Network Data Analytics Function for IBN-based Network Slice Lifecycle Management
Khizar Abbas, Talha Ahmed Khan, Muhammad Afaq, Javier José Díaz Rivera, Wang‐Cheol Song
Abstract
Networks slicing in 5G network enables the network operators to accommodate the different quality of service (QoS) to their customers. Moreover, the data analytics in 5G mobile network can be seen as a robust solution to transform the challenging features of 5G into a reality. So, for that, the Third Generation Partnership Project (3GPP) has been introduced a network data analytics function (NWDAF) in 5G service-based architecture (SBA). NWDAF collects the data from different core and management domains and performs analytics on that historical data. It also enables the network operators (NOs) to train their Machine Learning (ML) techniques and use various third-party solutions. On the other side, the automation and management of the end - to-end (e2e) network slicing in a multi -domain environment is a critical task. Therefore, in this manuscript, we have designed a closed-loop Intent-based Networking (IBN) platform, which automatically ensures the commissioning, activation, run-time monitoring, and decommissioning of the network slices. Moreover, we have integrated the newly introduced 3GPP NWDAF with the IBN platform for efficient e2e network slice lifecycle management. By implementing different ML models in the NWDAF function, we can predict the slice load, user mobility, traffic forecasts, and anomaly detection from the network slices.