Machine Learning-Based QoS & Traffic-Aware Prediction-Assisted Dynamic Network Slicing

Network slicing allows operators to sell customized slices to various tenants at different prices. To provide better-performing and cost-efficient services, network slicing is looking to intelligent resource management approaches to be aligned to users’ activities per slice. In this article, we propose a radio access network (RAN) slicing design methodology for quality of service (QoS) provisioning, for differentiated services in a 5G network. A performance model is constructed for each service using machine learning (ML)-based approaches, optimized using interference coordination approaches, and used to facilitate service level agreement (SLA) mapping to the radio resource. The optimal bandwidth allocation is dynamically adjusted based on instantaneous network load conditions. We investigate the application of machine learning in solving the radio resource slicing problem and demonstrate the advantage of machine learning through extensive simulations. A case study is presented to demonstrate the effectiveness of the proposed radio resource slicing approach.

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Dynamic network slicing and resource allocation for heterogeneous wireless services

2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) ◽

10.1109/pimrc.2017.8292663 ◽

2017 ◽

Cited By ~ 9

Author(s):

Jeongho Kwak ◽

Joonyoung Moon ◽

Hyang-Won Lee ◽

Long Bao Le

Keyword(s):

Resource Allocation ◽

Dynamic Network ◽

Network Slicing ◽

Wireless Services

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Machine-Learning Defined Networking: Towards Intelligent Networks

Special Topics in Information Technology - SpringerBriefs in Applied Sciences and Technology ◽

10.1007/978-3-030-62476-7_1 ◽

2021 ◽

pp. 3-12

Author(s):

Sebastian Troia

Keyword(s):

Machine Learning ◽

Service Providers ◽

Dynamic Network ◽

Network Function Virtualization ◽

Traffic Demand ◽

Network Function ◽

Network Utilization ◽

Underlying Network ◽

Network Operations ◽

Network Technologies

AbstractWith the advent of 5G technology and an ever-increasing traffic demand, today Communication Service Providers (CSPs) experience a progressive congestion of their networks. The operational complexity, the use of manual configuration, the static nature of current technologies together with fast-changing traffic profiles lead to: inefficient network utilization, over-provisioning of resources and very high Capital Expenditures (CapEx) and Operational Expenses (OpEx). This situation is forcing the CSPs to change their underlying network technologies, and have started to look at new technological solutions that increase the level of programmability, control, and flexibility of configuration, while reducing the overall costs related to network operations. Software Define Networking (SDN), Network Function Virtualization (NFV) and Machine Learning (ML) are accepted as effective solutions to reduce CapEx and OpEx and to boost network innovation. This chapter summarizes the content of my Ph.D. thesis, by presenting new ML-based approaches in order to efficiently optimize resources in 5G metro-core SDN/NFV networks. The main goal is to provide the modern CSP with intelligent and dynamic network optimization tools in order to address the requirements of increasing traffic demand and 5G technology.

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