DRL-based Slice Placement Under Non-Stationary Conditions

• URL: http://arxiv.org/abs/2108.02495v1
• Date: Thu, 5 Aug 2021 10:05:12 GMT
• Title: DRL-based Slice Placement Under Non-Stationary Conditions
• Authors: Jose Jurandir Alves Esteves, Amina Boubendir, Fabrice Guillemin, Pierre Sens
• Abstract summary: We consider online learning for optimal network slice placement under the assumption that slice requests arrive according to a non-stationary process. We specifically propose two pure-DRL algorithms and two families of hybrid DRL-heuristic algorithms. We show that the proposed hybrid DRL-heuristic algorithms require three orders of magnitude of learning episodes less than pure-DRL to achieve convergence.
• Score: 0.8459686722437155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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