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Hybrid surrogate model for online temperature and pressure predictions in data centers
Future Generation Computer Systems ( IF 7.5 ) Pub Date : 2020-08-25 , DOI: 10.1016/j.future.2020.08.029
Sahar Asgari , Hosein Moazamigoodarzi , Peiying Jennifer Tsai , Souvik Pal , Rong Zheng , Ghada Badawy , Ishwar K. Puri

The increase in cloud computing and big data storage has led to significant growth in data center (DC) infrastructure that is now estimated to consume more than 1.5% of the world’s electricity. Due to suboptimal DC design and operation, a significant fraction of this energy is wasted because of the cooling systems inability to effectively distribute cold air to servers. Consequently, additional cooling air must be circulated inside a DC to prevent local hot spots, which leads to undercooling at other locations. Row-based cooling is an emerging architecture that provides more effective airflow distribution, which lowers energy consumption. Since available methods are unsuitable for accurate online predictions, a general thermal model is required to predict spatiotemporal temperature changes inside a DC and hence optimize airflow distribution for this architecture. Typical approaches include physical models, computational fluid dynamics (CFD) simulations, and black-box data-driven models (DDMs). All three approaches are limited because they do not encapsulate the entirety of relevant operational parameters, are time-consuming and can provide unacceptable errors during extrapolative predictions. We address these deficiencies by developing a fast, adaptive, and accurate hybrid surrogate model by combining a DDM and the thermofluid transport relations to predict temperatures in a DC. Training data for the DDM is obtained from CFD simulations. An artificial neural network (ANN) with the Rectified Linear Unit (ReLU) activation function is shown to predict pressure distributions accurately in a row-based cooling DC. These predicted pressures are inputs for thermofluid transport equations to determine the temperature distribution. The applicability of the model is demonstrated by comparing predictions with experimental measurements that characterize the influence of varying server workload distribution and cooling unit operational conditions, i.e., temperature set-point, airflow rate, and fan locations, on the temperature distribution. The model can be used to (1) improve cooling configuration design, (2) facilitate thermally aware workload management, and (3) test “what if” scenarios to characterize the influence of operating conditions on the temperature distribution.



中文翻译:

用于数据中心在线温度和压力预测的混合替代模型

云计算和大数据存储的增长已导致数据中心(DC)基础架构的显着增长,据估计,该中心消耗了全球1.5%以上的电力。由于不理想的DC设计和运行,由于冷却系统无法将冷空气有效地分配给服务器,因此浪费了大部分能量。因此,必须在DC内部循环额外的冷却空气,以防止局部热点,从而导致其他位置的过冷。基于行的冷却是一种新兴的体系结构,可提供更有效的气流分配,从而降低能耗。由于可用的方法不适用于准确的在线预测,需要一个通用的热模型来预测DC内部的时空温度变化,从而优化此架构的气流分布。典型方法包括物理模型,计算流体动力学(CFD)模拟和黑匣子数据驱动模型(DDM)。所有这三种方法都受到限制,因为它们没有封装所有相关的操作参数,既费时又会在外推预测过程中提供不可接受的误差。通过结合DDM和热流体传输关系来预测DC中的温度,我们通过开发快速,自适应和准确的混合替代模型来解决这些缺陷。DDM的训练数据是从CFD模拟获得的。图中显示了具有整流线性单元(ReLU)激活功能的人工神经网络(ANN),可精确预测基于行的冷却DC中的压力分布。这些预测压力是热流体传输方程式的输入,以确定温度分布。该模型的适用性通过将预测与实验测量值进行比较来证明,该实验测量值描述了变化的服务器工作负载分布和冷却单元运行条件(即温度设定点,气流速率和风扇位置)对温度分布的影响。该模型可用于(1)改进冷却配置设计,(2)促进热意识的工作负载管理,以及(3)测试“假设情况”场景以表征操作条件对温度分布的影响。

更新日期:2020-08-25
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