Adaptive sensor data fusion for efficient climate control systems

Abstract

Thousands of data centres are using traditional airconditioned cooling concepts for the entire payload. Most of these data centres include multiple hardware generations and different types of ITinfrastructure components, i.e. storage, compute, and network devices. In the context of Green-IT, an efficient and safe parameterization of the air-conditioning-system is essential - keep the temperature as low as necessary, but not too low. Usually, only a few amount of temperature sensors are available to handle these important control cycles. But in order to optimise the cooling capacity, several scenario-specific parameters have to be considered, including the shape of the room, air flow, or component placements. In this context, the TU Chemnitz develops novel concepts to improve this process. We are using local sensor capabilities within the hardware components and combine these information with actual system loads to create an extended knowledge base, which also provides adaptive learning features. First measurement scenarios show huge optimisation potential. The respective trade-off between power consumption and cooling capacity results in significant cost savings.