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Software Defined Networking Flow Table Management of OpenFlow Switches Performance and Security Challenges: A Survey

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  • Babangida Isyaku

    (Department of Computer Science, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
    Department of Mathematics and Computer Science, Sule Lamido University, Kafin Hausa P.M.B 048, Jigawa State, Nigeria)

  • Mohd Soperi Mohd Zahid

    (Department of Computer and Information Science, Universiti Teknologi PETRONAS, Seri, Iskandar Perak 32610, Malaysia)

  • Maznah Bte Kamat

    (Department of Computer Science, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia)

  • Kamalrulnizam Abu Bakar

    (Department of Computer Science, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia)

  • Fuad A. Ghaleb

    (Department of Computer Science, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia)

Abstract

Software defined networking (SDN) is an emerging network paradigm that decouples the control plane from the data plane. The data plane is composed of forwarding elements called switches and the control plane is composed of controllers. SDN is gaining popularity from industry and academics due to its advantages such as centralized, flexible, and programmable network management. The increasing number of traffics due to the proliferation of the Internet of Thing (IoT) devices may result in two problems: (1) increased processing load of the controller, and (2) insufficient space in the switches’ flow table to accommodate the flow entries. These problems may cause undesired network behavior and unstable network performance, especially in large-scale networks. Many solutions have been proposed to improve the management of the flow table, reducing controller processing load, and mitigating security threats and vulnerabilities on the controllers and switches. This paper provides comprehensive surveys of existing schemes to ensure SDN meets the quality of service (QoS) demands of various applications and cloud services. Finally, potential future research directions are identified and discussed such as management of flow table using machine learning.

Suggested Citation

  • Babangida Isyaku & Mohd Soperi Mohd Zahid & Maznah Bte Kamat & Kamalrulnizam Abu Bakar & Fuad A. Ghaleb, 2020. "Software Defined Networking Flow Table Management of OpenFlow Switches Performance and Security Challenges: A Survey," Future Internet, MDPI, vol. 12(9), pages 1-30, August.
    • Handle: RePEc:gam:jftint:v:12:y:2020:i:9:p:147-:d:406476
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    References listed on IDEAS

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    1. Huseyin Polat & Onur Polat & Aydin Cetin, 2020. "Detecting DDoS Attacks in Software-Defined Networks Through Feature Selection Methods and Machine Learning Models," Sustainability, MDPI, vol. 12(3), pages 1-16, February.
    2. Lobna Dridi & Mohamed Faten Zhani, 2018. "A holistic approach to mitigating DoS attacks in SDN networks," International Journal of Network Management, John Wiley & Sons, vol. 28(1), January.
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    Cited by:

    1. Yazhi Liu & Jiye Zhang & Wei Li & Qianqian Wu & Pengmiao Li, 2021. "Load Balancing Oriented Predictive Routing Algorithm for Data Center Networks," Future Internet, MDPI, vol. 13(2), pages 1-13, February.
    2. Muhammed Nura Yusuf & Kamalrulnizam bin Abu Bakar & Babangida Isyaku & Ahmed Hamza Osman & Maged Nasser & Fatin A. Elhaj, 2023. "Adaptive Path Selection Algorithm with Flow Classification for Software-Defined Networks," Mathematics, MDPI, vol. 11(6), pages 1-24, March.

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