The virtualization of Radio Access Networks (vRAN) is well on its way to become a reality, driven by its advantages such as flexibility and cost-effectiveness. However, virtualization comes at a high price - virtual Base Stations (vBSs) sharing the same computing platform incur a significant computing overhead due to in extremis consumption of shared cache memory resources. Consequently, vRAN suffers from increased energy consumption, which fuels the already high operational costs in 5G networks. This paper investigates cache memory allocation mechanisms' effectiveness in reducing total energy consumption. Using an experimental vRAN platform, we profile the energy consumption and CPU utilization of vBS as a function of the network state (e.g., traffic demand, modulation scheme). Then, we address the high dimensionality of the problem by decomposing it per vBS, which is possible thanks to the Last-Level Cache (LLC) isolation implemented in our system. Based on this, we train a vBS digital twin, which allows us to train offline a classifier, avoiding the performance degradation of the system during training. Our results show that our approach performs very closely to an offline optimal oracle, outperforming standard approaches used in today's deployments.

翻译：无线接入网虚拟化（vRAN）正凭借其灵活性与成本效益等优势逐步成为现实。然而，虚拟化也带来了高昂代价：共享同一计算平台的虚拟基站（vBS）因过度消耗共享缓存资源而产生显著计算开销。这导致vRAN的能耗增加，进一步加剧了5G网络中本已高昂的运营成本。本文研究了缓存分配机制在降低总能耗方面的有效性。通过实验性vRAN平台，我们分析了vBS的能耗与CPU利用率随网络状态（如流量需求、调制方案）变化的特征。随后，我们利用系统中实现的最后一级缓存（LLC）隔离特性，将高维问题分解至每个vBS独立处理，以此降低问题复杂度。基于此，我们训练了vBS数字孪生模型，支持离线训练分类器，从而避免训练期间系统性能退化。实验结果表明，本方法的性能与离线最优基准极为接近，显著优于当前部署中采用的常规方案。