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Packet classification based on the decision tree with information entropy

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Abstract

Packet classification is indispensable for the next-generation routers targeting at the complete integration of advanced networking capabilities, which include differentiated services, memory access control, policy routing, and traffic billing. The classification method based on decision tree is advantageous in its structure and high efficiency, so it is suitable for real-time packet classification. A heuristic method is proposed based on the information entropy to build the decision tree more balanced considering the time complexity and the space complexity. It is suitable to solve rule subset uneven phenomenon and meets the requirement of big data with diverse data formats. The simulation results show that the algorithm can classify the packets quickly compared with previously described algorithms and has relatively small storage requirements.

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References

  1. Erdem O (2016) Pipelined hierarchical architecture for high performance packet classification. Comput Netw 103:143–164

    Article  Google Scholar 

  2. Al-Nejadi AYD, Husin NS (2015) Survey on multi field packet classification techniques. Res J Recent Sci 4(2):98–106

    Google Scholar 

  3. Lim H, Lee N, Jin G et al (2014) Boundary cutting for packet classification. IEEE/ACM Trans Netw 22(2):443–456

    Article  Google Scholar 

  4. Varvello M, Laufer R, Zhang F et al (2014) Multi-layer packet classification with graphics processing units. In: Conference on Emerging Network Experiment and Technology, pp 109–120

  5. Qu YR, Prasanna VK (2016) High-performance and dynamically updatable packet classification engine on FPGA. IEEE Trans Parallel Distrib Syst 27(1):197–209

    Article  Google Scholar 

  6. Chang Y, Chien S, Lin S, Hsieh S (2013) Efficient gray-code-based range encoding schemes for packet classification in TCAM. IEEE/ACM Trans Netw 21:1201–1214

    Article  Google Scholar 

  7. Panigrahy R, Sharma S, (2002) Reducing TCAM power consumption and increasing throughput. In: 10th Symposium on High Performance Interconnects, pp 107–112

  8. Shah D, Gupta P (2001) Fast updating algorithms for TCAM. Micro IEEE 21:36–47

    Article  Google Scholar 

  9. Jiang W, Prasanna VK (2012) Scalable packet classification on FPGA. IEEE Trans Very Large Scale Integr VLSI Syst 20:1668–1680

    Article  Google Scholar 

  10. Fong J, Wang X, Qi Y, Li J, Jiang W (2012) Para split: a scalable architecture on FPGA for terabit packet classification. In: IEEE 20th Annual Symposium on High-Performance Interconnects, pp 1–8

  11. Kohler E, Morris R, Chen B, Jannotti J, Kaashoek MF (2000) The click modular router. ACM Trans Comput Syst 18:263–297

    Article  Google Scholar 

  12. Srinivasan V, Varghese G, Suri S, Waldvagel M (1999) Fast and scalable layer four switching. In: Proceedings of ACM SIGCOMM’98, pp 191–202

    Article  Google Scholar 

  13. Merit Network Routing Assets Database. http://www.radb.net

  14. Buddhikot MM, Suri S, Waldvogel M (1999) Space decomposition techniques for fast layer-4 switching. In: Proceedings of Conference on the Protocols for High Speed Networks, Kluwer Academic Publishers, Salem, MA, USA, pp 25–41

    Chapter  Google Scholar 

  15. Gupta P, McKeown N (1999) Packet classification on multiple fields. In: Proceedings of ACM SIGCOMM’99, pp 147–160

    Article  Google Scholar 

  16. Gupta P, McKeown N (2001) Algorithms for packet classification. IEEE Network 15(2):24–32

    Article  Google Scholar 

  17. Gupta P, McKeown N (1998) Packet classification using hierarchical intelligent cuttings. In: Proceedings of the ACM Sigcomm’98, pp 34–41

  18. Xu, J, Singhal M, Joanne D (2000) A novel cache architecture to support layer-four packet classification at memory access speeds. In: International Conference on Computer Communications, pp 1445–1454

  19. Woo TYC (2000) A modular approach to packet classification: algorithms and results. Proc INFOCOM 3:1213–1222

    Google Scholar 

  20. Baboescu F, Varghese G (2001) Scalable packet classification. In: ACM SIGCOMM’01, San Diego, California, USA, pp 199–210

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Nos. 61763048, 61263022, 61303234), Science and Technology Foundation of Yunan Province (No. 2017FB095), the 18th Yunnan Young and Middle-aged Academic and Technical Leaders Reserve Personnel Training Program (No. 2015HB038), the key research project of natural science of Anhui Provincial Department of Education (KJ2017A354). The authors would like to thank the anonymous reviewers and the editors for their helpful suggestions and comments.

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Authors and Affiliations

  1. School of Computer and Information, Anqing Normal University, Anqing, Anhui, 246011, China

    Xiaoming Dong & Meng Qian

  2. School of Information, Yunnan University of Finance and Economics, Kunming, 650221, China

    Rong Jiang

Authors
  1. Xiaoming Dong
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  2. Meng Qian
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  3. Rong Jiang
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Contributions

XD and RJ designed the experiments and wrote the paper; MQ contributed the simulation experiments; All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Rong Jiang.

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Dong, X., Qian, M. & Jiang, R. Packet classification based on the decision tree with information entropy. J Supercomput 76, 4117–4131 (2020). https://doi.org/10.1007/s11227-017-2227-z

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  • DOI: https://doi.org/10.1007/s11227-017-2227-z

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