AI-Enabled Traffic Flow-Prediction and Function-Configuration for 5G Networks: An Integrated Design of High Reliability and Low Cost

With network function virtualization (NFV) technologies, network functions (NFs) are implemented and isolated from hardware resources to complete complex tasks flexibly and economically in 5G networks. However, high-dynamic traffic puts 5G networks at great risk of NF errors. NF scalability is a promising technology to solve this problem. Existing NF scaling methods suffer from the following issues: 1) Threshold-based methods execute NF scaling strategies afterward, resulting in long-time response issues. 2) Artificial intelligence (AI)-driven methods face problems of insufficient training samples and failure to consider the communication reliability and deployment cost jointly in NF scaling decision-making. To solve these problems, we propose an intelligent traffic flow-based NF scaling mechanism. First, the smart traffic flow-based NF scaling framework is designed, consisting of the intelligent traffic flow prediction component and the NF function configuration component. Second, we propose a few-shot learning (FSL)-enabled traffic flow prediction scheme, where the FSL model is constructed to predict traffic flow in new areas with few training samples. Third, a deep reinforcement learning (DRL)-driven NF configuration scheme is designed, which aims to improve the integrated communication reliability and deployment cost for long-term profits. Finally, experimental evaluation verifies the effectiveness of the proposed intelligent traffic flow-based NF scaling mechanism.