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

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.