Intelligent-Slicing: An AI-assisted Network Slicing Framework for 5G-and-Beyond Networks

Authors

Alaa Awad Abdellatif, Qatar University
Amr Abo-eleneen, Qatar University
Amr Mohamed, Qatar University
Aiman Erbad, Hamad Bin Khalifa University, College of Science and Engineering
Nikhil V. Navkar, Hamad Medical Corporation
Mohsen Guizani, Mohamed Bin Zayed University of Artificial IntelligenceFollow

Document Type

Article

Publication Title

IEEE Transactions on Network and Service Management

Abstract

5G-and-beyond networks are designed to fulfill the communication and computation requirements of various industries, which requires not only transporting the data, but also processing them to meet/address diverse key performance indicators (KPIs). Network Function Virtualization (NFV) has emerged to enable this vision by: (i) collecting the requirements of diverse services, using graphs of Virtual Network Functions (VNFs); and (ii) mapping these requirements into network management decisions. Because of the latter, we need to efficiently allocate computing and network resources to support the desired services, and because of the former such decisions must be jointly optimized considering all KPIs associated with supported services. Thus, this paper proposes an optimized, intelligent network slicing framework to maintain a high performance of network operation by supporting diverse and heterogeneous services, while meeting new KPIs, e.g., reliability, energy consumption, and data quality. Different from the existing works, which are mainly designed considering traditional metrics like throughput and latency, we present a novel methodology and resource allocation schemes that enable high-quality selection of radio points of access, VNF placement and data routing, as well as data compression ratios, from the end users to the cloud. Our results depict the efficiency of the proposed framework in enhancing the network performance when compared to baseline approaches that consider partial network view or fair resource allocation.

First Page

1024

Last Page

1039

DOI

10.1109/TNSM.2023.3274236

Publication Date

5-9-2023

Keywords

6G network, AI for slicing, Artificial intelligence, Cloud computing, Costs, network function virtualization, Network slicing, pervasive network intelligence, Quality of service, Resource management, Routing, software-defined networking

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Recommended Citation

A. Awad Abdellatif, A. Abo-Eleneen, A. Mohamed, A. Erbad, N. V. Navkar and M. Guizani, "Intelligent-Slicing: An AI-Assisted Network Slicing Framework for 5G-and-Beyond Networks," in IEEE Transactions on Network and Service Management,vol. 20, no. 2, pp. 1024-1039, June 2023, doi: 10.1109/TNSM.2023.3274236.