Efficiency Optimization for Computer Network Integrated Database Empowered by Artificial Intelligence of Things Technology

The collection, storage and sharing technology of massive perceptual data has promoted the rise of the Internet of things. Its large-scale application has put forward an urgent requirement for database to have strong data consistency and high resource efficiency. However, the existing database system architecture and management and control methods mostly reduce communication costs by reducing the amount of data transmitted and increasing node storage replicas, and lack of integrated awareness and optimization of system network resources, resulting in low database efficiency. For this reason, we construct an integrated computing network database system to drive the integrated perception representation of computing resources and networks, and enables the database with intelligent Internet of things technology (AIoT) to realize the joint intelligent scheduling of computing resources and networks, so as to reduce the overall cost and improve the efficiency of computing networks. First, we construct a distributed database network integrated with computing and network, and analyze its architecture characteristics. Then, in order to realize the unified representation of computing and communication optimization variables, an integrated cost perception model is constructed. On this basis, an intelligent one-stop resource optimization algorithm is proposed to maximize the overall computing efficiency of the database system. Finally, simulation experiments verify the superiority of the proposed architecture and algorithm in network performance, algorithm convergence, integration cost and resource efficiency.