Masatoshi Ogawa

Development of a cooling control system for data centers utilizing indirect fresh air based on Model Predictive Control

In this paper, a cooling control system based on Model Predictive Control is developed in order to reduce energy consumption of the facility equipment for the modular data center utilizing indirect fresh air, while considering the relationship between energy savings and temperature management of servers. Furthermore, the developed cooling control system is applied to the actual modular data center utilizing indirect fresh air, and its practical effectiveness is verified by conducting a demonstration experiment. To develop the proposed control system, a dynamic mathematical model for the actual modular data center is designed, and the proposed control system is mounted on an existing management server. The control system is able to operate without using a separate hardware controller. As a result, it is revealed that the proposed control system reduces facilitys total power consumption by about 49% compared to a conventional system under condition where the supply air flow rate is 20°C.

Cooling control restraining effects due to ICT equipment utilization of disturbance based on model predictive control for modular data center

This paper proposes a cooling control method that reduces spikes in CPU temperature that occur due to fluctuation in the utilization of information communication technology (ICT) equipment based on model predictive control (MPC) for a modular datacenter. To cope with the fluctuations, the proposed method not only considers the server power consumption using a prediction model, but also switches between an MPC controller and a necessary-air-volume controller based on the rate at which server power consumption rises. The MPC controller controls the CPU temperature in order to do three things simultaneously: avoid throttling the operation of the CPU, reduce as much as possible the power consumed by data center cooling fans, and adjust for the effects of fluctuations. The necessary-air-volume controller calculates the command value of the revolution speed of the cooling fans to supply the air volume required during maximum CPU utilization. The results of our control simulation show that the proposed control method can drastically reduce spikes in CPU temperature. The proposed method provided energy savings of more than 37.6% compared to the conventional control method under conditions where the CPU utilization is 80% and the fresh air temperature is 20°C.

Cooling control based on model predictive control using temperature information of IT equipment for modular data center utilizing fresh-air

A cooling control method based on a model predictive control (MPC) for a modular datacenter utilizing the fresh-air is proposed. The proposed method reduces the total energy consumption of information technology (IT) equipment and cooling facilities in the data center, while considering a relationship between energy-savings and the temperature information of IT equipment. This method based on MPC controls the central processing unit (CPU) temperature in servers by facility fans for cooling. To design the proposed method, it is developed a prediction model that represents the CPU temperature by the revolution speed of facility fans, the fresh-air temperature, utilization of servers, and other factors. Furthermore, the proposed control method is applied to the actual modular data center. The energy consumption of the proposed method is compared with that of a traditional method, which has controlled the temperature difference between the inlet and outlet of the server racks based on proportional integral (PI) control. Actual comparison experiments with traditional method are provided to validate effectiveness of the proposed method. The results show that the proposed method realizes energy-savings of more than 20% compared to the traditional control method in the actual modular datacenter.

Development of cooling control technology by just-in-time modeling for power reduction in large-scale data center

This study involves the development of a cooling control system utilizing the Just-In-Time (JIT) modeling for the more efficient operation of large-scale data center facilities. In addition, we propose how to control multiple air-conditioning units in order to minimize total power consumption in data center utilizing these predicted data. Data on temperature, humidity and electric power in large-scale data centers were used to generate predicted temperature and humidity for an hour later by JIT modeling. We also estimated the power consumption for air-conditioning units in data center over two months. As a result, it is clear the trend of predicted data is very similar to that of measured one. The accuracy of predicted data utilizing JIT modeling is sufficiently high from the results of calculated correlation coefficient and root mean squared error. The total power consumption for air-conditioning units utilizing proposed control system is found in 6.0% reduction compared with the actually measured data. Since these results were obtained under summer conditions that were the most severe in terms of use of fresh air, further benefits would be expected if a similar study were carried out in other seasons.