平衡比特与水滴：数据中心的压力调整型水管理
Balancing Bits and Drops: Stress-Adjusted Water Management for Data Centers
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中文总结 AI 辅助
研究数据中心水管理问题，引入压力调整型水框架，结合时空水压力量化用水影响，利用AWARE-US模型扩展框架，分析软件和基础设施层的水计算策略，如优化工作负载调度、评估雨水收集潜力及研究干冷可行性。
中文摘要 AI 辅助
数据中心对当今数字经济至关重要，但也是最大的淡水工业消费者之一。除了用水量巨大，其用水的环境影响因地点和季节而异，取决于当地和区域的水压力。以往研究多关注减少总用水量，忽视了相同单位的水因消耗时间和地点不同，环境后果差异巨大。本文引入压力调整型水框架，通过纳入时空水压力来量化数据中心用水的真实可持续性影响。利用AWARE-US模型捕捉县级月度水资源可用性变化，并扩展该框架以考虑发电的场外水足迹。基于这种压力感知核算，分析跨越软件和基础设施层的压力调整型水计算策略。具体研究联合优化水和碳效率的工作负载调度策略，评估雨水收集作为补充水源的潜力，以及研究干冷作为蒸发冷却的无水替代方案的可行性。
英文摘要
Data centers are critical to today's digital economy, but are also among the largest industrial consumers of freshwater. Beyond the sheer volume of water use, the environmental impact of data center water consumption varies significantly across locations and seasons, depending on local and regional water stress. However, prior research has largely focused on reducing total water use, overlooking that the same unit of water can have drastically different environmental consequences depending on when and where it is consumed. In this paper, we introduce a stress-adjusted water framework that quantifies the true sustainability impact of data center water consumption by incorporating both spatial and temporal water stress. Using the AWARE-US model, we capture county-level monthly variations in water availability and extend this framework to account for the off-site water footprint of electricity generation. Based on this stress-aware accounting, we analyze stress-adjusted water-computing strategies spanning both the software and infrastructure layers. Specifically, we study workload scheduling policies that jointly optimize water and carbon efficiency, evaluate the potential of rainwater harvesting as a supplemental water source, and investigate the feasibility of dry cooling as a water-free alternative to evaporative cooling.
