A Survey on SDN \& SDCN Traffic Measurement: Existing Approaches and Research Challenge

TL;DR

This survey reviews existing SDN and SDCN traffic measurement techniques, categorizes solutions based on network behavior, discusses challenges, and explores machine learning approaches to enhance security and performance.

Contribution

It provides a comprehensive categorization of SDN traffic measurement solutions, identifies key challenges, and highlights the potential of machine learning to improve SDN/SDCN security and efficiency.

Findings

Machine learning can enhance SDN traffic measurement performance.

Traffic measurement solutions are crucial for SDN security against threats.

Future research should focus on scalable and heterogeneous SDN/SDCN environments.

Abstract

Software Defined Network (SDN) is the next generation network that decouples the control plane from the data plane of forwarding devices by utilizing the OpenFlow protocol as a communication link between the data plane and the control plane. However, there are some security issues might be in actions on SDN that the attackers can take control over the SDN control plane. Thus, traffic measurement is a fundamental technique of protecting SDN against the high-security threats such as DDoS, heavy hitter, superspreader as well as live video calling, QoS control, high bandwidth requirement, resource management are also inevitable in SDN/Software Defined Cellular Network (SDCN). In such a scenario, we survey SDN traffic measurement solutions, in order to assess how these solutions can make a secured, efficient and robust SDN/SDCN architecture. In this paper, various types of SDN traffic measurement solutions have been categorized based on network applications behaviour. Furthermore, we find out the challenges related to SDN/SDCN traffic measurement and future scope of research, which will guide to design and develop more advanced traffic measurement solutions for a scalable, heterogeneous, hierarchical and widely deployed SDN/SDCN in future prospects. More in details, we list out kinds of practical machine learning (ML) approaches to analyze how we can make improvement in the traffic measurement performances. We conclude that using ML in SDN traffic measurement solutions will give benefit to get secured SDN/SDCN network in complementary ways.