Modeling of a Threshold-Based Scaling and Fractional Admission Controlling Problem for NSSI Blocks to Ensure QoS and Optimize Resource Utilization in 5G Network Slicing

Abstract

In the 5G core network, network functions can be flexibly scaled out/in network slices proactively. The autoscaling process increases effectiveness by scaling out network function instances and minimizes expenses by scaling NSSI (Network Slice Subnet Instance) blocks. 5G network functions have to deploy or terminate multiple NFIs (Network Function Instances) simultaneously; it significantly affects the system’s cost efficiency and ensures QoS (Quality of Service). In the paper, we will propose a Markov chain-based analytical model for the Threshold-based Scaling and Fractional Admission Controlling problem of NSSI blocks (called TSFAC-NB) within a 5G network slice. The model will incorporate two thresholds related to the scaling-out/scaling-in of NSSI blocks. We will also propose a FAC (Fractional Admission Controlling) mechanism in the model with two thresholds added to control NSSI blocks by the probability to optimize resource utilization. A threshold-based scaling and fractional admission controlling (TSFAC) algorithm is developed and implemented in Kubernetes-based Open5GS to evaluate the performance of TSFAC-NB experimentally. The simulation results show a similarity between the analytical and experimental results, in which the analytical model helps to determine the admission thresholds for the best performance of TSFAC-NB.