一句话：提出温水冷却系统 CoolEdge，通过动态水温和流速及冷板重设计缓解边缘数据中心多层级热点，大幅降低冷却能耗。
边缘数据中心因贴近用户、高密度和异构性，传统统一冷却控制会因服务器、硬件乃至单个组件内部严重热失衡而效率低下。本文提出 CoolEdge 温水冷却系统：通过精细设计水循环，按异构硬件组件动态调整水温与流速以消除硬件级热点；通过重设计冷板，无需人工干预快速分散芯片级热点；并从理论上量化温水冷却的节能潜力，周期性地确定合适水温与流速。基于硬件原型和 SURFsara 真实负载的评测表明，与常规和最先进水冷系统相比，CoolEdge 分别降低冷却能耗 81.81% 和 71.92%。
As the computing frontier drifts to the edge, edge datacenters play a crucial role in supporting various real-time applications. Different from cloud datacenters, the requirements of proximity to end-users, high density, and heterogeneity, present new challenges to cool the edge datacenters efficiently. Although warm water cooling has become a promising cooling technique for this infrastructure, the one-size-fits-all cooling control would lower the cooling efficiency considerably because of the severe thermal imbalance across servers, hardware, and even inside one hardware component in an edge datacenter. In this work, we propose CoolEdge, a hotspot-relievable warm water cooling system for improving the cooling efficiency and saving costs of edge datacenters. Specifically, through the elaborate design of water circulations, CoolEdge can dynamically adjust the water temperature and flow rate for each heterogeneous hardware component to eliminate the hardware-level hotspots. By redesigning cold plates, CoolEdge can quickly disperse the chip-level hotspots without manual intervention. We further quantify the power saving achieved by the warm water cooling theoretically, and propose a custom-designed cooling solution to decide an appropriate water temperature and flow rate periodically. Based on a hardware prototype and real-world traces from SURFsara, the evaluation results show that CoolEdge reduces the cooling energy by 81.81% and 71.92%, respectively, compared with conventional and state-of-the-art water cooling systems.
@inproceedings{PeiCZZLJWY22,
title = {CoolEdge: hotspot-relievable warm water cooling for energy-efficient edge datacenters},
author = {Qiangyu Pei and Shutong Chen and Qixia Zhang and Xinhui Zhu and Fangming Liu and Ziyang Jia and Yishuo Wang and Yongjie Yuan},
booktitle = {ASPLOS},
year = {2022},
doi = {10.1145/3503222.3507713}
}