Admission Control in Sliced Networks, with Predictive Analytics - Ngufor, Perose (Department of Electrical Engineering, 2024)
Over the years, the telecommunications industry has constantly adapted to accommodate the rising demand for more specialised network connectivity. Network slicing was introduced as a solution for providing specialised networks to customers. However, network slicing has a set of objectives which require that legacy network functions be revisited and updated to support network slicing. One such function is admission control. This work proposes two admission control algorithms and investigates how the admission control function can be improved by incorporating traffic forecasting into the admission control process. In this work, we present the state-of-the-art in admission control in sliced networks and the state-of-the-art of the application of predictive analytics to admission control. We design and evaluate two intra-slice admission control algorithms namely, the Decision Matrix algorithm and the Utility Index algorithm. A real IP network dataset, containing network flows collected from the University of Cauca, Popayán network is used for the simulation and evaluation of these admission control algorithms. The proposed admission control algorithms presented various strengths, with the Utility Index algorithm being highly profitable to the operator and the Decision Matrix algorithm being suitable for traffic with a large proportion of high priority traffic. A traffic forecasting model was implemented based on the Holt-Winters Exponential Smoothing predictive model. This forecasting model was trained using the network data from the real IP network dataset and then incorporated into the admission control process. For prediction-based admission control, the traffic forecasting model was used to forecast resource requirements of future network traffic in each slice and pre-emptively make provisions for the upcoming traffic. The performance of the intra-slice admission control algorithms with and without the influence of traffic forecasting was analysed and it was found that the use of predictive analytics to predict future slice traffic allows for dynamic allocation of slice resources. Prediction-based admission control, when compared to the admission control without predictions, showed better performance in terms of probability of blocking, system utilisation, and profitability to the operator.