NNIRSS: neural network-based intelligent routing scheme for SDN

  • Chuangchuang Zhang
  • Xingwei Wang
  • Fuliang Li
  • Min Huang
Original Article
  • 19 Downloads

Abstract

With the increasing diversification of network applications, SDN tends to be inefficient to satisfy the diversified application demands. Meanwhile, the continuous update of OpenFlow and flow table expansion causes the efficiency of routing and forwarding ability decreased as well as the storage space of ternary content addressable memory (TCAM) occupied by flow tables increased. In this paper, we present NNIRSS, a novel neural network (NN)-based intelligent routing scheme for SDN, which leverages the centralized controller to achieve transmission patterns of data flow by utilizing NN and replaces flow table with well-trained NN in the form of NN packet. The route of data flow can be predicted based on its application type to meet the quality of service requirements of network applications. Furthermore, we devise a radial basis function neural network-based intelligent routing mechanism. With combining APC-III and K-means algorithm, we propose APC-K-means algorithm to determine radial basis function centers. Finally, the simulation results demonstrate that our proposed NNIRSS is feasible and effective. It can reduce storage space of TCAM and routing time overhead as well as improve routing efficiency.

Keywords

SDN Intelligent routing RBFNN APC-K-means algorithm 

Notes

Acknowledgements

This work was supported by the Major International (Regional) Joint Research Project of NSFC under Grant No. 71620107003, the National Natural Science Foundation of China under Grant No. 61572123, the National Science Foundation for Distinguished Young Scholars of China under Grant No. 71325002, MoE and ChinaMobile Joint Research Fund under Grant No. MCM20160201, Program for Liaoning Innovative Research Term in University under Grant No. LT2016007, CERNET Innovation Project under Grant No. NGII20160126 and the Fundamental Research Funds for the Central Universities Project under Grant No. N150403007. We would like to thanks all referees for their invaluable insights and suggestions.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.College of Computer Science and EngineeringNortheastern UniversityShenyangChina
  2. 2.College of SoftwareNortheastern UniversityShenyangChina
  3. 3.College of Information Science and Engineering, State Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyangChina

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