AbstractTo support various service requirements such as massive Machine Type Communications, Ultra-Reliable and Low-Latency Communications in 5G scenario, Network Function Virtualization (NFV) plays an important role in the 5G network architecture to manage and orchestrate network services. As the key network function responsible for mobility management, Access and Mobility Management Function (AMF) can be deployed flexibly at the edge of the radio access network to improve the performance of mobility management based on NFV. In this paper, the optimal placement of AMF is addressed based on Deep Reinforcement Learning (DRL) in a heterogeneous radio access network, which aims to minimize the network utility including the average delay of mobility management requests at AMF, the average wired hops to relay the requests and the cost of AMF instances. By considering time-varying features including user mobility and the arrival rate of user mobility management requests, an AMF optimal placement approach is proposed for the long term optimization. Simulation results show that the performance of the proposed DRL based AMF optimal placement approach outperforms that of the baselines.
As a modern industrial division of labor and production area that adapts to market competition and industrial upgrading, the industrial park is a generation model for the country to actively plan and promote development. The integrated energy system of industrial parks is of great significance for improving energy efficiency, promoting large-scale development of renewable energy, improving the utilization of social infrastructure and energy supply, and achieving energy conservation and emission reduction targets. The combination of industrial parks and integrated energy service platform technologies can realize the coordinated operation and resource sharing of various energy systems, meet the individual needs of campus users, and provide diversified services for system maintainers, park managers, enterprises, and households. Based on the summary of the characteristics of the campus-type energy Internet, the paper studies the key technologies of its integrated energy service platform. The key technologies of information collection, source-charge prediction, multi-energy coordination scheduling and advanced application services of the company’s latest integrated energy service platform are introduced, and suggestions for the development of integrated energy service platform in the park are put forward.
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