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MBitCuts: optimal bit-level cutting in geometric space packet classification

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Abstract

Packet classification is one of the main tasks of modern network processors. A challenging problem in this regard is to use an algorithm that can classify packets at a high speed and with a reasonably low memory consumption. Traditional decision tree-based algorithms do not satisfy both requirements. BitCuts algorithm, which has been recently proposed to increase search speed in tree algorithms, is not an exception. We propose MBitCuts as a novel solution that reduces both memory usage and memory access in this algorithm by changing the method of bit selection in the cutting of the geometric subspace model of each tree node. The evaluation results show that the average number of memory accesses and the average memory usage in the proposed method have been reduced by 39% and 87%, respectively. Also, MBitCuts outperforms state-of-the-art tree-based algorithms by simultaneously achieving the best classification speed and the least memory consumption.

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References

  1. Hung S-C, Iliev N, Vamanan B, Trivedi AR (2019) Self-organizing maps-based flexible and high-speed packet classification in software defined networking. In: 2019 32nd International Conference on VLSI Design and 2019 18th International Conference on Embedded Systems (VLSID). IEEE, pp 545–546

  2. Dong X, Qian M, Jiang R (2018) Packet classification based on the decision tree with information entropy. J Supercomput. https://doi.org/10.1007/s11227-017-2227-z

    Article  Google Scholar 

  3. Indira B, Valarmathi K, Devaraj D (2019) An approach to enhance packet classification performance of software-defined network using deep learning. Soft Comput 23(18):8609–8619

    Article  Google Scholar 

  4. Liu Z, Sun S, Zhu H, Gao J, Li J (2017) BitCuts: a fast packet classification algorithm using bit-level cutting. Comput Commun 109:38–52

    Article  Google Scholar 

  5. Norige E, Liu AX, Torng E, Torng E, Norige E, Liu AX (2018) A ternary unification framework for optimizing TCAM-based packet classification systems. IEEE/ACM Trans Netw (TON) 26(2):657–670

    Article  Google Scholar 

  6. Shen R, Li X, Li H (2014) A space-and power-efficient multi-match packet classification technique combining TCAMs and SRAMs. J Supercomput 69(2):673–692

    Article  Google Scholar 

  7. Abbasi M, Rafiee M (2019) A calibrated asymptotic framework for analyzing packet classification algorithms on GPUs. J Supercomput 75(10):6574–6611. https://doi.org/10.1007/s11227-019-02861-2

    Article  Google Scholar 

  8. Taylor DE (2005) Survey and taxonomy of packet classification techniques. ACM Comput Surv (CSUR) 37(3):238–275

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Song H, Turner JS (2013) ABC: adaptive binary cuttings for multidimensional packet classification. IEEE/ACM Trans Netw 21(1):98–109

    Article  Google Scholar 

  11. Gupta P, McKeown N (2000) Classifying packets with hierarchical intelligent cuttings. IEEE Micro 20(1):34–41

    Article  Google Scholar 

  12. Kitamura Y, Iwata A, Mohri M, Shiraishi Y (2015) Storage-efficient tree structure with level-ordered unary degree sequence for packet classification. In: 2015 Third international symposium on computing and networking (CANDAR), 8–11 Dec 2015, pp 487–490. https://doi.org/10.1109/candar.2015.86

  13. Wang P, Chan C, Lee C, Chang H (2006) Scalable packet classification for enabling internet differentiated services. IEEE Trans Multimed 8(6):1239–1249. https://doi.org/10.1109/TMM.2006.884610

    Article  Google Scholar 

  14. Singh S, Baboescu F, Varghese G, Wang J (2003) Packet classification using multidimensional cutting. In: Proceedings of the 2003 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications. ACM, pp 213–224

  15. Vamanan B, Voskuilen G, Vijaykumar T (2010) EffiCuts: optimizing packet classification for memory and throughput. In: ACM SIGCOMM computer communication review, vol 4. ACM, pp 207–218

  16. Han W, Yi P, Tian L (2014) Prefix cuttings for packet classification with fast updates. KSII Trans Internet Inf Syst 8:1442–1462. https://doi.org/10.3837/tiis.2014.04.016

    Article  Google Scholar 

  17. Qi Y, Xu L, Yang B, Xue Y, Li J (2009) Packet classification algorithms: from theory to practice. In: INFOCOM 2009. IEEE, pp 648–656

  18. Li W, Li X, Li H, Xie G (2018) CutSplit: a decision-tree combining cutting and splitting for scalable packet classification. https://doi.org/10.1109/infocom.2018.8485947

  19. Hilewitz Y, Lee RB (2008) A new basis for shifters in general-purpose processors for existing and advanced bit manipulations. IEEE Trans Comput 58(8):1035–1048

    Article  MathSciNet  Google Scholar 

  20. Taylor DE, Turner JS (2005) Classbench: a packet classification benchmark. In: INFOCOM 2005. 24th Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings IEEE. IEEE, pp 2068–2079

  21. Lim H, Choe Y, Shim M, Lee J (2014) A quad-trie conditionally merged with a decision tree for packet classification. IEEE Commun Lett 18(4):676–679

    Article  Google Scholar 

  22. Yu W, Sivakumar S, Pao D (2019) Pseudo-TCAM: SRAM-based architecture for packet classification in one memory access. IEEE Netw Lett 1:89–92

    Article  Google Scholar 

  23. Abbasi M, Tahouri R, Rafiee M (2019) Enhancing the performance of the aggregated bit vector algorithm in network packet classification using GPU. PeerJ Comput Sci 5:e185

    Article  Google Scholar 

  24. Shen T, Zhang D-F, Xie G-G, Zhang X-Y (2018) Optimizing multi-dimensional packet classification for multi-core systems. J Comput Sci Technol 33(5):1056–1071

    Article  MathSciNet  Google Scholar 

  25. Li X, Shao Y (2018) Memory compression for recursive flow classification algorithm in network packet processing devices. In: 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, pp 1502–1505

  26. Inoue T, Mano T, Mizutani K, Minato S-i, Akashi O (2018) Fast packet classification algorithm for network-wide forwarding behaviors. Comput Commun 116:101–117

    Article  Google Scholar 

  27. Harada T, Tanaka K, Mikawa K (2018) Acceleration of packet classification via inclusive rules. In: 2018 IEEE Conference on Communications and Network Security (CNS). IEEE, pp 1–2

  28. Lee J, Byun H, Mun JH, Lim H (2017) Utilizing 2-D leaf-pushing for packet classification. Comput Commun 103:116–129. https://doi.org/10.1016/j.comcom.2017.02.005

    Article  Google Scholar 

  29. Hsieh C-L, Weng N (2015) Scalable many-field packet classification using multidimensional-cutting via selective bit-concatenation. In: Proceedings of the eleventh ACM/IEEE symposium on architectures for networking and communications systems. IEEE Computer Society, pp 187–188

  30. Perez KG, Yang X, Scott-Hayward S, Sezer S (2014) Optimized packet classification for software-defined networking. In: Communications (ICC), 2014 IEEE International Conference on. IEEE, pp 859–864

  31. Lim H, Lee N, Jin G, Lee J, Choi Y, Yim C (2014) Boundary cutting for packet classification. IEEE/ACM Trans Netw 22(2):443–456

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Engineering Faculty, Bu-Ali Sina University, Hamedan, Iran

    Mahdi Abbasi, Saeideh Vesaghati Fazel & Milad Rafiee

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  1. Mahdi Abbasi
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  2. Saeideh Vesaghati Fazel
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  3. Milad Rafiee
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Correspondence to Mahdi Abbasi.

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Abbasi, M., Vesaghati Fazel, S. & Rafiee, M. MBitCuts: optimal bit-level cutting in geometric space packet classification. J Supercomput 76, 3105–3128 (2020). https://doi.org/10.1007/s11227-019-03090-3

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