DRL-based Slice Placement under Realistic Network Load Conditions

• URL: http://arxiv.org/abs/2109.12857v1
• Date: Mon, 27 Sep 2021 07:58:45 GMT
• Title: DRL-based Slice Placement under Realistic Network Load Conditions
• Authors: Jos\'e Jurandir Alves Esteves, Amina Boubendir, Fabrice Guillemin and Pierre Sens
• Abstract summary: We propose a network slice placement optimization solution based on Deep Reinforcement Learning (DRL) The solution is adapted to networks with large scale and under non-stationary traffic conditions (namely, the network load) We demonstrate the applicability of the proposed solution and its higher and stable performance over a non-controlled DRL-based solution.
• Score: 0.8459686722437155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose to demonstrate a network slice placement optimization solution based on Deep Reinforcement Learning (DRL), referred to as Heuristically-controlled DRL, which uses a heuristic to control the DRL algorithm convergence. The solution is adapted to realistic networks with large scale and under non-stationary traffic conditions (namely, the network load). We demonstrate the applicability of the proposed solution and its higher and stable performance over a non-controlled DRL-based solution. Demonstration scenarios include full online learning with multiple volatile network slice placement request arrivals.

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