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

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