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

A. El-serwy, Aya; AbdElhalim, Eman; A. Mohamed, Mohamed

doi:10.21608/bfemu.2022.261455

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

Document Type : Research Studies

Authors

¹ Researcher of Electronics and Communications Engineering Department, Faculty of Engineering. Mansoura University, 35516 Mansoura City, Egypt

² Assistant Professor of Electronics and Communications Engineering Department, Faculty of Engineering. Mansoura University, 35516 Mansoura City, Egypt

³ Professor of Electronics and Communications Engineering Department, Faculty of Engineering. Mansoura University, 35516 Mansoura City, Egypt

10.21608/bfemu.2022.261455

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)

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