XF-EdgeAI Team led by Prof. Wang Xiaofei from Tianjin University’s College of Intelligence and Computing won the First Prize in Huawei Cloud First Technology Innovation Competition with their work, the “Edge-cloud cluster scheduling system with edge autonomy and centralized cloud control”.
The competition is organized by Huawei Cloud, orienting at researchers from domestic universities and scientific research institutions. The competition builds a platform for exchanges and interactions between industry and academia so as to jointly promote technological innovation in the cloud and AI industry. Fifty-one teams from universities like Tsinghua University, Peking University, Shanghai Jiaotong University, Zhejiang University, Huazhong University of Science and Technology, Tianjin University and other universities participated in the competition. A bonus pool of 300,000 RMB is set for the winners, of which the first prize can receive 100,000 RMB.
As China is vigorously promoting the construction of new infrastructures, emerging technologies like 5G have been developed in all walks of life, including the network architecture represented by cloud computing technology. The proposal of edge computing can effectively alleviate the bottleneck problems encountered in the development of cloud computing, such as ultra-low latency service requirements and reliable security and privacy protection. Better collaborative work between edge computing and cloud computing can greatly promote the process of network intelligence. The edge computing industry has been committed to integrating edge technology and cloud-native technology. In recent years, the collaboration of cloud computing and edge computing has become a hot research topic for the industry.
Prof. Xiaofei Wang’s research team has been conducting in-depth research on edge intelligence, edge-cloud collaborated platform and resource scheduling, and published a number of high-level scientific research papers and achievements. The research team innovatively proposed autonomous dispatch of service requests based on reinforcement learning at the edge, and the orchestration of services deployed at the edge by the cloud, enabling automatic evolution of the system's operational efficiency to an overall optimal state.
By the College of Intelligence and Computing
