A Heuristically Assisted Deep Reinforcement Learning Approach for Network Slice Placement

• URL: http://arxiv.org/abs/2105.06741v1
• Date: Fri, 14 May 2021 10:04:17 GMT
• Title: A Heuristically Assisted Deep Reinforcement Learning Approach for Network Slice Placement
• Authors: Jose Jurandir Alves Esteves, Amina Boubendir, Fabrice Guillemin, and Pierre Sens
• Abstract summary: We introduce a hybrid placement solution based on Deep Reinforcement Learning (DRL) and a dedicated optimization based on the Power of Two Choices principle. The proposed Heuristically-Assisted DRL (HA-DRL) allows to accelerate the learning process and gain in resource usage when compared against other state-of-the-art approaches.
• Score: 0.7885276250519428
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Network Slice placement with the problem of allocation of resources from a virtualized substrate network is an optimization problem which can be formulated as a multiobjective Integer Linear Programming (ILP) problem. However, to cope with the complexity of such a continuous task and seeking for optimality and automation, the use of Machine Learning (ML) techniques appear as a promising approach. We introduce a hybrid placement solution based on Deep Reinforcement Learning (DRL) and a dedicated optimization heuristic based on the Power of Two Choices principle. The DRL algorithm uses the so-called Asynchronous Advantage Actor Critic (A3C) algorithm for fast learning, and Graph Convolutional Networks (GCN) to automate feature extraction from the physical substrate network. The proposed Heuristically-Assisted DRL (HA-DRL) allows to accelerate the learning process and gain in resource usage when compared against other state-of-the-art approaches as the evaluation results evidence.

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