A Novel 5G Digital Twin Approach for Traffic Prediction and Elastic Network Slice Management

5G and beyond cellular networks are promising technologies for Industrial Internet of Things (IIoT) because of their support for high bandwidth, low latency, reliability and differentiated services that are provided through a virtualization technique called network slicing. However, the dynamic nature of IIoT traffic and their differentiated QoS requirements makes provisioning multiple network slices on the same shared physical infrastructure a challenging problem. To address this problem, this paper presents an approach that uses a digital twin of the 5G network in predicting network traffic to improve network resource utilization, reliability, and efficiency. Specifically, the digital twin combines Holt smoothing and time series transformer models to predict end-to-end 5G network traffic over multiple time steps. Empirical evaluations comparing the performance of different machine learning algorithms used by the digital twin on two datasets: LTE 4G network traffic (497, 545 instances with 4 features) and Unicauca-Version2-87Atts (3, 577, 296 instances with 87 features) reveal that the combination of Holt smoothing and time series transformer models yields the best results. The prediction results from our approach are then used in provisioning elastic network slices.