Published in 2023

Network slicing in virtualized 5G Core with VNF sharing

5G Core Network slicing Machine learning LSTM

Authors: Azad Jalalian Zharabad, Saleh Yousefi, Thomas Kunz

Journal: Journal of Network and Computer Applications

Description:

Through multiplexing separate virtual networks on the same network infrastructure, network slicing will lead to customization, scalability, flexibility, and isolation of services in different domains of the network. It also results in lower CAPEX/OPEX via increased utilization of network resources. In this paper, we have formulated an Integer Linear Programming (ILP) model to create network slices in the 5G virtualized core by considering the sharing of virtual network functions (VNFs) between different network slices to minimize cost. The ILP model aims to minimize the number of VNF instances and resource consumption while meeting the network slice requirements. The main motivation for sharing the 5G core VNFs among different network slices is to reduce the number of VNF instances, which results in a lower management overhead of VNFs. As solving the optimization problems is time-consuming and does not scale for larger deployments, we also developed a Heuristic Backtracking Algorithm for Network Slicing (HBA-NS). Furthermore, in order to account for the dynamic nature of traffic, our proposed network slicing framework also includes a CNN-LSTM deep learning model to predict UEs’ requests in each network slice. Based on the predicted requests, the HBA-NS algorithm scales allocated resources of different network slices to meet the dynamic SLA requirements of users. Our evaluation results show that the solution achieved by the HBA-NS algorithm is on average 6% worse than the optimal solution in the worst case but derived at a significantly lower run time. We also evaluated the proposed network slicing framework in terms of different criteria including its isolation property. Our simulation results show that network slicing can provide better performance compared to competitive approaches.