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Enhancing Service Classification for Network Slicing in 5G Using Machine Learning Algorithms

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New Trends in Information and Communications Technology Applications (NTICT 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1764))

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Abstract

In a virtualization aspect, Network Function Virtualization (NFV) has a role in implementing network slicing. Using NFV to slice the network, make the network more flexible, but very complicated in term of management. A slice is a set of services that the network needs based on the user requirements. Moreover, each slice has a set of services called sub-slice, or one type of service. This research aims to improve the availability and scalability of the services in network slicing by managing the performance of the inter/intra slice in real-time. Also, this research will enhance the Quality of Service (QoS) for the network resources and services and the Quality of Experience (QoE) for the users within the slice when we applied machine learning algorithms to classify and predicate accurate service to the user.

With this research, we implemented the slices based on the principles of NFV to deliver flexibility in the 5G network by creating multiple slices on top of the physical network. When the implementation of the prototype is completed, traffic generated tool was used to send traffic over the slices. After data collection, we classified different services using machine learning algorithms. The optimizable tree model had almost high accuracy among other algorithms which was 99.3%.

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Author information

Authors and Affiliations

  1. Centre for Smart and Advanced Technologies (CAST), University of Northampton, Northampton, UK

    Noor Abdalkarem Mohammedali, Triantafyllos Kanakis & Michael Opoku Agyeman

  2. School of Computing and Engineering, University of Gloucestershire, Gloucestershire, UK

    Ali Al-Sherbaz

  3. College of Information Technology, University of Babylon, Babylon, Iraq

    Saad Talib Hasson

Authors
  1. Noor Abdalkarem Mohammedali
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  2. Triantafyllos Kanakis
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  3. Ali Al-Sherbaz
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  4. Michael Opoku Agyeman
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  5. Saad Talib Hasson
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Corresponding author

Correspondence to Noor Abdalkarem Mohammedali .

Editor information

Editors and Affiliations

  1. University of Information Technology and Communications, Baghdad, Iraq

    Abbas M. Al-Bakry

  2. University of Information Technology and Communications, Baghdad, Iraq

    Safaa O. Al-Mamory

  3. University of Information Technology and Communications, Baghdad, Iraq

    Mouayad A. Sahib

  4. University of Information Technology and Communications, Baghdad, Iraq

    Loay E. George

  5. University of Information Technology and Communications, Baghdad, Iraq

    Jaafar A. Aldhaibani

  6. University of Information Technology and Communications, Baghdad, Iraq

    Haitham S. Hasan

  7. Open University of Tanzania, Dar es Salaam, Tanzania

    George S. Oreku

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Mohammedali, N.A., Kanakis, T., Al-Sherbaz, A., Agyeman, M.O., Hasson, S.T. (2023). Enhancing Service Classification for Network Slicing in 5G Using Machine Learning Algorithms. In: Al-Bakry, A.M., et al. New Trends in Information and Communications Technology Applications. NTICT 2022. Communications in Computer and Information Science, vol 1764. Springer, Cham. https://doi.org/10.1007/978-3-031-35442-7_2

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  • DOI: https://doi.org/10.1007/978-3-031-35442-7_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35441-0

  • Online ISBN: 978-3-031-35442-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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