  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 tropics. However, due to the complex psychrometric dynamics, operating the
air free-cooled DC in tropics generally requires adaptive control of supply air
condition to maintain the computing performance and reliability of the servers.
This paper 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.
