DRL-based Slice Placement Under Non-Stationary Conditions

2021/08/05 by Esteves, Jose Jurandir Alves, Boubendir, Amina, Guillemin, Fabrice +1
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Networking and Internet Architecture (cs.NI)

paper · doi:10.48550/arxiv.2108.02495

Abstract

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 DRL-heuristic approach is more reliable than pure-DRL in a real non-stationary network scenario.

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