Universiti Teknologi Malaysia Institutional Repository

Flow-aware elephant flow detection for software-defined networks

Hamdan, Mosab and Mohammed, Bushra and Humayun, Usman and Abdelaziz, Ahmed and Khan, Suleman and Muhammad Imran, M. Akhtar Ali and Marsono, M. N. (2020) Flow-aware elephant flow detection for software-defined networks. IEEE Access, 8 . pp. 72585-72597. ISSN 21693536

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Official URL: http://dx.doi.org/10.1109/ACCESS.2020.2987977

Abstract

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:flow classification, Software-defined networking
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:86775
Deposited By: Widya Wahid
Deposited On:30 Sep 2020 09:08
Last Modified:30 Sep 2020 09:08

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