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

TL;DR

This paper presents a novel AI-driven approach for automated multi-objective network slicing in 5G C-RAN using deep reinforcement learning, specifically TD3, within a standardized OpenAI Gym environment, achieving improved resource management.

Contribution

It introduces a multi-objective DRL framework with TD3 for autonomous resource reconfiguration in 5G network slicing, implemented in a standardized simulation environment.

Findings

Enhanced slice admission success rate

Reduced latency and energy consumption

Improved CPU utilization

Abstract

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.