Impacts of Increasing Temperature and Relative Humidity in Air-Cooled Tropical Data Centers

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Duc Van Le;Jing Zhou;Rongrong Wang;Rui Tan;Fei Duan
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引用次数: 0

Abstract

Data centers (DCs) are power-intensive facilities which use a significant amount of energy for cooling the servers. Increasing the temperature and relative humidity (RH) setpoints is a rule-of-thumb approach to reducing the DC energy usage. However, the high temperature and RH may undermine the server's reliability. Before we can choose the proper temperature and RH settings, it is essential to understand how the temperature and RH setpoints affect the DC power usage and server's reliability. To this end, we constructed and experimented with an air-cooled DC testbed in Singapore, which consists of a direct expansion cooling system and 521 servers running real-world application workloads. This paper presents the key measurement results and observations from our 11-month experiments. Our results suggest that by operating at a supply air temperature setpoints of 29 $^{\circ }$ C, our testbed achieves substantial cooling power saving with little impact on the server's reliability. Furthermore, we present a total cost of ownership (TCO) analysis framework which guides settings of the temperature and RH for a DC. Our observations and TCO analysis framework will be useful to future efforts in building and operating air-cooled DCs in tropics and beyond.
气冷式热带数据中心温度和相对湿度上升的影响
数据中心(DC)是电力密集型设施,需要消耗大量能源来冷却服务器。提高温度和相对湿度(RH)设定值是减少 DC 能源消耗的一个常用方法。然而,高温和相对湿度可能会降低服务器的可靠性。在选择合适的温度和相对湿度设置之前,我们必须了解温度和相对湿度设置点如何影响直流电能使用和服务器的可靠性。为此,我们在新加坡建造了一个风冷直流试验台并进行了实验,该试验台由直接膨胀冷却系统和运行实际应用工作负载的 521 台服务器组成。本文介绍了为期 11 个月实验的主要测量结果和观察结果。我们的结果表明,通过在 29$^{\circ }$C 的供气温度设定值下运行,我们的测试平台实现了大量的制冷节能,而对服务器的可靠性影响很小。此外,我们还提出了一个总拥有成本(TCO)分析框架,用于指导直流电的温度和相对湿度设置。我们的观察结果和总拥有成本分析框架将有助于今后在热带地区及其他地区建造和运行风冷直流电。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Sustainable Computing
IEEE Transactions on Sustainable Computing Mathematics-Control and Optimization
CiteScore
7.70
自引率
2.60%
发文量
54
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