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261455

Network Slicing Based on Real-Time Traffic Classification in Software Defined Network (SDN) using Machine Learning

Article

Last updated: 04 Jan 2025

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Subjects

-

Tags

Electronics and Communications Engineering

Abstract

With the increase in smart devices, performance of traditional networks is limited by this huge amount of generated traffic flows. A scalable and programmable networking solution can be achieved in software defined networks (SDNs) through the separation between the control plane and the data plane. This advantage can allow machine learning (ML) applications to control and automate networks. Concurrently, network slicing (NS) is a promising technology. It is necessary to meet the variety of service needs and requirements. It provides the network as a service (Naas). So, combining NS and ML in SDNs can achieve good network resources management. This paper focuses on applying real-time network traffic analysis to assign each traffic to its suitable network slice according to traffic flows classification. In the proposed model, robust scale is used to scale the features instead of max/min normalization. Also, the k-means clustering algorithm is used to separate the dataset into the optimum number of different clusters (slices). Five different supervised models are applied to achieve high classification accuracy. The highest accuracy that can be obtained from feed-forward artificial neural network is (98.2%), while support vector machine (SVM) with linear function gives an accuracy of (96.7%).  The challenges faced are collecting data from SDN's controller to apply real-time traffic flow classification, which is a primary step to assign each flow to its suitable network slice (Bandwidth)

DOI

10.21608/bfemu.2022.261455

Keywords

Network slicing, Software defined networks (SDNs), Traffic classification, Machine Learning

Authors

First Name

Aya

Last Name

A. El-serwy

MiddleName

-

Affiliation

Researcher of Electronics and Communications Engineering Department, Faculty of Engineering. Mansoura University, 35516 Mansoura City, Egypt

Email

ayaelserwy@mans.edu.eg

City

Mansoura

Orcid

0000-0002-3344-0649

First Name

Eman

Last Name

AbdElhalim

MiddleName

-

Affiliation

Assistant Professor of Electronics and Communications Engineering Department, Faculty of Engineering. Mansoura University, 35516 Mansoura City, Egypt

Email

eman_haleim@mans.edu.eg

City

Mansoura

Orcid

0000-0003-4801-9862

First Name

Mohamed

Last Name

A. Mohamed

MiddleName

-

Affiliation

Professor of Electronics and Communications Engineering Department, Faculty of Engineering. Mansoura University, 35516 Mansoura City, Egypt

Email

mazim12@mans.edu.eg

City

-

Orcid

0000-0003-1899-3621

Volume

47

Article Issue

3

Related Issue

34982

Issue Date

2022-06-01

Receive Date

2022-04-04

Publish Date

2022-09-27

Page Start

1

Page End

10

Print ISSN

1110-0923

Online ISSN

2735-4202

Article File

BFEMU_Volume 47_Issue 3_Pages 1-10.pdf

PDF . 2.0MB

Link

https://bfemu.journals.ekb.eg/article_261455.html

Detail API

https://bfemu.journals.ekb.eg/service?article_code=261455

Order

9

Type

Research Studies

Type Code

1,205

Publication Type

Journal

Publication Title

MEJ. Mansoura Engineering Journal

Publication Link

https://bfemu.journals.ekb.eg/

MainTitle

Network Slicing Based on Real-Time Traffic Classification in Software Defined Network (SDN) using Machine Learning

Details

Type

Article

Created At

22 Jan 2023