Conference paper Open Access

Continuous Multi-objective Zero-touch Network Slicing via Twin Delayed DDPG and OpenAI Gym

Rezazadeh, Farhad; Chergui, Hatim; Alonso, Luis; Verikoukis, Christos

Artificial intelligence (AI)-driven zero-touch network slicing (NS) is a new paradigm enabling the automation of resource management and orchestration (MANO) in multi-tenant beyond 5G (B5G) networks. In this paper, we tackle the problem of cloud-RAN (C-RAN) joint slice admission control and resource allocation by first formulating it as a Markov decision process (MDP). We then invoke an advanced continuous deep reinforcement learning (DRL) method called twin delayed deep deterministic policy gradient (TD3) to solve it. In this intent, we introduce a multi-objective approach to make the central unit (CU) learn how to re-configure computing resources autonomously while minimizing latency, energy consumption and virtual network function (VNF) instantiation cost for each slice. Moreover, we build a complete 5G C-RAN network slicing environment using OpenAI Gym toolkit where, thanks to its standardized interface, it can be easily tested with different DRL schemes. Finally, we present extensive experimental results to showcase the gain of TD3 as well as the adopted multi-objective strategy in terms of achieved slice admission success rate, latency, energy saving and CPU utilization.

Grant numbers : SPOT5G - Single Point of attachment communications heterogeneous mobile data networks ( TEC2017-87456-P), 5G-Solutions - 5G-Solutions (H2020-ICT-2018-3 // Grant agreement ID: 856691) and MonB5G - Distributed management of Network Slices in beyond 5G (code: 871780) projects. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Publication date:
December 7, 2020
DOI:
10.5281/zenodo.4459653

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Keyword(s):
Admission control B5G continuous DRL C-RAN network slicing OpenAI Gym resource allocation zerotouch
Grants:
European Commission:
  • 5G STEP FWD - 5G System Technological Enhancements Provided by Fiber Wireless Deployments (722429)
Meeting:
IEEE Global Communications Conference (IEEE GLOBECOM 2020), virtual event, 7-11 December 2020
License (for files):
Creative Commons Attribution 4.0 International

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