AI Based Resource Management for 5G Network Slicing: History, Use Cases, and Research Directions

ABSTRACT 5G, 6G, and beyond networks promise to support vertical industrial services with strict QoS parameters, but the hardware‐based "one‐size‐fits‐all" model of legacy networks lacks the flexibility needed for diverse services. The foundation of 5G networks lies in softwarization, with...

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Published in:Concurrency and computation Vol. 37; no. 2
Main Authors: Dubey, Monika, Singh, Ashutosh Kumar, Mishra, Richa
Format: Journal Article
Language:English
Published: Hoboken, USA John Wiley & Sons, Inc 25.01.2025
Wiley Subscription Services, Inc
Subjects:
5G mobile communication
Artificial intelligence
Communications traffic
Edge computing
Industrial applications
Network function virtualization
Network slicing
Quality of service architectures
Resource management
Resource scheduling
SDN
Software-defined networking
State-of-the-art reviews
Wireless networks
ISSN:1532-0626, 1532-0634
Online Access:Get full text
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Abstract ABSTRACT 5G, 6G, and beyond networks promise to support vertical industrial services with strict QoS parameters, but the hardware‐based "one‐size‐fits‐all" model of legacy networks lacks the flexibility needed for diverse services. The foundation of 5G networks lies in softwarization, with network slicing, Software Defined Networking (SDN), and Network Function Virtualisation (NFV) serving as its core components. The network‐slicing‐based shared network environment necessitates an intelligent and flexible resource management approach. In this case, traditional approaches are no longer suitable for dealing with a dynamic network environment. With recent advancements, AI‐based approaches have the potential to manage resources autonomously. This paradigm shift underscores the need for deep and extensive investigation. However, existing literature on this subject is fragmented and lacks a cohesive overview of network slicing. To address these gaps, our review paper aims to provide a comprehensive scope of network slicing in a unified manner. In this sequence at first, this paper presented a conceptual overview of network slicing and enabling technologies, including SDN, NFV, and edge computing. Secondly, this paper identifies the relevant phases of resource management and presents AI‐based resource management for network traffic classification, admission, allocation, and scheduling. Finally, it also discusses the deployment of network slicing‐enabled key use cases and their practical deployment, the research gap, and open research challenges. To the best of our knowledge, this is the first attempt to critically analyze and present a consolidated review of the state of the art in network slicing resource management modules and network slicing‐enabled key industrial use cases. This paper aims to guide researchers in developing innovative solutions and assist network players in the practical deployment of network slices for industrial applications.
AbstractList 5G, 6G, and beyond networks promise to support vertical industrial services with strict QoS parameters, but the hardware‐based "one‐size‐fits‐all" model of legacy networks lacks the flexibility needed for diverse services. The foundation of 5G networks lies in softwarization, with network slicing, Software Defined Networking (SDN), and Network Function Virtualisation (NFV) serving as its core components. The network‐slicing‐based shared network environment necessitates an intelligent and flexible resource management approach. In this case, traditional approaches are no longer suitable for dealing with a dynamic network environment. With recent advancements, AI‐based approaches have the potential to manage resources autonomously. This paradigm shift underscores the need for deep and extensive investigation. However, existing literature on this subject is fragmented and lacks a cohesive overview of network slicing. To address these gaps, our review paper aims to provide a comprehensive scope of network slicing in a unified manner. In this sequence at first, this paper presented a conceptual overview of network slicing and enabling technologies, including SDN, NFV, and edge computing. Secondly, this paper identifies the relevant phases of resource management and presents AI‐based resource management for network traffic classification, admission, allocation, and scheduling. Finally, it also discusses the deployment of network slicing‐enabled key use cases and their practical deployment, the research gap, and open research challenges. To the best of our knowledge, this is the first attempt to critically analyze and present a consolidated review of the state of the art in network slicing resource management modules and network slicing‐enabled key industrial use cases. This paper aims to guide researchers in developing innovative solutions and assist network players in the practical deployment of network slices for industrial applications.
ABSTRACT 5G, 6G, and beyond networks promise to support vertical industrial services with strict QoS parameters, but the hardware‐based "one‐size‐fits‐all" model of legacy networks lacks the flexibility needed for diverse services. The foundation of 5G networks lies in softwarization, with network slicing, Software Defined Networking (SDN), and Network Function Virtualisation (NFV) serving as its core components. The network‐slicing‐based shared network environment necessitates an intelligent and flexible resource management approach. In this case, traditional approaches are no longer suitable for dealing with a dynamic network environment. With recent advancements, AI‐based approaches have the potential to manage resources autonomously. This paradigm shift underscores the need for deep and extensive investigation. However, existing literature on this subject is fragmented and lacks a cohesive overview of network slicing. To address these gaps, our review paper aims to provide a comprehensive scope of network slicing in a unified manner. In this sequence at first, this paper presented a conceptual overview of network slicing and enabling technologies, including SDN, NFV, and edge computing. Secondly, this paper identifies the relevant phases of resource management and presents AI‐based resource management for network traffic classification, admission, allocation, and scheduling. Finally, it also discusses the deployment of network slicing‐enabled key use cases and their practical deployment, the research gap, and open research challenges. To the best of our knowledge, this is the first attempt to critically analyze and present a consolidated review of the state of the art in network slicing resource management modules and network slicing‐enabled key industrial use cases. This paper aims to guide researchers in developing innovative solutions and assist network players in the practical deployment of network slices for industrial applications.
Author Dubey, Monika
Singh, Ashutosh Kumar
Mishra, Richa
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  givenname: Richa
  orcidid: 0000-0002-6770-7188
  surname: Mishra
  fullname: Mishra, Richa
  email: richa_mishra@allduniv.ac.in
  organization: University of Allahabad, Allahabad
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Snippet ABSTRACT 5G, 6G, and beyond networks promise to support vertical industrial services with strict QoS parameters, but the hardware‐based "one‐size‐fits‐all"...
5G, 6G, and beyond networks promise to support vertical industrial services with strict QoS parameters, but the hardware‐based "one‐size‐fits‐all" model of...
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SubjectTerms 5G mobile communication
Artificial intelligence
Communications traffic
Edge computing
Industrial applications
Network function virtualization
Network slicing
Quality of service architectures
Resource management
Resource scheduling
SDN
Software-defined networking
State-of-the-art reviews
Wireless networks
Title AI Based Resource Management for 5G Network Slicing: History, Use Cases, and Research Directions
URI https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fcpe.8327
https://www.proquest.com/docview/3152768875
Volume 37
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