Smart DC: An AI and Digital Twin-based Energy-Saving Solution for Data Centers

• Main Authors: Zhang, Ziting, Zeng, Yu, Liu, Haoran, Zhao, Chaoyue, Wang, Feng, Chen, Yunqing
• Format: Conference Proceeding
• Language: English
• Subjects: data center (DC); Data centers; Data models; digital twin; Digital twins; energy conservation; Energy consumption; Redundancy; Temperature distribution; Training
• ISSN: 2374-9709
• DOI: 10.1109/NOMS54207.2022.9789853

With the rapid growth of mobile internet, Internet of Things (IoT), and cloud computing, the demand for data services has arisen sharply. As the core data service infrastructure, the number of data centers (DCs) has surged and led to higher energy consumption, which is not conducive to energy conservation, emission reduction, and sustainable development. In this paper, we proposed an energy-saving solution based on Artificial Intelligence (AI) and digital twin in DC scenarios, called Smart DC. The proposed solution can reduce DCs' energy consumption by optimizing air distribution and reducing cooling redundancy. Specifically, the digital twin model in this solution is used to verify and optimize AI strategies, and solve the problem of insufficient data in physical data center. Data for AI training and information mining is limited because the environment in the DCs change little. Moreover, in order to ensure that the DCs operate at a safe temperature, the adjustment of parameters should be conservative, so there is still room for cooling redundancy. We combined digital twin and AI, exploring the temperature rise boundary in the digital DCs and mine more data pairs, which has proven to increase the robustness of the AI model and achieve better energy-saving effect. The simulation and experiment results show that the proposed solution can ensure safe and efficient operation and keep the energy-saving rate of the cooling system to reach 41.07%.