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Application-aware QoS routing in SDNs using machine learning techniques

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Abstract

Software Defined Networking has become an efficient and promising means for overcoming the limitations of traditional networks, e.g., by guaranteeing the corresponding Quality of Service (QoS) of various applications. Compared with the inherent distributed characteristics of the traditional network, SDN is logically centralized and can utilize machine learning techniques to keep track of transmission requirements of each application. In this research, we first develop an efficient data dimension reduction approach by considering the correlation coefficients between data items. We classify the traffic data into distinguished categories based on the QoS requirements by a supervised machine learning method. Then, we propose a QoS Aware Routing (QAR) algorithm according to the QoS requirements of each application that finds a path with either the minimum average link occupied times or the maximum average path residual capacity. The accuracy of machine learning model shows that our proposed dimension reduction approach is more effective than other data preprocessing methods, and the results of blocking probability indicate that our QAR algorithm outperforms significantly previous algorithms.

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References

  1. Optimization G (2016) Gurobi optimizer[J]. https://www.gurobi.com

  2. Rojas JS (2017) Ip network traffic flows labeled with 75 apps. Kaggle. Retrieved from https://www.kaggle.com/jsrojas/ip-network-traffic-flows-labeled-with-87-apps

  3. Akin E, Korkmaz T (2019) Comparison of routing algorithms with static and dynamic link cost in SDN. In 16th IEEE Annual Consumer Communications & Networking Conference (CCNC) pp. 1–8

  4. Alam F, Katib I, Alzahrani AS (2013) New networking era: Software defined networking. Int J Adv Res Comput Sci Softw 3(11):349–353

    Google Scholar 

  5. AlGhadhban A, Shihada B (2018) Flight: A fast and lightweight elephant-flow detection mechanism. In 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS) pp. 1537–1538

  6. Amaral P, Dinis J, Pinto P, Bernardo L, Tavares J, Mamede HS (2016) Machine learning in software defined networks: data collection and traffic classification. In Proc 24th Int Conf Network Protocols (ICNP) pp. 1–5

  7. Astuto B, Mendonca M, Nguyen Z, Obraczka K, Turletti T (2014) A survey of software-defined networking: Past, present, and future of programmable networks. IEEE Commun Surv Tutorials 16(3):1617–1634

    Article  Google Scholar 

  8. Becker N, Werft W, Toedt G, Lichter P, Benner A (2009) Penalizedsvm: A r-package for feature selection svm classification. Bioinformatics 25(13):1711–1712

    Article  Google Scholar 

  9. Biau G, Scornet E (2016) A random forest guided tour. Test 25(2):197–227

    Article  MathSciNet  Google Scholar 

  10. Callado AC, Kamienski CA, Szabó G, Gero BP, Kelner J, Fernandes SF, Sadok DFH (2009) A survey on Internet traffic identification. IEEE Commun Surv Tutorials 11(3):37–52

    Article  Google Scholar 

  11. Caruana R, Niculescu-Mizil A (2006) An empirical comparison of supervised learning algorithms. In Proceedings of the 23rd International Conference on Machine learning ACM pp. 161–168

  12. Chen Y, Farley T, Ye N (2004) QoS requirements of network applications on the internet. Inf Knowl Syst Manag 4(1):55–76

    Google Scholar 

  13. Chhabra A, Kiran M (2017) Classifying elephant and mice flows in high-speed scientific networks

  14. Christiansen B (2005) The shortcomings of nonlinear principal component analysis in identifying circulation regimes. J Clim 18:4814–4823

    Article  Google Scholar 

  15. Conti M, Gregori E, Panzieri F (2000) Load distribution among replicated web servers: A qos-based approach. ACM SIGMETRICS Performance Evaluation Review 27(4):12–19

    Article  Google Scholar 

  16. Da Silva AS, Machado CC, Bisol RV, Granville LZ, Schaeffer-Filho A (2015) Identification and selection of flow features for accurate traffic classification in SDN. In Proc IEEE 14th Int Symp Network Computing and Applications (NCA) IEEE pp. 134–141

  17. Erman J, Arlitt M, Mahanti A (2006) Traffic classification using clustering algorithms. In Proceedings of the 2006 SIGCOMM workshop on Mining network data pp. 281–286

  18. Erman J, Mahanti A, Arlitt M, Cohen I, Williamson C (2007) Semi-supervised network traffic classification. In Proceedings of the 2007 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer ystems pp. 369–370

  19. Ersoz D, Yousif MS, Das CR (2007) Characterizing network traffic in a cluster-based, multi-tier data center. In 27th International Conference on Distributed Computing Systems (ICDCS’07) IEEE pp. 59

  20. Fonti V, Belitser E (2017) Feature selection using lasso. VU Amsterdam Research Paper in Business Analytics 30:1–25

    Google Scholar 

  21. Jarschel M, Zinner T, Höhn T, Tran-Gia P (2013) On the accuracy of leveraging sdn for passive network measurements. In 2013 Australasian Telecommunication Networks and Applications Conference (ATNAC) IEEE pp. 41–46

  22. Jolliffe I (2002) Principal Component Analysis, 2nd ed. Springer

  23. Karakus M, Durresi A (2017) Quality of service (QoS) in software defined networking (SDN): A survey. J Netw Comput Appl 80:200–218

    Article  Google Scholar 

  24. Kotsiantis SB, Zaharakis I, Pintelas P (2007) Supervised machine learning: A review of classification techniques. Emerg Artif Intell Appl Comput Eng 160:3–24

    Google Scholar 

  25. Layeghy S, Pakzad F, Portmann M (2016) Scor: Software-defined constrained optimal routing platform for sdn. arXiv preprint arXiv:1607.03243

  26. Li C-Y, Li G, Wai P-KA, Li VO (2004) A wavelength-switched time-slot routing scheme for wavelength-routed networks. In 2004 IEEE International Conference on Communications (IEEE Cat. No. 04CH37577) 3:1689–1693

  27. Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:11–26

    Article  Google Scholar 

  28. McKeown N, Anderson T, Balakrishnan H, Parulkar G, Peterson L, Rexford J, Shenker S, Turner J (2008) Openflow: enabling innovation in campus networks. ACM SIGCOMM Computer Communication Review 38(2):69–74

    Article  Google Scholar 

  29. Menze BH, Kelm BM, Masuch R, Himmelreich U, Bachert P, Petrich W, Hamprecht FA (2009) A comparison of random forest and its gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinformatics 10(1):1–16

    Article  Google Scholar 

  30. Mwangi B, Tian TS, Soares JC (2014) A review of feature reduction techniques in neuroimaging. Neuroinformatics 12(2):229–244

    Article  Google Scholar 

  31. Neums L, Meier R, Koestler DC, Thompson JA (2019) Improving survival prediction using a novel feature selection and feature reduction framework based on the integration of clinical and molecular data. In PACIFIC SYMPOSIUM ON BIOCOMPUTING 2020. World Scientific, pp. 415–426

  32. Patle A, Chouhan DS (2013) Svm kernel functions for classification. In 2013 International Conference on Advances in Technology and Engineering (ICATE) IEEE pp. 1–9

  33. Pua Y-H, Kang H, Thumboo J, Clark RA, Chew ES-X, Poon CL-L, Chong H-C, Yeo S-J (2019) Machine learning methods are comparable to logistic regression techniques in predicting severe walking limitation following total knee arthroplasty. Knee Surgery, Sports Traumatology, Arthroscopy pp. 1–10

  34. Ripley BD (2007) Pattern recognition and neural networks. Cambridge University Press

  35. Rish I et al (2001) An empirical study of the naive bayes classifier. In IJCAI 2001 workshop on empirical methods in artificial intelligence 3:41–46

  36. Shlens J (2014) A tutorial on principal component analysis. arXiv preprint arXiv:1404.1100

  37. Sylvester EV, Bentzen P, Bradbury IR, Clément M, Pearce J, Horne J, Beiko RG (2018) Applications of random forest feature selection for fine-scale genetic population assignment. Evol Appl 11(2):153–165

    Article  Google Scholar 

  38. Tibshirani RJ et al (2013) The lasso problem and uniqueness. Electron J Stat 7:1456–1490

    Article  MathSciNet  Google Scholar 

  39. Velasco L, Jirattigalachote A, Ruiz M, Monti P, Wosinska L, Junyent G (2012) Statistical approach for fast impairment-aware provisioning in dynamic all-optical networks. J Opt Commun Networking 4(2):130–141

    Article  Google Scholar 

  40. Vu HD, But J (2015) How rtt between the control and data plane on a sdn network impacts on the perceived performance. In 2015 International Telecommunication Networks and Applications Conference (ITNAC) IEEE pp. 179–184

  41. Wang Z, Crowcroft J (1996) Quality-of-service routing for supporting multimedia applications. IEEE J Sel Areas Commun 14(7):1228–1234

    Article  Google Scholar 

  42. Wei JY, McFarland RI (2000) Just-in-time signaling for wdm optical burst switching networks. J Lightwave Technol 18(12):2019–2037

    Article  Google Scholar 

  43. Xiao P, Liu N, Li Y, Lu Y, Tang X-J, Wang H-W, Li M-X (2016) A traffic classification method with spectral clustering in SDN. In Proc 17th Int Conf Parallel and Distributed Computing, Applications and Technologies (PDCAT) IEEE pp. 391–394

  44. Xie J, Yu FR, Huang T, Xie R, Liu J, Liu Y (2018) A survey of machine learning techniques applied to software defined networking (SDN): Research issues and challenges. IEEE Commun Surv Tutorials 21(1):393–430

    Article  Google Scholar 

  45. Yamansavascilar B, Guvensan MA, Yavuz AG, Karsligil ME (2017) Application identification via network traffic classification. In 2017 International Conference on Computing, Networking and Communications (ICNC) IEEE pp. 843–848

  46. Yang M, Wu Q, Guo K, Zhang Y (2019) Evaluation of device cost, power consumption, and network performance in spatially and spectrally flexible sdm optical networks. J Lightwave Technol 37(20):5259–5272

    Article  Google Scholar 

  47. Ye Q, Li J, Qu K, Zhuang W, Shen X, Li X (2018) A network slicing framework for end-to-end QoS provisioning in 5G networks. IEEE Veh Technol Mag 13(2):65–74

    Article  Google Scholar 

  48. Yen JY (1971) Finding the k shortest loopless paths in a network. Manag Sci 17(11):712–716

    Article  MathSciNet  Google Scholar 

  49. Zhang J, Chen X, Xiang Y, Zhou W, Wu J (2014) Robust network traffic classification. IEEE/ACM Trans Networking 23(4):1257–1270

    Article  Google Scholar 

  50. Zhang J, Xiang Y, Wang Y, Zhou W, Xiang Y, Guan Y (2012) Network traffic classification using correlation information. IEEE Trans Parallel Distrib Syst 24(1):104–117

    Article  Google Scholar 

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Acknowledgements

We would like to thank Prof. Neil Millar and anonymous reviewers, whose insightful comments and modification suggestions are valuable for the improvement and completion of this paper. This work has been partially supported by JSPS Grant-in-Aid for Scientific Research (C) 21K04544.

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Correspondence to Weichang Zheng.

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Zheng, W., Yang, M., Zhang, C. et al. Application-aware QoS routing in SDNs using machine learning techniques. Peer-to-Peer Netw. Appl. 15, 529–548 (2022). https://doi.org/10.1007/s12083-021-01262-8

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