Flow-Aware Elephant Flow Detection for Software-Defined Networks

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

A comprehensive survey of load balancing techniques in software-defined network

A software-defined network (SDN) separates the network control plane from the data forwarding plane. SDN has shown significant benefits in many ways compared to conventional non-SDN networks. However, traffic distribution in SDN impacts efficiency and raises many other challenges. For instance, uneven load distribution in the SDN significantly impacts the network performance. Hence, several SDN load balancing (LB) techniques have been introduced to improve the efficiency of SDN. In this article, we provide a thematic taxonomy of LB in SDN, considering several parameters from the past technical studies such as the objectives of LB, data plane LB techniques, control plane LB techniques, other aspects of data plane/control plane LB as well as the performance metrics for LB techniques. Furthermore, useful insights on LB and a comparative analysis of various promising SDN LB techniques are also included in the survey. Finally, existing challenges and future direction on SDN LB techniques are highlighted