ML-driven scaling of 5G Cloud-Native RANs

The evolution of the different network functions to a cloud-native configuration creates fertile ground for the efficient management and reconfiguration of the network. Through the wide application of softwarization and virtualization, cloud-native approaches can extend even to the RAN, that has been dominated by monolithic non-configurable hardware equipment in the past generations of mobile network access. As such, a cloud-native deployment can cover the end-to-end SG network architecture, from the Core Network to the base stations, with the respective services benefiting from several advanced features, such as automatic scaling of the deployed functions based on monitored metrics. Through the application of Machine Learning, the evolution of the metrics can be predicted and thus the respective functions can be pro-actively scaled. In this work, we use an end-to-end real-world cloud-native deployment of a SG network, and deal with two different types of scaling, applied at three different parts of the network: vertical scaling for the base station, and horizontal scaling for control and user plane functions of the core network. We use a real-world dataset for replicating traffic over our setup and closely monitor the evolution of metrics from different parts of the network. By applying Machine Learning methods, we accurately predict the future network load and use it to decide on the pro-active allocation of resources for the RAN and the Core Network.