Exploring Intelligent Dynamic Resource Provisioning for Elastic Massive MIMO vRAN

The computational load in a Virtualized Radio Access Network (vRAN) varies rapidly and massive Multiple-Input Multiple-Output (MIMO) makes it even more dynamic. Existing massive MIMO vRAN systems commonly adopt static resource provisioning and thus often result in either under- or over-provisioning of compute resources. In this paper, we explore the feasibility of a Dynamic Resource Provisioning (DRP) in a massive MIMO vRAN to dynamically allocate Central Processing Unit (CPU) cores for varying computational loads. We investigate a Reinforcement Learning (RL)-based DRP algorithm that provisions optimal number CPU cores in an automated manner. To experimentally demonstrate its viability, we integrate our RL-based DRP into a real-time fully-software massive MIMO vRAN prototype system. Our early experimental results, however, do not show any significant advantage of an RL-based solution over a simple Steepest Gradient Descent (SGD)-based DRP. We believe that this surprising result is largely due to the fact that current open-source vRAN systems cannot sufficiently reflect the highly complex nature of the real vRAN. We further discuss the way ahead, challenges, and opportunities in this research direction.