EffiEye: Application-Aware Large Flow Detection in Data Center
Binfeng Wang, Jinshu Su, Lin Chen, Jinsheng Deng, Long Zheng · 2017
With the rapid development of cloud computing, thousands of servers and various cloud applications are involved in data center. These changes result in more and more complex flows in data center, which motivates the need for faster, lower overhead, more scalable large flow detection technology. This paper firstly shows the shortcomings of the traditional large flow detection technologies. Then it proposes a new method named EffiEye, which efficiently realizes application-aware large flow detection in the controller. EffiEye mainly replies on two different mechanisms: one is the flow classification based on the pre-classification of cloud applications in App Info module, which can ensure the fast detecting speed, the other is the flow-stat triggering supported by OpenFlow 1.5, which can ensure the high detecting accuracy.