Assessing the Cloud-RAN in the Linux Kernel: Sharing Computing and Network Resources

Ocampo, Andres F., Mah Rukh, Fida ORCID: 0000-0001-7660-1150, Elmokashfi, Ahmed and Bryhni, Haakon (2024) Assessing the Cloud-RAN in the Linux Kernel: Sharing Computing and Network Resources. Sensors, 24 (7). Art 2365. doi:10.3390/s24072365

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Abstract

Cloud-based Radio Access Network (Cloud-RAN) leverages virtualization to enable the coexistence of multiple virtual Base Band Units (vBBUs) with collocated workloads on a single edge computer, aiming for economic and operational efficiency. However, this coexistence can cause performance degradation in vBBUs due to resource contention. In this paper, we conduct an empirical analysis of vBBU performance on a Linux RT-Kernel, highlighting the impact of resource sharing with user-space tasks and Kernel threads. Furthermore, we evaluate CPU management strategies such as CPU affinity and CPU isolation as potential solutions to these performance challenges. Our results highlight that the implementation of CPU affinity can significantly reduce throughput variability by up to 40%, decrease vBBU’s NACK ratios, and reduce vBBU scheduling latency within the Linux RT-Kernel. Collectively, these findings underscore the potential of CPU management strategies to enhance vBBU performance in Cloud-RAN environments, enabling more efficient and stable network operations. The paper concludes with a discussion on the efficient realization of Cloud-RAN, elucidating the benefits of implementing proposed CPU affinity allocations. The demonstrated enhancements, including reduced scheduling latency and improved end-to-end throughput, affirm the practicality and efficacy of the proposed strategies for optimizing Cloud-RAN deployments.

Item Type:	Article
Article Type:	Article
Uncontrolled Keywords:	Cloud radio access network (Cloud-RAN); Functional splitting; vRAN; 5G cellular networks; RT Linux
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Research Priority Areas:	Applied Business & Technology
Depositing User:	Kamila Niekoraniec
Date Deposited:	25 Apr 2024 14:12
Last Modified:	26 Apr 2024 08:34
URI:	https://eprints.glos.ac.uk/id/eprint/13997

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