TY - JOUR
T1 - Flow-Aware Elephant Flow Detection for Software-Defined Networks
AU - Hamdan, Mosab
AU - Mohammed, Bushra
AU - Humayun, Usman
AU - Abdelaziz, Ahmed
AU - Khan, Suleman
AU - Ali, M Akhtar
AU - Imran, Muhammad
AU - Marsono., MN
PY - 2020/4/30
Y1 - 2020/4/30
N2 - 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.
AB - 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.
KW - Software-defined networking
KW - elephant flow detection
KW - flow classification
U2 - 10.1109/ACCESS.2020.2987977
DO - 10.1109/ACCESS.2020.2987977
M3 - Article
VL - 8
SP - 72585
EP - 72597
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
M1 - 9066961
ER -