DRL-based Slice Placement Under Non-Stationary Conditions - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

DRL-based Slice Placement Under Non-Stationary Conditions

Amina Boubendir
  • Fonction : Auteur
  • PersonId : 1080196
Fabrice Guillemin
  • Fonction : Auteur
  • PersonId : 1080198
Pierre Sens

Résumé

We consider online learning for optimal network slice placement under the assumption that slice requests arrive according to a non-stationary Poisson process. We propose a framework based on Deep Reinforcement Learning (DRL) combined with a heuristic to design algorithms. We specifically design two pure-DRL algorithms and two families of hybrid DRL-heuristic algorithms. To validate their performance, we perform extensive simulations in the context of a large-scale operator infrastructure. The evaluation results show that the proposed hybrid DRL-heuristic algorithms require three orders of magnitude of learning episodes less than pure-DRL to achieve convergence. This result indicates that the proposed hybrid DRLheuristic approach is more reliable than pure-DRL in a real non-stationary network scenario.
Fichier principal
Vignette du fichier
CNSM2021.pdf (1.11 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03332502 , version 1 (02-09-2021)

Identifiants

  • HAL Id : hal-03332502 , version 1

Citer

Jose Jurandir Alves Esteves, Amina Boubendir, Fabrice Guillemin, Pierre Sens. DRL-based Slice Placement Under Non-Stationary Conditions. CNSM 2021 - 17th International Conference on Network and Service Management, Oct 2021, Izmir, Turkey. ⟨hal-03332502⟩
89 Consultations
89 Téléchargements

Partager

Gmail Facebook X LinkedIn More