Research on cloud-edge integration service architecture for edge computing

With the rapid development of the Internet of Things (IoT) and big data technologies, edge computing has become a crucial solution for real-time data processing and low-latency requirements. However, traditional edge computing architectures face bottlenecks in multi-node coordination and dynamic computation resource scheduling. To address this issue, this paper proposes a cloud-edge integration service architecture designed for edge computing. This architecture combines the advantages of edge computing and cloud computing, utilizing Kubernetes for containerized management to ensure efficient collaboration between edge nodes and the cloud platform. Edge nodes are responsible for real-time data collection and local computation, while the cloud platform handles large-scale data storage, deep learning model training, and batch processing. With optimized data transmission protocols, the system achieves low latency and high throughput in data flow. Experimental results show that the proposed architecture significantly improves system performance, reduces latency, and enhances scalability. This architecture provides an effective solution for intelligent data processing and large-scale deployment in edge computing environments.