Machine Learning based Performance Prediction for Cloud-native 5G Mobile Core Network

Network functions that apply advanced cloud-native technologies are called Cloud-native Network Functions (CNFs). CNFs reap many of the benefits of a microservices architecture. However, CNFs are expected to be used, for example, as a platform for MEC and will require more distributed deployment in various cloud environments from the edge to the private or public cloud than ordinary web services. As a result, the number of combinations of software and hardware resources will explode, making it difficult to design optimal hardware resources in accordance with the requirements of the various network services. To overcome this challenge, we propose an automated CNF provisioning engine that optimizes the hardware resources allocated to CNFs from the viewpoint of performance assurance and minimizing equipment costs even in various clouds. In this paper, we used machine learning for the cloud-native 5G mobile core to build a performance prediction model for both control plane and user plane functions under various hardware conditions on the basis of the performance characteristics data obtained from our test platform. From evaluating the prediction accuracy of the constructed models, we clarify that the models can predict with high accuracy even using features that can be easily fed back to the hardware resource design.