Performance and viability analysis of deploying cloud-native 5G autoscaling platforms

Yucebas, Amber, Xiong, Ruoting, Ren, Yi, Zhang, Xu and Parr, Gerard (2025) Performance and viability analysis of deploying cloud-native 5G autoscaling platforms. In: in Proc. The 1st free5GC World Forum 2025, in conjunction with ACM CCS 2025. UNSPECIFIED. (In Press)

Full text not available from this repository. (Request a copy)

Abstract

5G mobile network technology is undergoing rapid deployment. Autoscaling in 5G refers to the dynamic allocation and removal of network functions based on real-time service demand. It provides additional capacity to serve new users, while avoiding the risk of excessive costs. In this paper, we compare two stateless 5G autoscaling platforms: CoreKube and free5GC-helm, both deployed on the Hetzner Cloud platform. We utilize PacketRusher to generate high load for autoscaling evaluation, and collect metrics for analysis. Additionally, we analyze the bottleneck and autoscaling problem of free5GC-helm, providing guidance for real-world deployment. Our investigation revealed that the free5GC-helm scaling mechanism quickly encounters bottlenecks, primarily due to decisions made within the network repository function.

Item Type:	Book Section
Uncontrolled Keywords:	5g,network function virtualization,autoscaling,kubernetes,stateless network
Faculty \ School:	Faculty of Science Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and AI Faculty of Science > Research Groups > Cyber Intelligence and Networks
Depositing User:	LivePure Connector
Date Deposited:	11 Nov 2025 15:30
Last Modified:	11 Nov 2025 15:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/100950
DOI:

Actions (login required)

View Item