Data center is a fundamental infrastructure of computers and networking equipment to collect, store, process, and distribute huge amounts of data for a variety of applications such as Cyber–Physical–Social Systems, business enterprises and social networking. As the demands of remote data services keep increasing, both the workload of the data center and its power consumption are rapidly rising. An indispensable part of a data center is the cooling system which provides a suitable operation environment, and accounts for around 30% of the power consumption of the data center. Therefore, optimized energy management of data center’s cooling system is a highly profitable research area. Generally, a cooling system is made up of a mechanical refrigeration sub-system and a terminal cooling sub-system. Heat generated during operation of the data center will be absorbed by the latter one, and transferred into the outdoor environment via the former one. Depending on the cooling principle, current cooling solutions can be classified into air-cooling, liquid-cooling or free cooling technology. Although air-cooling is widely used in most existing data centers, the other two solutions have attracted more interests due to their excellent cooling effectiveness and higher energy efficiencies. Among the different cooling equipment, the chillers and fans are the major power consumers of the entire cooling system. Therefore, modeling of their power consumption is important for energy management of the cooling system, which can be classified into mechanism-based methods and data-driven methods. Based on the aforementioned models, optimization strategies for the operation management of cooling equipment are proposed to reduce the power consumption of the cooling system, which mainly includes the model predictive control-based methods and reinforcement learning-based methods. This paper is an overview of the data center’s cooling system, which mainly includes the mainstream cooling solutions, the power consumption modeling methods and the optimization control strategies. In addition, several current challenges and future work in the data center’s cooling system are described.

数据中心是计算机和网络设备的基本基础设施，用于收集、存储、处理和分发大量数据，用于各种应用程序，例如网络-物理-社交系统、商业企业和社交网络。随着远程数据服务需求的不断增加，数据中心的工作负载和功耗都在快速上升。冷却系统是数据中心不可或缺的一部分，它提供合适的运行环境，约占数据中心功耗的30%。因此，优化数据中心冷却系统的能源管理是一个高利润的研究领域。通常，制冷系统由机械制冷子系统和终端制冷子系统组成。数据中心在运行过程中产生的热量会被后者吸收，并通过前者传递到室外环境中。根据冷却原理，目前的冷却解决方案可分为风冷、液冷或自然冷却技术。尽管风冷在大多数现有数据中心中得到广泛应用，但其他两种解决方案因其出色的冷却效果和更高的能效而引起了更多人的兴趣。在不同的冷却设备中，冷水机和风扇是整个冷却系统的主要用电设备。因此，它们的功耗建模对于冷却系统的能量管理很重要，可以分为基于机制的方法和数据驱动的方法。基于上述模型，提出了冷却设备运行管理优化策略，以降低冷却系统的功耗，主要包括基于模型预测控制的方法和基于强化学习的方法。本文是对数据中心冷却系统的概述，主要包括主流的冷却解决方案、功耗建模方法和优化控制策略。此外，还描述了数据中心冷却系统目前面临的几个挑战和未来的工作。主要包括主流散热方案、功耗建模方法和优化控制策略。此外，还描述了数据中心冷却系统目前面临的几个挑战和未来的工作。主要包括主流的散热方案、功耗建模方法和优化控制策略。此外，还描述了数据中心冷却系统目前面临的几个挑战和未来的工作。