Regression Based Dynamic Elephant Flow Detection in Airborne Network

As an important part of the spatial information network, airborne network (AN), which connects air platforms with upper satellites and ground devices, has been increasingly important right now. Due to the heavy-tailed distribution of network traffic, elephant flow detection is usually used to catch and control the key part of network traffic with low costs, which is a practical strategy to strengthen network management and improve network performance. In this paper, we consider the problem of dynamic threshold elephant flow detection in AN, and an intelligent method based on regression with pre-classification is proposed to adapt to the limited and dynamically changing bandwidth. The filtering mechanism with waiting-window is used firstly to filter out parts of small flows to decrease the detection cost. Then, the pre-classification is used to divide the range to be predicted and the flow size regression can be carried out in a compressed range, which makes the results more accurate. Finally, the predicted size is compared with the specific detection threshold related to the specific moment, and the elephant flow is identified. Numerical experiments demonstrate that the proposed method has a better adaptability to dynamic threshold and the performance is much better.