There is a newer version of this record available.

Journal article Open Access

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

Jose A. Ayala-Romero; Andres Garcia-Saavedra; Marco Gramaglia; Xavier Costa-Perez; Albert Banchs; Juan J. Alcaraz


JSON-LD (schema.org) Export

{
  "description": "<p>The virtualization of radio access networks (vRAN) is the last milestone in the NFV revolution. However, the complex dependencies between computing and radio resources make vRAN resource control particularly daunting. We present vrAIn, a dynamic 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% over CPU-agnostic methods; (ii) it improves the probability of meeting QoS targets by 25% over static policies; (iii) upon computing capacity under-provisioning, vrAIn improves throughput by 25% 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.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Trinity College Dublin", 
      "@type": "Person", 
      "name": "Jose A. Ayala-Romero"
    }, 
    {
      "affiliation": "NEC Laboratories Europe GmbH", 
      "@id": "https://orcid.org/0000-0003-2005-2222", 
      "@type": "Person", 
      "name": "Andres Garcia-Saavedra"
    }, 
    {
      "affiliation": "Universidad Carlos III de Madrid", 
      "@type": "Person", 
      "name": "Marco Gramaglia"
    }, 
    {
      "affiliation": "NEC Laboratories Europe GmbH", 
      "@type": "Person", 
      "name": "Xavier Costa-Perez"
    }, 
    {
      "affiliation": "Universidad Carlos III de Madrid & IMDEA Networks", 
      "@type": "Person", 
      "name": "Albert Banchs"
    }, 
    {
      "affiliation": "Universidad Politecnica de Cartagena", 
      "@type": "Person", 
      "name": "Juan J. Alcaraz"
    }
  ], 
  "headline": "vrAIn: Deep Learning based Orchestration for Computing and Radio Resources in vRANs", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2021-01-15", 
  "url": "https://zenodo.org/record/5037024", 
  "keywords": [
    "RAN virtualization", 
    "resource management", 
    "machine learning"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.5037024", 
  "@id": "https://doi.org/10.5281/zenodo.5037024", 
  "@type": "ScholarlyArticle", 
  "name": "vrAIn: Deep Learning based Orchestration for Computing and Radio Resources in vRANs"
}
110
96
views
downloads
All versions This version
Views 11044
Downloads 9640
Data volume 697.9 MB290.3 MB
Unique views 9336
Unique downloads 8736

Share

Cite as