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Review
. 2022 Apr 15;22(8):3031.
doi: 10.3390/s22083031.

Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey

Johanna Andrea Hurtado Sánchez  1 , Katherine Casilimas  1 , Oscar Mauricio Caicedo Rendon  1
Affiliations
Review

Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey

Johanna Andrea Hurtado Sánchez et al. Sensors (Basel). .

Abstract

Network Slicing and Deep Reinforcement Learning (DRL) are vital enablers for achieving 5G and 6G networks. A 5G/6G network can comprise various network slices from unique or multiple tenants. Network providers need to perform intelligent and efficient resource management to offer slices that meet the quality of service and quality of experience requirements of 5G/6G use cases. Resource management is far from being a straightforward task. This task demands complex and dynamic mechanisms to control admission and allocate, schedule, and orchestrate resources. Intelligent and effective resource management needs to predict the services' demand coming from tenants (each tenant with multiple network slice requests) and achieve autonomous behavior of slices. This paper identifies the relevant phases for resource management in network slicing and analyzes approaches using reinforcement learning (RL) and DRL algorithms for realizing each phase autonomously. We analyze the approaches according to the optimization objective, the network focus (core, radio access, edge, and end-to-end network), the space of states, the space of actions, the algorithms, the structure of deep neural networks, the exploration-exploitation method, and the use cases (or vertical applications). We also provide research directions related to RL/DRL-based network slice resource management.

Keywords: admission control; deep reinforcement learning; network slicing; resource allocation; resource orchestration; resource scheduling.

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Conflict of interest statement

The authors declare no conflict of interest. The financiers had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
5G/6G network slices.
Figure 2
Figure 2
Resource management phases.
Figure 3
Figure 3
RL/DRL-based admission control architecture.
Figure 4
Figure 4
Resource allocation architecture using RL/DRL.
Figure 5
Figure 5
Resource orchestration architecture using RL/DRL.
Figure 6
Figure 6
Resource scheduling architecture using RL/DRL.
See this image and copyright information in PMC

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