A Data-driven, Multi-setpoint Model Predictive Thermal Control System for Data Centers

Abstract

This paper presents a system for jointly managing cooling units and workload assignment in modular data centers. The system aims to minimize power consumption while respecting temperature constraints, all in a thermally heterogeneous environment. Unlike traditional cooling controllers, which may over/under cool certain areas in the data center due to the use of a single setpoint, our framework does not have a single setpoint to satisfy. Instead, using a data-driven thermal model, the proposed system generates an optimal temperature map, the required temperature distribution matrix (RTDM), to be used by the controller, eliminating under/over cooling and improving power efficiency. The RTDM is the resulting temperature distribution when jointly considering workload assignment and cooling control. In addition, we propose the use of model predictive control (MPC) to regulate the operational variables of cooling units in a power-efficient fashion to comply with the RTDM. Within each iteration of the MPC loop, an optimization problem involving the thermal model is solved, and the underlying thermal model is updated. To prove the feasibility of the proposed power efficient system, it has been implemented on an actual modular data center in our facilities. Results from the implementation show the potential for considerable power savings compared to other control methods.