Flow-Aware Elephant Flow Detection for Software-Defined Networks

Mosab Hamdan, Bushra Mohammed, Usman Humayun, Ahmed Abdelaziz, Suleman Khan, M Akhtar Ali, Muhammad Imran, MN Marsono.

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
28 Downloads (Pure)

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.

Original languageEnglish
Article number9066961
Pages (from-to)72585-72597
Number of pages13
JournalIEEE Access
Volume8
Early online date14 Apr 2020
DOIs
Publication statusPublished - 30 Apr 2020

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