# Assessing the Cloud-RAN in the Linux Kernel: Sharing Computing and Network Resources
## 6 Conclusions  
This study provides insights into the impact of resource sharing with collocated workloads on vBBU performance in edge servers managed by the Linux Kernel. The empirical investigation highlights the substantial challenges posed by resource contention when vBBUs share resources with user-space tasks and Kernel threads. Effective CPU management strategies, particularly, CPU affinity, emerge as crucial mechanisms for significantly enhancing vBBU performance. The application of CPU affinity, demonstrates substantial benefits, including a notable reduction in throughput variability, decreased vBBU NACK ratios, and lowered scheduling latency within the Linux RT-Kernel. Although CPU isolation exhibits performance comparable to that in a reference scenario where vBBU is the only active process, the trade-off involves the potential underutilization of resources. In contrast, the allocation of orthogonal CPU affinity to containers hosting both vBBUs and best-effort applications demonstrates the potential to guarantee CPU isolation, thereby improving end-to-end vBBU performance in a Cloud-RAN setup. Looking ahead, the static nature of CPU affinity allocation in this study prompts a call for future exploration into dynamic assignment methodologies. Future research efforts will focus on adapting CPU affinity allocations based on real-time application demands,  
Figure 11: Cloud-RAN performance: end-to-end throughput measured by vBBU’s UEs. CPU affinity allocation: (i) noA vs. (ii) IA-WA. CPU usage, and availability, with a view to improving adaptability and responsiveness to varying workloads. Additionally, the research agenda includes the investigation of resource contention scenarios in diverse setups, such as those involving dynamic functional splitting within Cloud-RAN. The ultimate goal is to develop robust strategies and mechanisms that optimize vBBU performance across a spectrum of Cloud-RAN configurations. While our investigation anchors itself primarily on the exploration of CPU resource sharing and its direct impact on vBBU performance, we recognize the vast landscape of potential performance-affecting factors within a Cloud-RAN environment. Future studies could further this research by considering a more diverse array of performance metrics and external factors that contribute to system efficiency. As part of our future work, we look forward to comparisons with alternative resource management strategies to ascertain the distinct advantages of CPU affinity and CPU isolation within Cloud-RAN deployments. This study lays the groundwork by establishing a benchmark for the effectiveness of these well-understood strategies in managing vBBU performance in resource-sharing scenarios. We recognize the limitations arising from the scope of experimentation at our disposal and hope that future endeavors could expand upon our work, contributing further to the complex discussion of resource management in real-time systems. Although the results we have presented provide valuable insights into the performance dynamics of vBBUs within our experimental configuration, we fully recognize that these findings are contingent on the specific conditions and constraints of our setup. These results serve as a snapshot of performance under certain hardware specifications, network conditions, and software settings, providing a focused examination that is inherently limited in scope. Consequently, the broader applicability of our findings to different configurations warrants caution and further investigation. Moving forward, future research could benefit from a diverse range of experimental scenarios, including varying hardware platforms and network loads, to enhance the generalizability of results and provide a more versatile understanding of CPU management strategies in varied Cloud-RAN environments.