Using Distributed Reinforcement Learning for Resource Orchestration in a Network Slicing Scenario

TL;DR

This paper proposes a distributed deep reinforcement learning framework using Advantage Actor Critic for dynamic resource allocation in network slicing, demonstrating superior performance and adaptability over static and empirical strategies.

Contribution

It introduces a novel distributed DRL approach with A2C for resource orchestration in network slicing, emphasizing cooperation among multiple agents.

Findings

Outperforms static resource allocation methods

Achieves better performance than empirical strategies

Ensures high adaptability without retraining

Abstract

The Network Slicing (NS) paradigm enables the partition of physical and virtual resources among multiple logical networks, possibly managed by different tenants. In such a scenario, network resources need to be dynamically allocated according to the slices' requirements. In this paper, we attack the above problem by exploiting a Deep Reinforcement Learning approach. Our framework is based on a distributed architecture, where multiple agents cooperate towards a common goal. The agents' training is carried out following the Advantage Actor Critic algorithm, which allows to handle continuous action spaces. By means of extensive simulations, we show that our approach yields better performance than both a static allocation of system resources and an efficient empirical strategy. At the same time, the proposed system ensures high adaptability to different scenarios without the need for additional training.