부산대학교 도서관

The development of 5G, cloud computing, artificial intelligence (AI) and other new generation information technologies has promoted the rapid development of the data center (DC) industry, which directly increase severe energy consumption and carbon emissions problem. In addition to traditional engineering based methods, AI based technology has been widely used in existing data centers. However, the existing AI model training schemes are time-consuming and laborious. To tackle this issues, we propose an automated training and deployment platform for AI modes based on cloud-edge architecture, including the processes of data processing, data annotation, model training optimization, and model publishing. The proposed system can generate specific models based on the room environment and realize standardization and automation of model training, which is helpful for large-scale data center scenarios. The simulation and experimental results show that the proposed solution can reduce the time required of single model training by 76.2%, and multiple training tasks can run concurrently. Therefore, it can adapt to the large-scale energy-saving scenario and greatly improve the model iteration efficiency, which improves the energy-saving rate and help green energy conservation for data centers.