vrAIn: Deep Learning Based Orchestration for Computing and Radio Resources in vRANs

The virtualization of radio access networks (vRAN) is the last milestone in the NFV revolution. However, the complex relationship between computing and radio dynamics make vRAN resource control particularly daunting. We present vrAIn, a resource orchestrator for vRANs based on deep reinforcement learning. First, we use an autoencoder to project high-dimensional context data (traffic and channel quality patterns) into a latent representation. Then, we use a deep deterministic policy gradient (DDPG) algorithm based on an actor-critic neural network structure and a classifier to map contexts into resource control decisions. We have evaluated vrAIn experimentally, using an open-source LTE stack over different platforms, and via simulations over a production RAN. Our results show that: (i) vrAIn provides savings in computing capacity of up to 30 percent over CPU-agnostic methods; (ii) it improves the probability of meeting QoS targets by 25 percent over static policies; (iii) upon computing capacity under-provisioning, vrAIn improves throughput by 25 percent over state-of-the-art schemes; and (iv) it performs close to an optimal offline oracle. To our knowledge, this is the first work that thoroughly studies the computational behavior of vRANs and the first approach to a model-free solution that does not need to assume any particular platform or context.

Automated summary

This paper presents vrAIn, a resource orchestrator for vRANs based on deep reinforcement learning. By using an autoencoder to project high-dimensional context data and employing a deep deterministic policy gradient algorithm, vrAIn effectively maps contexts into resource control decisions. Experimental evaluation demonstrates the superior performance of vrAIn in terms of saving computing capacity, improving QoS targets, and increasing throughput.