Multi-Resource Network Slicing with Deep Reinforcement Learning for an Optimal QoS Satisfaction Ratio

The 5G network provides low latency, high speed, and large bandwidth characteristics, shorting the transmission time and providing efficient services to users. In a typical 5G network architecture, multiple resources, i.e., communication and computation resources, are provided to meet various requirements of the services. Therefore, proper resource allocation is important to improve the quality of services (QoS) of users while network slicing can properly allocate the resources according to their service requirements. In this paper, we propose a novel approach, deep reinforcement learning (DRL)-based network slicing with maximum QoS satisfaction (DRL-MQS), for multi-resource allocation. The concept of this approach is to first calculate the delay distribution of each service to obtain the overall QoS satisfaction ratio, and then use the DRL approach to find the resource allocation with an optimal QoS satisfaction ratio. The evaluation results show that the DRL-MQS can improve the QoS satisfaction ratio by 10.54% compared with the previous approach which tries to maximize resource utilization. Moreover, no matter what packet arrival rate, DRL-MQS always has the best performance.