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An Elephant Flow Detection Method Based on Machine Learning

  • Kaihao Lou
  • Yongjian Yang
  • Chuncai WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11910)

Abstract

Software-Defined Networking (SDN) is regarded as the next generation network. Current network is difficult to be configured and managed, and SDN is proposed to change this situation, which makes it attract a lot of attention of the academia and industry. The detection of Elephant Flow is an important service of SDN, based on which we can achieve the management of the network traffic and implement services such as the load balancing of traffic, congestion avoidance and so on. This paper focuses on the iterative method to detect Elephant Flow. We propose a method which uses the random forest to learn the arguments produced in the iterative detection and to improve the accuracy and speed of the detection. The experiments show that our method can efficiently improve the accuracy and speed of the detection compared to other methods.

Keywords

Software-Defined Networking Elephant flow detection Machine learning Random forest 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyJilin UniversityChangchunChina
  2. 2.Changchun Polytechnic UniversityChangchunChina

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