# Assessing the Cloud-RAN in the Linux Kernel: Sharing Computing and Network Resources
###### 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. Cloud radio access network (Cloud-RAN); functional splitting; vRAN; 5G cellular networks; RT Linux +
Footnote †: journal: _Sensors_  
0000-0001-6600-0000][PERSON]. [PERSON], [PERSON]. managing latency-tolerant tasks such as control layer functionalities [9]. Despite the RT-Kernel ability to prioritize RT processes, the performance of latency-critical vBBU functions might be potentially impacted by computation-intensive workloads running in the user-space, as well as from non-preemptible Kernel threads, especially when they are scheduled on the same CPUs. Furthermore, in the advent future generation of mobile systems like 6G and the proliferation of IoT devices, the mobile network architecture must support an ever-increasing number of latency-critical applications. This requires an in-depth understanding of resource sharing implications to maintain stringent performance requirements. While the existing literature suggests that vBBUs can be effectively run on general-purpose servers [10], the detailed operational implications of such shared-resource scenarios remain not fully understood [11]. Our study seeks to bridge this gap by investigating how compute-intensive workloads and Kernel thread activity influence vBBU performance. Moreover, we assess the effectiveness of CPU management methodologies such as CPU affinity [12] and CPU isolation [13], which are established techniques used to enhance the performance and stability of RT systems in general-purpose computing environments [14; 15; 16; 17]. This study offers a focused empirical analysis of deploying vBBUs on general-purpose edge servers managed by the Linux RT-Kernel, with a specific emphasis on the performance implications of resource sharing in Cloud-RAN deployment. Although our analysis is concentrated on this niche, it serves as a foundation for further inquiry into various facets of vBBU performance degradation in different operational contexts. Our contributions are twofold. Firstly, we provide an empirical examination of resource sharing for latency-critical systems like vBBUs on general-purpose edge servers, particularly relevant in emerging generations of mobile systems such as 5G and potential 6G architectures [18; 19]. Secondly, we investigate the applicability of CPU affinity and isolation strategies in mitigating performance degradation in Cloud-RAN environments managed by the Linux Kernel. This work advances the understanding of vBBU operations in shared environments, crucial for optimizing Cloud-RAN implementations and ensuring consistent vBBU performance across diverse computational settings.