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. 2023 Dec 4;23(23):9608.
doi: 10.3390/s23239608.

An E2E Network Slicing Framework for Slice Creation and Deployment Using Machine Learning

Sujitha Venkatapathy  1 , Thiruvenkadam Srinivasan  2 , Han-Gue Jo  3 , In-Ho Ra  3
Affiliations

An E2E Network Slicing Framework for Slice Creation and Deployment Using Machine Learning

Sujitha Venkatapathy et al. Sensors (Basel). .

Abstract

Network slicing shows promise as a means to endow 5G networks with flexible and dynamic features. Network function virtualization (NFV) and software-defined networking (SDN) are the key methods for deploying network slicing, which will enable end-to-end (E2E) isolation services permitting each slice to be customized depending on service requirements. The goal of this investigation is to construct network slices through a machine learning algorithm and allocate resources for the newly created slices using dynamic programming in an efficient manner. A substrate network is constructed with a list of key performance indicators (KPIs) like CPU capacity, bandwidth, delay, link capacity, and security level. After that, network slices are produced by employing multi-layer perceptron (MLP) using the adaptive moment estimation (ADAM) optimization algorithm. For each requested service, the network slices are categorized as massive machine-type communications (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low-latency communications (uRLLC). After network slicing, resources are provided to the services that have been requested. In order to maximize the total user access rate and resource efficiency, Dijkstra's algorithm is adopted for resource allocation that determines the shortest path between nodes in the substrate network. The simulation output shows that the present model allocates optimum slices to the requested services with high resource efficiency and reduced total bandwidth utilization.

Keywords: 5G network; machine learning; network slicing; virtual network embedding; virtual network function.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
An overview of the E2E network slicing architecture.
Figure 2
Figure 2
Proposed model for slice creation and deployment.
Figure 3
Figure 3
(a) Structure of MLP. (b) Neurons in MLP.
Figure 4
Figure 4
Flowchart of resource allocation for one VNR.
Figure 5
Figure 5
Performance measurements: (a) precision, (b) recall, and (c) F1 Score.
Figure 6
Figure 6
Classification accuracy.
Figure 7
Figure 7
Resource efficiency with different SN nodes.
Figure 8
Figure 8
User access rate with different SN nodes.
Figure 9
Figure 9
Node utilization of proposed system.
Figure 10
Figure 10
Link utilization of proposed system.
Figure 11
Figure 11
Resource efficiency comparison with existing algorithms.
Figure 12
Figure 12
User access rate comparison with existing algorithms.
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References

    1. Babbar H., Rani S., AlZubi A.A., Singh A., Nasser N., Ali A. Role of Network Slicing in Software Defined Networking for 5G: Use Cases and Future Directions. IEEE Wirel. Commun. 2022;29:112–118. doi: 10.1109/MWC.001.2100318. - DOI
    1. Phyu H.P., Naboulsi D., Stanica R. Machine Learning in Network Slicing—A Survey. IEEE Access. 2023;11:39123–39153. doi: 10.1109/ACCESS.2023.3267985. - DOI
    1. Zhang S. An overview of network slicing for 5G. IEEE Wirel. Commun. 2019;26:111–117. doi: 10.1109/MWC.2019.1800234. - DOI
    1. Ordonez-Lucena J., Ameigeiras P., Lopez D., Ramos-Munoz J.J., Lorca J., Folgueira J. Network slicing for 5G with SDN/NFV: Concepts, architectures, and challenges. IEEE Commun. Mag. 2017;55:80–87. doi: 10.1109/MCOM.2017.1600935. - DOI
    1. Li Q., Wu G., Papathanassiou A., Mukherjee U. An end-to-end network slicing framework for 5G wireless communication systems. arXiv. 20161608.00572

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