Network Traffic Prediction and Auto-Scaling of SFC using Temporal Fusion Transformer

To ensure efficient network operations, internet service providers need to take management actions such as deploying, scaling, and live migrating new virtual network functions (VNFs) in response to user demands and changes in network environments. However, relying on manual measures to handle such changes can result in longer response times and additional costs in the event of problems, compared to proactive management measures. Therefore, it is necessary to analyze network traffic and predict future traffic to proactively manage services and systems. This paper proposes a network traffic prediction model as a preliminary study for proactive service function chaining (SFC) configuration and VNF scaling research and performs auto-scaling of VNFs in a simulation environment. The proposed model uses a state-of-the-art algorithm Temporal Fusion Transformer (TFT), which considers the heterogeneity of data in time series data prediction, to predict actual traffic measurement in an OpenStack environment. Then, it shows the results of auto-scaling in the simulation environment based on the threshold value. The TFT model showed a maximum reduction of 94% in Mean Squared Error (MSE) compared to Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models in two real measurement datasets. Also, we were able to confirm that the use of traffic predicted by TFT can reduce the scaling frequency by up to 76%.