Mixed Logic Dynamic modeling used for Cloud Services Optimization in Smart City

The Internet of Things (IoT) technology for cloud services has made some progress. However, there are still many limitations in largescale information flow perception modeling and performance optimization. In this paper, the Mixed Logic Dynamic modeling method of IoT information flow perception is studied. The IoT sensor nodes, controlled nodes and coordination nodes are used to describe the system application scenarios, whereas the automaton is used to carry out internal information transmission, This lays an important foundation for the coordination and optimization of the system. Then, an open queuing method based on large-scale information flow perception modeling and network delay analysis method is proposed. By analyzing the end-to-end delay of the node path and the average delay analysis of the whole queuing network, the open queuing is obtained, So as to maximize the performance of the perceived network in the IoT. Aiming at the characteristics of distributed data of information flow perception in the IoT, a priority-based queuing network is proposed to model and analyze the aggregation nodes based on embedded multi-core System on Chip (SoC), which greatly improves the performance of embedded multicore SoC.