| dc.description.abstract | The development of AIoT (AI+IoT) has caused the network to carry a lot of load, especially the centralized cloud computing, which causes even more serious network latency and conflicts with the development of AIoT’s stricter timeliness requirement, and the edge computing is currently as one of the most promising solutions. Therefore, we study an AIoT framework that conforms to the AIoT heterogeneous network environment and supports vertical horizontal decentralized computing. It uses multi-layer architecture, microservices and virtualization technology to enable various services within the framework to quickly move and execute in different architectures. We use the cluster gateway example of the face recognition project to demonstrate an effective edge computing system. Based on the research results of comparing with traditional development method and virtual machine method, this framework significantly reduces the number of building development and operation environments times, and ensure performing consistent functions when crossing platform and landing, meanwhile, it does not generate obviously additional overhead. The lightweight image which saves mush more memory, can be executed more much copies, shipped more faster, and the mechanism of quick deployment and load balancing can deal with different situation and more service requests flexibly. The results of this research can provide developers as a reference platform for developing and operating an economical and efficient AIoT edge computing system. | en_US |