Air free-cooled data centers (DCs) have not existed in the tropical zone due to the unique challenges of year-round high ambient temperature and relative humidity (RH). The increasing availability of servers that can tolerate higher temperatures and RH due to the regulatory bodies’ prompts to raise DC temperature setpoints sheds light upon the feasibility of air free-cooled DCs in the tropics. However, due to the complex psychrometric dynamics, operating the air free-cooled DC in the tropics generally requires adaptive control of supply air condition to maintain the computing performance and reliability of the servers. This article studies the problem of controlling the supply air temperature and RH in a free-cooled tropical DC below certain thresholds. To achieve the goal, we formulate the control problem as Markov decision processes and apply deep reinforcement learning (DRL) to learn the control policy that minimizes the cooling energy while satisfying the requirements on the supply air temperature and RH. We also develop a constrained DRL solution for performance improvements. Extensive evaluation based on real data traces collected from an air free-cooled testbed and comparisons among the unconstrained and constrained DRL approaches as well as two other baseline approaches show the superior performance of our proposed solutions.

中文翻译：

---

热带空气自由冷却数据中心控制的深度强化学习

由于全年高环境温度和相对湿度 (RH) 的独特挑战，热带地区不存在空气自然冷却数据中心 (DC)。由于监管机构提示提高 DC 温度设定点，可以承受更高温度和 RH 的服务器的可用性不断增加，这揭示了在热带地区使用空气自然冷却 DC 的可行性。然而，由于复杂的湿度动力学，在热带地区运行空气自然冷却 DC 通常需要对送风条件进行自适应控制，以保持服务器的计算性能和可靠性。本文研究了将自然冷却热带 DC 中的送风温度和 RH 控制在一定阈值以下的问题。为了实现目标，我们将控制问题表述为马尔可夫决策过程，并应用深度强化学习（DRL）来学习在满足送风温度和相对湿度要求的同时最小化冷却能量的控制策略。我们还开发了一个受约束的 DRL 解决方案以提高性能。基于从空气自由冷却试验台收集的真实数据跟踪的广泛评估以及无约束和约束 DRL 方法以及其他两种基线方法之间的比较显示了我们提出的解决方案的卓越性能。