Abstract
With the rapid development of big data, cloud computing, mobile Internet and other new-generation information technologies, smart cities, carry more and more intelligent applications, and put forward higher requirements for data processing capacity and network service quality. The integration system of Edge-to-Cloud collaboration provides a feasible method to effectively improve network performance to adapt to the changing needs of users. However, the existing network management mechanism is difficult to adapt to the actual demand changes due to the characteristics of the edge-cloud fusion system, such as device diversity, location complexity and resource dynamics. This article to the idea of sliding mode control function of network virtualization technology and network, the study method and collaborative optimization theory as the foundation, of the application of static and dynamic models of the intelligence cities, fault prediction and fault tolerant management mechanism, task scheduling and resource allocation adaptive collaborative optimization mechanism, to ensure that the cities network real-time, reliability, and security, At the same time, combined with the smart campus platform of our school, the real-time performance and effectiveness of the network are tested and evaluated, providing a case demonstration for the intelligent application of smart cities. This project will further enrich and improve the theoretical system of end-to-end cloud collaborative optimization and provide new means and methods for intelligent cities network QoS(Quality of Service) guarantee.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have