  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.
