workload temporal shifting. An averaging-based resource usage processing method is developed to map fine-resolution CPU, GPU and memory usage into unified time intervals compatible with power system operation. A workload temporal scheduling model is then formulated to shift batch workloads while preserving execution continuity, delay constraints, and server resource capacities, and is coupled with a utilization-dependent server power model to translate workload scheduling decisions into server power demand. Two complementary flexibility metrics are evaluated: short-term peak demand shaving and the maximum duration of sustained power reduction. Numerical results based on real GPU cluster traces demonstrate that workload temporal shifting can provide quantifiable and grid-compatible demand flexibility for AI data centers with limited disruption to computing workloads.
Comments5 pages, 5 figures, conference
