Controlled Deep Reinforcement Learning for Optimized Slice Placement

• URL: http://arxiv.org/abs/2108.01544v1
• Date: Tue, 3 Aug 2021 14:54:00 GMT
• Title: Controlled Deep Reinforcement Learning for Optimized Slice Placement
• Authors: Jose Jurandir Alves Esteves, Amina Boubendir, Fabrice Guillemin, Pierre Sens
• Abstract summary: We present a hybrid ML-heuristic approach that we name "Heuristically Assisted Deep Reinforcement Learning (HA-DRL)" The proposed approach leverages recent works on Deep Reinforcement Learning (DRL) for slice placement and Virtual Network Embedding (VNE) The evaluation results show that the proposed HA-DRL algorithm can accelerate the learning of an efficient slice placement policy.
• Score: 0.8459686722437155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a hybrid ML-heuristic approach that we name "Heuristically Assisted Deep Reinforcement Learning (HA-DRL)" to solve the problem of Network Slice Placement Optimization. The proposed approach leverages recent works on Deep Reinforcement Learning (DRL) for slice placement and Virtual Network Embedding (VNE) and uses a heuristic function to optimize the exploration of the action space by giving priority to reliable actions indicated by an efficient heuristic algorithm. The evaluation results show that the proposed HA-DRL algorithm can accelerate the learning of an efficient slice placement policy improving slice acceptance ratio when compared with state-of-the-art approaches that are based only on reinforcement learning.

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