MemorAI: Energy-Efficient Last-Level Cache Memory Optimization for Virtualized RANs

TL;DR

This paper proposes an energy-efficient cache memory allocation method for virtualized RANs, utilizing a vBS digital twin and classifier to optimize energy use while maintaining performance.

Contribution

It introduces a novel cache allocation mechanism for vRANs that leverages a vBS digital twin and classifier to reduce energy consumption effectively.

Findings

Approach closely matches offline optimal performance

Outperforms standard cache management methods

Reduces energy consumption in virtualized RANs

Abstract

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.