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

作     者：Wei Jiang Daquan Feng Liping Qian Yao Sun Wei Jiang;Daquan Feng;Liping Qian;Yao Sun

作者机构：College of Information EngineeringZhejiang University of TechnologyHangzhou 310023China Shenzhen Key Laboratory of Digital Creative TechnologyGuangdong Province Engineering Laboratory for Digital Creative TechnologyCollege of Electronics and Information EngineeringShenzhen UniversityShenzhen 518060China James Watt School of EngineeringUniversity of GlasgowG128QQScotlandUK 

出 版 物：《Journal of Communications and Information Networks》 (通信与信息网络学报（英文）)

年 卷 期：2024年第9卷第2期

页      面：162-175页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 0810[工学-信息与通信工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 081001[工学-通信与信息系统] 

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 

主　　题：deep learning mobile edge computing user admission control resource scheduling 

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 *** 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.