A Survey on SDN and SDCN Traffic Measurement: Existing Approaches and Research Challenges

The Software-Defined Network (SDN) is a next-generation network that uses OpenFlow to decouple the control plane from the data plane of forwarding devices. Other protocols for southbound interfaces include ForCES and POF. However, some security issues might be in action on the SDN, so that attackers...
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Autor*in:	MD Samiul Islam [verfasserIn] Mohammed Al-Mukhtar [verfasserIn] MD Rahat Kader Khan [verfasserIn] Mojammel Hossain [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	SDN SDN traffic measurement SDN measurement SDCN

Übergeordnetes Werk:	In: Eng - MDPI AG, 2021, 4(2023), 2, Seite 1071-1115
Übergeordnetes Werk:	volume:4 ; year:2023 ; number:2 ; pages:1071-1115

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/eng4020063

Katalog-ID:	DOAJ094162727

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authorswithroles_txt_mv	MD Samiul Islam @@aut@@ Mohammed Al-Mukhtar @@aut@@ MD Rahat Kader Khan @@aut@@ Mojammel Hossain @@aut@@
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callnumber-first	T - Technology
author	MD Samiul Islam
spellingShingle	MD Samiul Islam misc TK1-9971 misc SDN misc SDN traffic measurement misc SDN measurement misc SDCN misc Electrical engineering. Electronics. Nuclear engineering A Survey on SDN and SDCN Traffic Measurement: Existing Approaches and Research Challenges
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topic_title	TK1-9971 A Survey on SDN and SDCN Traffic Measurement: Existing Approaches and Research Challenges SDN SDN traffic measurement SDN measurement SDCN
topic	misc TK1-9971 misc SDN misc SDN traffic measurement misc SDN measurement misc SDCN misc Electrical engineering. Electronics. Nuclear engineering
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title	A Survey on SDN and SDCN Traffic Measurement: Existing Approaches and Research Challenges
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title_sort	survey on sdn and sdcn traffic measurement: existing approaches and research challenges
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title_auth	A Survey on SDN and SDCN Traffic Measurement: Existing Approaches and Research Challenges
abstract	The Software-Defined Network (SDN) is a next-generation network that uses OpenFlow to decouple the control plane from the data plane of forwarding devices. Other protocols for southbound interfaces include ForCES and POF. However, some security issues might be in action on the SDN, so that attackers can take control of the SDN control plane. Since live video calling, QoS control, high bandwidth needs, and resource management are inevitable in any SDN/Software-Defined Cellular Network (SDCN), traffic monitoring is an integral approach for safeguarding against DDoS, heavy hitters, and superspreaders. In such a scenario, SDN traffic measurement comes into action. Thus, we survey SDN traffic measurement solutions to assess how these solutions can make a secure, efficient, and robust SDN/SDCN architecture. This research classifies SDN traffic measurement solutions according to network application behavior and compares several ML approaches. Furthermore, we find out the challenges related to SDN/SDCN traffic measurement and the future scope of research, which will guide the design and development of more advanced traffic measurement solutions for a scalable, heterogeneous, hierarchical, and widely deployed SDN/SDCN architecture. In more detail, we list different kinds of practical machine learning (ML) approaches to analyze how we can improve traffic measurement performances. We conclude that using ML in SDN traffic measurement solutions will help secure SDNs/SDCNs in complementary ways.
abstractGer	The Software-Defined Network (SDN) is a next-generation network that uses OpenFlow to decouple the control plane from the data plane of forwarding devices. Other protocols for southbound interfaces include ForCES and POF. However, some security issues might be in action on the SDN, so that attackers can take control of the SDN control plane. Since live video calling, QoS control, high bandwidth needs, and resource management are inevitable in any SDN/Software-Defined Cellular Network (SDCN), traffic monitoring is an integral approach for safeguarding against DDoS, heavy hitters, and superspreaders. In such a scenario, SDN traffic measurement comes into action. Thus, we survey SDN traffic measurement solutions to assess how these solutions can make a secure, efficient, and robust SDN/SDCN architecture. This research classifies SDN traffic measurement solutions according to network application behavior and compares several ML approaches. Furthermore, we find out the challenges related to SDN/SDCN traffic measurement and the future scope of research, which will guide the design and development of more advanced traffic measurement solutions for a scalable, heterogeneous, hierarchical, and widely deployed SDN/SDCN architecture. In more detail, we list different kinds of practical machine learning (ML) approaches to analyze how we can improve traffic measurement performances. We conclude that using ML in SDN traffic measurement solutions will help secure SDNs/SDCNs in complementary ways.
abstract_unstemmed	The Software-Defined Network (SDN) is a next-generation network that uses OpenFlow to decouple the control plane from the data plane of forwarding devices. Other protocols for southbound interfaces include ForCES and POF. However, some security issues might be in action on the SDN, so that attackers can take control of the SDN control plane. Since live video calling, QoS control, high bandwidth needs, and resource management are inevitable in any SDN/Software-Defined Cellular Network (SDCN), traffic monitoring is an integral approach for safeguarding against DDoS, heavy hitters, and superspreaders. In such a scenario, SDN traffic measurement comes into action. Thus, we survey SDN traffic measurement solutions to assess how these solutions can make a secure, efficient, and robust SDN/SDCN architecture. This research classifies SDN traffic measurement solutions according to network application behavior and compares several ML approaches. Furthermore, we find out the challenges related to SDN/SDCN traffic measurement and the future scope of research, which will guide the design and development of more advanced traffic measurement solutions for a scalable, heterogeneous, hierarchical, and widely deployed SDN/SDCN architecture. In more detail, we list different kinds of practical machine learning (ML) approaches to analyze how we can improve traffic measurement performances. We conclude that using ML in SDN traffic measurement solutions will help secure SDNs/SDCNs in complementary ways.
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title_short	A Survey on SDN and SDCN Traffic Measurement: Existing Approaches and Research Challenges
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