Skip to main content
Log in

Facing Network Management Challenges with Functional Data Analysis: Techniques & Opportunities

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

Current fixed and mobile networks’ behavior is rapidly changing, which calls for flexible monitoring approaches to avoid loosing track with such a fast evolutionary pace. Due to the many challenges that this scenario is posing to network managers, we propose the exploration of Functional Data Analysis (FDA) techniques as a mean to easily deal with network management and analysis issues. Specifically, we describe and evaluate several FDA methods with applications to network measurement preprocessing and clustering, bandwidth allocation, and anomaly and outlier detection. Our work focuses on how these FDA-based tools serve to improve the outcomes of traffic data mining and analysis, providing easy-to-understand and comprehensive outputs for network managers. We present the results that we have obtained from real case studies in the Spanish Academic network using throughput time series, comparing them with other alternatives of the state of the art. With this com- parative, we have qualitatively and quantitatively evaluated the advantages of FDA-methods in the networking area.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. https://www.sandvine.com/trends/encryption.html

References

  1. Aguado A, López V, Marhuenda J, González de Dios O., Fernández-Palacios JP (2015) ABNO: a feasible SDN approach for multivendor IP and optical networks. IEEE/OSA Journal of Opt Commun and Netw 7(2):A356–A362

    Article  Google Scholar 

  2. Andrews J, Buzzi S, Choi W, Hanly S, Lozano A, Soong A, Zhang J (2014) What will 5G be?. IEEE J Sel Areas Commun 32(6):1065-1082

    Article  Google Scholar 

  3. Antonello R, Fernandes S, Kamienski C, Sadok D, Kelner J, Gdor I, Szab G, Westholm T (2012) Deep packet inspection tools and techniques in commodity platforms: Challenges and trends. J Netw Comput Appl 35(6):1863–1878

    Article  Google Scholar 

  4. Arribas-Gil A, Romo J (2014) Shape outlier detection and visualization for functional data: the outliergram. Biostatistics 15(4):603–619

    Article  Google Scholar 

  5. Bajpai V, Schönwälder J (2015) A survey on internet performance measurement platforms and related standardization efforts. IEEE Commun Surv Tutor 17(3):1313–1341

    Article  Google Scholar 

  6. Bari MF, Boutaba R, Esteves R, Granville LZ, Podlesny M, Rabbani MG, Zhang Q, Zhani MF (2013) Data center network virtualization: a survey. IEEE Commun Surv Tutor 15(2):909–928

    Article  Google Scholar 

  7. Chen N, Rong B, Mouaki A, Li W (2015) Self-organizing scheme based on NFV and SDN architecture for future heterogeneous networks. Mob Netw Appl 20(4):466–472

    Article  Google Scholar 

  8. Claeskens G, Hubert M, Slaets L, Vakili K (2014) Multivariate functional halfspace depth. J Am Stat Assoc 109 (505):411–423

    Article  MathSciNet  MATH  Google Scholar 

  9. Cuevas A (2014) A partial overview of the theory of statistics with functional data. J Stat Plann Infer 147(1):1–23

    Article  MathSciNet  MATH  Google Scholar 

  10. Cuevas A, Febrero M, Fraiman R (2007) Robust estimation and classification for functional data via projection-based depth notions. Comput Stat 22(3):481–496

    Article  MathSciNet  MATH  Google Scholar 

  11. Febrero M, Galeano P, González-Manteiga W (2008) Outlier detection in functional data by depth measures, with application to identify abnormal NOx levels. Environmetrics 19(4):331–345

    Article  MathSciNet  Google Scholar 

  12. Febrero-Bande M, Oviedo de la Fuente M (2012) Statistical computing in functional data analysis: the R package fda.usc. J Stat Softw 51(4):1–28

    Article  Google Scholar 

  13. García-Dorado JL, Aracil J, Hernández JA, López de Vergara JE (2008) A queueing equivalent thresholding method for thinning traffic captures. In: Network Operations and Management Symposium, 2008. NOMS 2008. IEEE, pp 176–183

  14. García-Dorado JL, Hernández JA, Aracil J, López de Vergara JE, López-Buedo S (2011) Characterization of the busy-hour traffic of IP networks based on their intrinsic features. Comput Netw 55(9):2111–2125

    Article  Google Scholar 

  15. Gibeli LH, Breda GD, Miani RS, Zarpelão BB, De Souza Mendes L (2013) Construction of baselines for VoIP traffic management on open MANs. Int J Netw Manag 23(2):137–153

    Article  Google Scholar 

  16. Hubert M, Rousseeuw PJ, Segaert P (2015) Multivariate functional outlier detection. Stat Methods Appl 24(2):177–202

    Article  MathSciNet  MATH  Google Scholar 

  17. Jacques J, Preda C (2013) Functional data clustering: a survey. ADAC 8(3):231–255

    Article  MathSciNet  Google Scholar 

  18. Kyriakopoulos K, Parish D (2007) A live system for wavelet compression of high speed computer network measurements. In: Passive and Active Network Measurement, Lecture Notes in Computer Science, vol 4427. Springer, Berlin Heidelberg, pp 241– 244

    Google Scholar 

  19. Lakhina A, Papagiannaki K, Crovella M, Diot C, Kolaczyk ED, Taft N (2004) Structural analysis of network traffic flows. SIGMETRICS Perform Eval. Rev 32(1):61–72

    Article  Google Scholar 

  20. Lambert M (1995) RFC 1857: A Model for Common Operational Statistics

  21. Li B, Springer J, Bebis G, Gunes MH (2013) A survey of network flow applications. J Netw Comput Appl 36 (2):567–581

    Article  Google Scholar 

  22. López-Pintado S, Romo J (2009) On the concept of depth for functional data. J Am Stat Assoc 104 (486):718–734

    Article  MathSciNet  MATH  Google Scholar 

  23. López-Pintado S, Romo J (2011) A half-region depth for functional data. Comput Stat Data Anal 55(4):1679–1695

    Article  MathSciNet  MATH  Google Scholar 

  24. Manteiga WG, Vieu P (2007) Statistics for functional data. Comput Stat Data Anal 51(10):4788–4792

    Article  MathSciNet  MATH  Google Scholar 

  25. Mata F, García-dorado JL, Aracil J (2012) Detection of traffic changes in large-scale backbone networks: The case of the Spanish academic network. Comput Netw 56 (2):686–702

    Article  Google Scholar 

  26. Moreno V, Ramos J, Muelas D, García-Dorado JL, Gómez-Arribas FJ, Aracil J (2014) Multi-granular, multi-purpose and multi-Gb/s monitoring on off-the-shelf systems. Int J Netw Manag 24(4):221–234

    Article  Google Scholar 

  27. Muelas D, Gordo M, García Dorado JL, López de Vergara JE (2015) Dictyogram: a statistical approach for the definition and visualization of network flow categories. In: 11Th International Conference on Network and Service Management (CNSM 2015), pp 219–227

  28. Muelas D, López de Vergara JE, Berrendero JR (2015) Functional data analysis: a step forward in network management. In: 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM), pp 882–885

  29. De O, Schmidt R, Van den Berg H, Pras A (2015) Measurement-based network link dimensioning. In: 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM), pp 1071–1077

  30. De O, Schmidt R, Sadre R, Melnikov N, Schönwälder J, Pras A (2014) Linking network usage patterns to traffic gaussianity fit. In: 2014 IFIP Networking Conference, pp 1–9

  31. Oh E, Son K, Krishnamachari B (2013) Dynamic base station switching-on/off strategies for green cellular networks. IEEE Tran Wirel Commun 12(5):2126–2136

    Article  Google Scholar 

  32. Papadogiannakis A, Polychronakis M, Markatos EP (2013) Scap: Stream-oriented network traffic capture and analysis for high-speed networks. ACM, NY, USA

    Book  Google Scholar 

  33. Pison G, Struyf A, Rousseeuw PJ (1999) Displaying a clustering with CLUSPLOT. Comput Stat Data Anal 30(4):381–392

    Article  MATH  Google Scholar 

  34. Ramsay J, Hooker G, Graves S (2009) Functional data analysis with R and MATLAB. Springer, New York

    Book  MATH  Google Scholar 

  35. Ramsay J, Silverman B (1997) Functional data analysis. Springer, New York

    Book  MATH  Google Scholar 

  36. Ramsay J, Wickham H, Graves S, Hooker G fda: Functional Data Analysis (2014). http://CRAN.R-project.org/package=fda. R package version 2.4.4

  37. Saad S, Traore I, Ghorbani A, Sayed B, Zhao D, Lu W, Felix J, Hakimian P (2011) Detecting P2P botnets through network behavior analysis and machine learning. In: 2011 Ninth Annual International Conference on Privacy, Security and Trust (PST), pp 174–180

  38. Simmross-Wattenberg F, Asensio-Pérez J, Casaseca-de-la Higuera P, Martín-Fernández M, Dimitriadis I, Alberola-López C (2011) Anomaly detection in network traffic based on statistical inference and alpha-stable modeling. IEEE Trans Dependable Secure Comput 8(4):494–509

    Article  Google Scholar 

  39. Simoncelli D, Dusi M, Gringoli F, Niccolini S (2013) Stream-monitoring with BlockMon: convergence of network measurements and data analytics platforms. SIGCOMM Comput Commun Rev 43:29–36

    Article  Google Scholar 

  40. Wei TE, Mao CH, Jeng A, Lee HM, Wang HT, Wu DJ (2012) Android malware detection via a latent network behavior analysis. In: 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications (trustcom), pp 1251–1258

  41. Xu K, Wang F, Wang H (2012) Lightweight and informative traffic metrics for data center monitoring. J Netw Syst Manag 20(2):226–243

    Article  MathSciNet  Google Scholar 

  42. Zuo Y, Serfling R (2000) General notions of statistical depth function. Ann Stat 28(2):461–482

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

This work has been partially supported by the Spanish Ministries of Economy and Competitiveness (PackTrack, TEC2012-33754; Tráfica, TEC2015-69417-C2-1-R), and of Science and Innovation (MTM2013-44045-P).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to David Muelas.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Muelas, D., López de Vergara, J.E., Berrendero, J.R. et al. Facing Network Management Challenges with Functional Data Analysis: Techniques & Opportunities. Mobile Netw Appl 22, 1124–1136 (2017). https://doi.org/10.1007/s11036-016-0733-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-016-0733-5

Keywords

Navigation