RT Journal Article
T1 Live migration of virtual machine and container based mobile core network components: A comprehensive study
A1 Ramanathan, Shunmugapriya
A1 Kondepu, Koteswararao
A1 Razo, Miguel
A1 Tacca, Marco
A1 Valcarenghi, Luca
A1 Fumagalli, Andrea
AB With the increasing demand for openness, flexibility, and monetization, the Network Function Virtualization (NFV) of mobile network functions has become the embracing factor for most mobile network operators. Early reported field deployments of virtualized Evolved Packet Core (EPC) - the core network (CN) component of 4G LTE and 5G non-standalone mobile networks - reflect this growing trend. To best meet the requirements of power management, load balancing, and fault tolerance in the cloud environment, the need for live migration of these virtualized components cannot be shunned. Virtualization platforms of interest include both Virtual Machines (VMs) and Containers, with the latter option offering more lightweight characteristics. This paper's first contribution is the proposal of a framework that enables migration of containerised virtual EPC components using an open-source migration solution which does not fully support the mobile network protocol stack yet. The second contribution is an experimental-based comprehensive analysis of live migration in two virtualization technologies - VM and Container - with the additional scrutinization on the container migration approach. The presented experimental comparison accounts for several system parameters and configurations: flavor (image) size, network characteristics, processor hardware architecture model, and the CPU load of the backhaul network components. The comparison reveals that the live migration completion time and also the end-user service interruption time of the virtualized EPC components is reduced approximately by 70% in the container platform when using the proposed framework.
PB IEEE
SN 2169-3536
YR 2021
FD 2021-07-26
LK https://hdl.handle.net/10016/34089
UL https://hdl.handle.net/10016/34089
LA eng
NO This work was supported in part by the NSF under Grant CNS-1405405, Grant CNS-1409849, Grant ACI-1541461, and Grant CNS-1531039T; and in part by the EU Commission through the 5GROWTH Project under Grant 856709.
DS e-Archivo
RD 1 sept. 2024