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      Zero-touch Continuous Network Slicing Control via Scalable Actor-Critic Learning

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          Abstract

          Artificial intelligence (AI)-driven zero-touch network slicing is envisaged as a promising cutting-edge technology to harness the full potential of heterogeneous 5G and beyond 5G (B5G) communication systems and enable the automation of demand-aware resource management and orchestration (MANO). In this paper, we tackle the issue of B5G radio access network (RAN) joint slice admission control and resource allocation according to proposed slice-enabling cell-free massive multiple-input multiple-output (mMIMO) setup by invoking a continuous deep reinforcement learning (DRL) method. We present a novel Actor-Critic-based network slicing approach called, prioritized twin delayed distributional deep deterministic policy gradient (D-TD3)}. The paper defines and corroborates via extensive experimental results a zero-touch network slicing scheme with a multi-objective approach where the central server learns continuously to accumulate the knowledge learned in the past to solve future problems and re-configure computing resources autonomously while minimizing latency, energy consumption, and virtual network function (VNF) instantiation cost for each slice. Moreover, we pursue a state-action return distribution learning approach with the proposed replay policy and reward-penalty mechanisms. Finally, we present numerical results to showcase the gain of the adopted multi-objective strategy and verify the performance in terms of achieved slice admission rate, latency, energy, CPU utilization, and time efficiency.

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          Author and article information

          Journal
          17 January 2021
          Article
          2101.06654
          fb2e95d5-b40e-478c-b92d-8d12765401ce

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          Custom metadata
          cs.NI

          Networking & Internet architecture
          Networking & Internet architecture

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