A Machine Learning Based Elephant Flow Detection Method in Software-Defined Networks

• Main Authors: Huang, Yuan-Hao, 黃元顥
• Other Authors: Huang, Jiun-Long
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/47857729332487532623

碩士 === 國立交通大學 === 網路工程研究所 === 105 === The proposed software-defined networking makes the bandwidth used more effectively. So how to detect elephant flow efficient becomes an important issue. Most elephant flow detection in recent papers use posterior detection methods. These methods could not reroute before elephant flow generated. Other methods using previous prediction are often using IP address and port number as feature, but these methods are not accurate while detecting an unknown flow. Most of flows are mice flow, these methods would classify most elephant flows into mice flows. Therefore, we propose an elephant flow detection method based on machine learning. Without using IP address, we adopt the behavior of first few packet between client and server while establish the connection which called Application Round to predict elephant flow. Besides, we propose a two phase classification method. The classifiers are running in controller and switch. We also could update the training model and learn new features. The result shows that we can not only predict the elephant flow accurately but also have a good recall rate.