Data-driven fault tolerant predictive control for temperature regulation in data center with rack-based cooling architecture

Recently increasing amount of data centers (DCs) have been constructed for the development of information and communications technology (ICT). Meanwhile, more energy consumption is required to support the operation of DCs, where nearly thirty percent of the consumption is used for cooling system. Therefore, this paper studies an efficient cooling architecture named rack mountable cooling unit (RMCU) for DCs, and provides a novel data-driven fault tolerant predictive controller to regulate the temperature accordingly. In order to circumvent the complicated physics modeling in DC, a data-driven modeling method is developed. In this method, multiple local linear models in form of auto-regressive exogenous (ARX) model are selected to describe the strongly nonlinear system. Besides, the multiple sub-models are identified through partial least square (PLS) and corresponding training data is partitioned by fuzzy c-means (FCM). Then, on the basis of developed data-driven model, a predictive controller considering actuator faults is designed to manage the temperature in DC. Finally, real experimental results are presented and demonstrate the superior performance of our proposed controller.

Automated summary

This paper investigates an efficient cooling architecture named RMCU and a novel data-driven fault tolerant predictive controller to regulate the temperature in data centers. A data-driven modeling method is developed using ARX models to describe the system and PLS and FCM to identify sub-models and partition training data. A predictive controller considering actuator faults is designed, demonstrating superior performance in managing temperature in data centers through real experimental results.