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Estimating VNF Resource Requirements Using Machine Learning Techniques

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10634))

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Abstract

Resource Management in the network function virtualization (NFV) environment is a challenging task. The continuously varying demands of virtual network functions (VNF) call for dynamic algorithms to efficiently scale the allocated resources and meet fluctuating needs. In this context, studying the behavior of a VNF as a function of its environment helps to model its resource requirements and thus allocate them dynamically. This paper investigates the use of machine learning techniques to estimate VNFs needs in term of CPU as a function of the traffic they will process. We propose and adapt a Support Vector Regression (SVR) based approach to resolve the problem. Results show its efficiency and superiority compared to the state of the art.

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References

  1. Kim, C., Sivaraman, A., Katta, N., Bas, A., Dixit, A., Wobker, L.J.: In-band network telemetry via programmable dataplanes. In: SIGCOMM Industrial Demo Program (2015)

    Google Scholar 

  2. Kreutz, D., Ramos, F.M., Verissimo, P.E., Rothenberg, C.E., Azodolmolky, S., Uhlig, S.: Software-defined networking: a comprehensive survey. Proc. IEEE 103(1), 14–76 (2015)

    Article  Google Scholar 

  3. Mestres, A., Rodriguez-Natal, A., Carner, J., Barlet-Ros, P., Alarcón, E., Solé, M., Muntés, V., Meyer, D., Barkai, S., Hibbett, M.J., et al.: Knowledge-defined networking (2016)

    Google Scholar 

  4. Jmila, H., Drira, K., Zeghlache, D.: A self-stabilizing framework for dynamic bandwidth allocation in virtual networks. In: 2016 IEEE/IFIP Network Operations and Management Symposium, pp. 69–77 (2016)

    Google Scholar 

  5. Mijumbi, R., Serrat, J., Gorricho, J.L., Bouten, N., Turck, F.D., Boutaba, R.: Network function virtualization: state-of-the-art and research challenges. IEEE Commun. Surv. Tutor. 18(1), 236–262 (2016)

    Article  Google Scholar 

  6. Mijumbi, R., Serrat, J., Gorricho, J.L., Latre, S., Charalambides, M., Lopez, D.: Management and orchestration challenges in network functions virtualization. IEEE Commun. Mag. 54(1), 98–105 (2016)

    Article  Google Scholar 

  7. Herrera, J.G., Botero, J.F.: Resource allocation in NFV: a comprehensive survey. IEEE Trans. Netw. Serv. Manag. 13(3), 518–532 (2016)

    Article  Google Scholar 

  8. Mijumbi, R., Hasija, S., Davy, S., Davy, A., Jennings, B., Boutaba, R.: A connectionist approach to dynamic resource management for virtualised network functions. In: 12th International Conference on Network and Service Management (CNSM), pp. 1–9. IEEE (2016)

    Google Scholar 

  9. Mijumbi, R., Hasija, S., Davy, S., Davy, A., Jennings, B., Boutaba, R.: Topology-aware prediction of virtual network function resource requirements. IEEE Tran. Netw. Serv. Manag. 14(1), 106–120 (2017)

    Article  Google Scholar 

  10. Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61–80 (2009)

    Article  Google Scholar 

  11. Clearwater project. http://www.projectclearwater.org/

  12. Gupta, L., Samaka, M., Jain, R., Erbad, A., Bhamare, D., Metz, C.: COLAP: a predictive framework for service function chain placement in a multi-cloud environment. In: IEEE 7th Annual Computing and Communication Workshop and Conference, pp. 1–9 (2017)

    Google Scholar 

  13. Gupta, V., Dharmaraja, S., Arunachalam, V.: Stochastic modeling for delay analysis of a VoIP network. Ann. Oper. Res. 233(1), 171–180 (2015)

    Article  MATH  MathSciNet  Google Scholar 

  14. Shi, R., Zhang, J., Chu, W., Bao, Q., Jin, X., Gong, C., Zhu, Q., Yu, C., Rosenberg, S.: MDP and machine learning-based cost-optimization of dynamic resource allocation for network function virtualization. In: IEEE International Conference on Services Computing, pp. 65–73 (2015)

    Google Scholar 

  15. Riccobene, V., McGrath, M.J., Kourtis, M.A., Xilouris, G., Koumaras, H.: Automated generation of VNF deployment rules using infrastructure affinity characterization. In: 2016 IEEE NetSoft Conference and Workshops (NetSoft), pp. 226–233 (2016)

    Google Scholar 

  16. Salzberg, S.L.: C4.5: programs for machine learning by J. Ross Quinlan. Mach. Learn. 16(3), 235–240 (1994). Morgan Kaufmann Publishers Inc. 1993

    MathSciNet  Google Scholar 

  17. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)

    MATH  Google Scholar 

  18. Jolliffe, I.: Principal Component Analysis. Springer, New York (1986). doi:10.1007/b98835

    Book  MATH  Google Scholar 

  19. Khedher, M.I., El Yacoubi, M.A.: Two-stage filtering scheme for sparse representation based interest point matching for Person re-identification. In: Battiato, S., Blanc-Talon, J., Gallo, G., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2015. LNCS, vol. 9386, pp. 345–356. Springer, Cham (2015). doi:10.1007/978-3-319-25903-1_30

    Chapter  Google Scholar 

  20. Snort. https://www.snort.org/

  21. OVS. http://openvswitch.org/

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Author information

Authors and Affiliations

  1. SAMOVAR, Telecom SudParis, CNRS, University of Paris-Saclay, 9 rue Charles Fourier, 91011, Evry Cedex, France

    Houda Jmila, Mohamed Ibn Khedher & Mounim A. El Yacoubi

Authors
  1. Houda Jmila
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  2. Mohamed Ibn Khedher
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  3. Mounim A. El Yacoubi
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Corresponding author

Correspondence to Houda Jmila .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Jmila, H., Khedher, M.I., El Yacoubi, M.A. (2017). Estimating VNF Resource Requirements Using Machine Learning Techniques. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10634. Springer, Cham. https://doi.org/10.1007/978-3-319-70087-8_90

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  • DOI: https://doi.org/10.1007/978-3-319-70087-8_90

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70086-1

  • Online ISBN: 978-3-319-70087-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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