An adaptive approach for elephant flow detection with the rapidly changing traffic in data center network

Software‐defined network, which separates control plane from the underlying physical devices, has the advantages of global visibility and high flexibility. Among the most typical applications in software‐defined network, there is significant interest on classifying flows, especially for elephant flow detection. Previous studies show that detecting and rerouting elephant flows (flows that transfer significant amount of data) effectively can lead to a 113% improvement in aggregate throughput compared with the traditional routing. However, the threshold of the existing detection approach was preconfigured without the consideration of the rapidly changing traffic in data center networks. This phenomenon could cause high detection error rate. To address this problem, we propose an adaptive approach for elephant flow detection, which could efficiently identify elephant flows with low latency and low overhead. Particularly, to meet the demands of the traffic characteristics in data center networks, dynamical traffic learning algorithm is adopted to configure the threshold value real timely and dynamically. Numerical results and experimental tests show that the mean error rate of detection is only 4.61% and the maximum number of packet‐in messages is minimum compared to other methods.