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Elephant flow detection intelligence for software-defined networks: a survey on current techniques and future direction

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Abstract

Software-defined networking (SDN) is characterized by the separation of the packet forwarding plane from the network control plane. This separation offers an extensive view of the network’s state, enhancing network resilience and management. Network traffic classification can improve SDN control and resource provisioning, particularly for elephant flows (EFs) detection. Existing techniques for detecting EFs utilize preset thresholds and bandwidth that are inadequate for changing traffic concepts. Moreover, these techniques consume high data plane-controller bandwidth and have a high detection time. This research first describes the related management techniques in SDN. Then according to the detecting location, elephant flow detection approaches are classified into four kinds: host-based, switch-based, controller-based, and hybrid controller-switch-based detection. This research examined four types of detection approaches and concluded that host-based detection primarily relies on the flow statistics threshold. Such approaches frequently gather flow statistics by monitoring the socket buffer or via the hypervisor. In contrast, switch-based detection can leverage both the flow statistics threshold and flow characteristics. Controller-based detection techniques in SDN focus on extracting flow feature statistics at the controller level, aiming to reduce switch overhead while potentially increasing controller loads. Finally, hybrid controller-switch-based detection combines both routing aspects, offering fine-grained flow control. However it faces challenges in maintaining a balance in timeliness, accuracy, and cost. Furthermore, the survey incorporates recent SDN advancements such as machine learning-based methods, programmable switches, and real-world SDN applications in data centers, global content delivery networks, healthcare, and IoT. Finally, the article makes a comprehensive comparison and puts forward several points of future prediction in terms of elephant flow detection, taking into account recent advances in SDN research.

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Acknowledgements

The authors would like to acknowledge the support provided by the Interdisciplinary Research Center for Intelligent Secure Systems at King Fahd University of Petroleum and Minerals (KFUPM). Furthermore, the author Hashim Elshafie extends their appreciation to the Deanship of Scientific Research at King Khalid University for supporting this work through number RGP2/469/44. Moreover, Mohammed S. M. Gismalla expresses appreciation for the support received from the Center for Communication Systems and Sensing at King Fahd University of Petroleum and Minerals.

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Authors and Affiliations

  1. Interdisciplinary Research Center for Intelligent Secure Systems (IRC-ISS), KFUPM, Dhahran, Saudi Arabia

    Mosab Hamdan & Mustafa Ghaleb

  2. College of Computer Science, King Khalid University, Abha, Saudi Arabia

    Hashim Elshafie

  3. Faculty of Computing and Information Technology, King Saud University, Riyadh, Saudi Arabia

    Sayeed Salih

  4. Department of Information Technology, University of Khartoum, Khartoum, Sudan

    Samah Abdelsalam & Omayma Husain

  5. Interdisciplinary Research Center for Communication Systems and Sensing (IRC-CSS), KFUPM, Dhahran, Saudi Arabia

    Mohammed S. M. Gismalla

  6. Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia

    M. N. Marsono

  7. Department of Management Information System, College of Business Administration, University of Hail, Hail, Saudi Arabia

    Samah Abdelsalam

  8. Department of Information Technology, Ajman University, Ajman, UAE

    Omayma Husain

Authors
  1. Mosab Hamdan
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  2. Hashim Elshafie
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  3. Sayeed Salih
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  4. Samah Abdelsalam
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  6. Mohammed S. M. Gismalla
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  7. Mustafa Ghaleb
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  8. M. N. Marsono
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Correspondence to Mustafa Ghaleb.

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Hamdan, M., Elshafie, H., Salih, S. et al. Elephant flow detection intelligence for software-defined networks: a survey on current techniques and future direction. Evol. Intel. 17, 2125–2143 (2024). https://doi.org/10.1007/s12065-023-00902-7

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