Flow-Aware Elephant Flow Detection for Software-Defined Networks

Hamdan, Mosab, Mohammed, Bushra, Humayun, Usman, Abdelaziz, Ahmed, Khan, Suleman, Ali, Akhtar, Imran, Muhammad and Marsono., MN (2020) Flow-Aware Elephant Flow Detection for Software-Defined Networks. IEEE Access, 8. pp. 72585-72597. ISSN 2169-3536

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Official URL: https://doi.org/10.1109/access.2020.2987977


Software-defined networking (SDN) separates the network control plane from the packet forwarding plane, which provides comprehensive network-state visibility for better network management and resilience. Traffic classification, particularly for elephant flow detection, can lead to improved flow control and resource provisioning in SDN networks. Existing elephant flow detection techniques use pre-set thresholds that cannot scale with the changes in the traffic concept and distribution. This paper proposes a flow-aware elephant flow detection applied to SDN. The proposed technique employs two classifiers, each respectively on SDN switches and controller, to achieve accurate elephant flow detection efficiently. Moreover, this technique allows sharing the elephant flow classification tasks between the controller and switches. Hence, most mice flows can be filtered in the switches, thus avoiding the need to send large numbers of classification requests and signaling messages to the controller. Experimental findings reveal that the proposed technique outperforms contemporary methods in terms of the running time, accuracy, F-measure, and recall.

Item Type: Article
Uncontrolled Keywords: Software-defined networking, Flow classification, Elephant flow detection
Subjects: G400 Computer Science
G500 Information Systems
G600 Software Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Elena Carlaw
Date Deposited: 28 Apr 2020 14:53
Last Modified: 31 Jul 2021 11:47
URI: http://nrl.northumbria.ac.uk/id/eprint/42946

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