A Study on 5G Network Slice Isolation Based on Native Cloud and Edge Computing Tools

TL;DR

This paper investigates resource allocation mechanisms in 5G network slices using native cloud and edge computing tools, demonstrating that CPU limitations enhance slice performance in a hospital medical video scenario.

Contribution

It introduces resource management strategies to improve slice isolation in private 5G networks with open-source tools, addressing integration challenges.

Findings

CPU limitations improve slice performance

Memory restrictions have minimal impact

Data and scripts are publicly available

Abstract

5G networks support various advanced applications through network slicing, network function virtualization (NFV), and edge computing, ensuring low latency and service isolation. However, private 5G networks relying on open-source tools still face challenges in maturity and integration with edge/cloud platforms, compromising proper slice isolation. This study investigates resource allocation mechanisms to address this issue, conducting experiments in a hospital scenario with medical video conferencing. The results show that CPU limitations improve the performance of prioritized slices, while memory restrictions have minimal impact. The generated data and scripts have been made publicly available for future research and machine learning applications.