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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Eng |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4117/4/2/63 |
| _version_ | 1797594986151149568 |
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| author | MD Samiul Islam Mohammed Al-Mukhtar MD Rahat Kader Khan Mojammel Hossain |
| author_facet | MD Samiul Islam Mohammed Al-Mukhtar MD Rahat Kader Khan Mojammel Hossain |
| author_sort | MD Samiul Islam |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T02:30:30Z |
| format | Article |
| id | doaj.art-1b29fc0295054f909a3101335ec8585d |
| institution | Directory Open Access Journal |
| issn | 2673-4117 |
| language | English |
| last_indexed | 2024-03-11T02:30:30Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Eng |
| spelling | doaj.art-1b29fc0295054f909a3101335ec8585d2023-11-18T10:14:50ZengMDPI AGEng2673-41172023-04-01421071111510.3390/eng4020063A Survey on SDN and SDCN Traffic Measurement: Existing Approaches and Research ChallengesMD Samiul Islam0Mohammed Al-Mukhtar1MD Rahat Kader Khan2Mojammel Hossain3Computing Science, University of Alberta, Edmonton, AB T5H 2T5, CanadaComputer Center, University of Baghdad, Baghdad 10071, IraqComputer Science & Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, BangladeshInformation Engineering, University of Padova, 35100 Padova, ItalyThe 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.https://www.mdpi.com/2673-4117/4/2/63SDNSDN traffic measurementSDN measurementSDCN |
| spellingShingle | MD Samiul Islam Mohammed Al-Mukhtar MD Rahat Kader Khan Mojammel Hossain A Survey on SDN and SDCN Traffic Measurement: Existing Approaches and Research Challenges Eng SDN SDN traffic measurement SDN measurement SDCN |
| title | A Survey on SDN and SDCN Traffic Measurement: Existing Approaches and Research Challenges |
| title_full | A Survey on SDN and SDCN Traffic Measurement: Existing Approaches and Research Challenges |
| title_fullStr | A Survey on SDN and SDCN Traffic Measurement: Existing Approaches and Research Challenges |
| title_full_unstemmed | A Survey on SDN and SDCN Traffic Measurement: Existing Approaches and Research Challenges |
| title_short | A Survey on SDN and SDCN Traffic Measurement: Existing Approaches and Research Challenges |
| title_sort | survey on sdn and sdcn traffic measurement existing approaches and research challenges |
| topic | SDN SDN traffic measurement SDN measurement SDCN |
| url | https://www.mdpi.com/2673-4117/4/2/63 |
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