Deep learning based resource forecasting for 5G core network scaling in Kubernetes environment
Résumé
5G networks are moving towards cloudification which gives the telecom operators the flexibility to manage their networks efficiently and cost-effectively. Scaling network functions on demand is one of the advantages of using container-based deployment in cloud environments. With the continuously changing network traffic patterns due to the emerging new 5G use cases, there is a need for novel automated network resources management approach in cloud-native environments. Considering the scale and the complexity of the 5G network, managing resources is a challenge. To address this, we propose a deep learning-based resource usage forecasting approach that provides useful insights for decision-making in containerized Network Function (CNF) scaling for the Kubernetes environment. Kubernetes is a container orchestration tool that becoming popular among Telecom operators due to its simplicity. We implemented a testbed in the Kubernetes environment to generate a dataset closer to real-world data for deep learning model training and evaluated the best-performing model for resource usage forecasting. We benchmarked our approach against another deep learning-based resource usage forecasting approach which proved our method can provide a highly accurate forecast for further horizons.