Deep Learning based Efficient Edge Slicing for System Cost Minimization in Wireless Networks  

作　　者：Wei Jiang Daquan Feng Liping Qian Yao Sun 

机构地区：[1]College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China [2]Shenzhen Key Laboratory of Digital Creative Technology,Guangdong Province Engineering Laboratory for Digital Creative Technology,College of Electronics and Information Engineering,Shenzhen University,Shenzhen 518060,China [3]James Watt School of Engineering,University of Glasgow,G128QQ,Scotland,UK

出　　处：《Journal of Communications and Information Networks》2024年第2期162-175,共14页通信与信息网络学报（英文）

基　　金：supported in part by the National Natural Science Foundation of China under Grant 62302450;in part by the Project Supported by Zhejiang Provincial Natural Science Foundation of China under Grant LQ24F020037.

摘　　要：It is widely recognized that the future wireless networks are able to efficiently slice heterogeneous resources to provide customized services for various use cases. However, it is challenging to meet the diverse requirements of ever-growing applications, especially the stringent requirements of numerous delay-sensitive and/or computation-intensive applications. To tackle this challenge, we should not only consider user admission control to cope with resource limitations, but also make resource management more intelligent and flexible to meet diverse service needs. Taking advantages of mobile edge computing(MEC)and network slicing, in this paper, we propose deep edge slicing(DES),to jointly optimize user admission control and resource scheduling with the aim of minimizing the system cost while guaranteeing multitudinous quality-of-service (QoS) requirements. Specifically, we first apply a deep reinforcement learning approach to select the optimal set of access users with different service requests for maximizing resource utilization.Then a deep learning algorithm is employed to predict traffic data for allocating the communication and computing resources to different slices in advance. Finally, we realize the dynamic scheduling of heterogeneous resources by solving the optimization problem of minimizing the system cost. Simulation results demonstrate that DES can greatly reduce the system cost compared to other benchmarks.

关 键 词：deep learning mobile edge computing user admission control resource scheduling 

分 类 号：TN92[电子电信—通信与信息系统]