Toward Effective and Sustainable Traffic Management with Network Scaling Configuration: A Federated FSL Approach

In 5G and beyond networks, the highly dynamic and time-varying environment leads to multiple types of network function (NF) errors. NF scaling technology is capable of addressing this problem by scaling up/down NFs adaptively according to real-time network traffic. Existing NF scaling approaches face the following issues: 1) Threshold-based approaches adjust NFs when traffic loads exceed/fall preset thresholds, which is passive and has a long response time. 2) Forecasting-based approaches suffer from inaccurate predictions, insufficient training data, and difficulty adapting to all dynamic and varying network environments. 3) Reinforcement learning-based approaches improve robustness, but still struggle with the problem of insufficient data. To address the above issues and challenges, we propose a federated traffic prediction-based NF scaling mechanism. First, the federated traffic flow prediction and function configuration framework is designed, including fine-grained federated traffic prediction and intelligent NF configuration. Second, we propose a federated few-shot learning-driven traffic flow prediction scheme, where Siamese FSL networks are constructed to forecast the traffic flow of new areas with few samples. Third, we devise a deep reinforcement learning-driven online NF configuration scheme to select real-time NF scaling strategies for efficient and sustainable networks. Finally, experimental results demonstrate that our proposed federated traffic flow prediction-based NF scaling mechanism achieves better performance than baselines.