Towards reinforcement learning approach to energy-efficient control of server fans in data centres

Modern data centres require control, which aims to improve their energy efficiency and maintain their high availability. This work considers the implementation of a server fan agent, which is intended to minimise the power consumption of the corresponding server fan or group of fans. In the paper, the reinforcement learning approach to energy-efficient control of server fans is suggested. The reinforcement learning workflow is considered. The Simulink blocks simplifying the building of the environment for the reinforcement learning agent are developed. This work provides the framework for creating and training reinforcement learning agents of different types. As the paper is only a work-in-progress, possible type of agents and their training process is described, but training and deploying the agent is a work for the future.