An intelligent scheduling for 5G user plane function placement and chaining reconfiguration

Services and use cases in 5G and beyond networks are characterized by strict requirements such as ultra-low latency, increased capacity, and high user mobility. Moreover, these networks must be capable of satisfying these ambitious demands as well as anticipating and adapting to dynamically changing conditions in a quick and feasible manner. This study deals with the problem of determining the best time to readjust the user plane function (UPF) placement and session mapping configuration to avoid quality of service (QoS) degradation in the system due to user mobility. To this aim, we rely on machine learning (ML) techniques to anticipate poor QoS events and decide whether a reconfiguration procedure is required based on a pre-established QoS tolerance threshold. Specifically, an ML-based framework, called intelligent scheduling of the reconfiguration (ISR), is proposed to automate the reconfiguration process. This framework applies supervised ML methods, either regressors or classifiers, to predict the QoS values/status at a given time horizon. The simulation experiments revealed the proposed mechanism's superiority compared to the established scheduling baseline. The ISR solution could not only keep the system QoS under desired values most of the time but also reduce the number of readjustment events by at least 50% compared to the baselines.