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Development of an adaptive routing mechanism in software-defined networks

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Abstract

The purpose of this paper is to develop of a single mechanism of the adaptive routing of different types of traffic based on the current quality of service requirements. Software-defined networking is a technology of the future. The current development trend of communication systems constantly confirms this fact. However, to date, the use of this technology in its current form is only justified in large networks of major technology companies and service providers. Currently, a large number of dynamic routing protocols have been developed in communication networks. Our task is to create a solution that can make it possible to use the ability of each node to make a decision on the transmission of information by every possible means for each type of traffic. This task can be accomplished by solving the problem of the development of generalized metric that characterizes the communication channels between devices in the network in detail and the problem of the development of a mechanism of adaptive network logical topology reconfiguration (route control) in order to ensure the high quality of service of the whole network that meets current quality requirements for a particular type of service.

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References

  1. ITU-T. Recommendation Y.1540 Internet Protocol Aspects–Quality of Service and Network Performance, 2011.

  2. ITU-T. Recommendation Y.1541 Network Performance Objectives for IP-Based Services, 2011.

  3. ITU-T. Y.1541: Network Performance Objectives for IP-Based Services.

  4. Buford, J.F., Yu, H., and Lua, E., P2P Networking and Applications, The Morgan Kaufmann Series in Networking, 2009.

    Google Scholar 

  5. De Ghein, L., MPLS Fundamentals, Cisco Press, 2006.

    Google Scholar 

  6. Nadeau, T.D. and Gray, K., SDN: Software Defined Networks, O’Reilly Media, 2013.

    Google Scholar 

  7. Veshegna, Sh., Kachestvo obsluzhivaniya v IP-setyakh (Service Quality in IP-Networks), Cisco Press, 2003.

    Google Scholar 

  8. Blei, D.M., Ng, A.Y., and Jordan, M.I., Latent Dirichlet Allocation, J. Mach. Learn. Res., 2002, vol. 3, pp. 993–1022.

    MATH  Google Scholar 

  9. Daud, A., Li, J., Zhou, L., and Muhammad, F., Knowledge discovery through directed probabilistic topic models, Front. Comput. Sci. China, 2010, vol. 4, no. 2, pp. 280–301.

    Article  Google Scholar 

  10. Gelman, A., Carlin, J.B., Stern, H.S., and Rubin, D.B., Bayesian Data Analysis, Chapman and Hall/CRC, 2013.

    MATH  Google Scholar 

  11. Vapnik, V., Statistical Learning Theory, Wiley, 1998.

    MATH  Google Scholar 

  12. Ferguson, T.S., A Bayesian analysis of some nonparametric problems, Ann. Stat., 1973, vol. 1, no. 2, pp. 209–230.

    Article  MathSciNet  MATH  Google Scholar 

  13. Kintsch, W., Handbook of Latent Semantic Analysis, Hillsdale, NJ: Erlbaum, 2007.

    Google Scholar 

  14. Knorr, E.M. and Ng, R.T., Algorithms for mining distance-based outliers in large datasets, Proceedings of the 24th International Conference on Very Large Data Bases, 1998, vol. 1, 392–403.

    Google Scholar 

  15. Knorr, E.M. and Ng, R.T., Finding intensional knowledge of distance-based outliers, Proceedings of the 25th International Conference on Very Large Data Bases, 1999, vol. 1, pp. 211–222.

    Google Scholar 

  16. Minka, T. and Lafferty, J., Expectation-propagation for the generative aspect model, Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence, 2002.

    Google Scholar 

  17. Ganeriwal, S., Balzano, L.K., and Mani, B., Reputation-based framework for high integrity sensor networks, ACM Trans. Sens. Networks, 2007, vol.5.

  18. Paramasivan, B.A., Prakash, M.J., and Kaliappan, M., Development of a secure routing protocol using game theory model in mobile ad hoc networks, J. Commun. Networks, 2015, vol. 1, no, 15, pp. 75–83.

    Article  Google Scholar 

  19. Samsudin, N.A. and Bradley, A.P., Extended naive Bayes for group based classification Advances in Intelligent Systems and Computing, 1st International Conference on Soft Computing and Data Mining, 2014, vol. 287, pp. 497–506.

    MATH  Google Scholar 

  20. Jolliffe, I.T., Principal Components Analysis, New York: Springer-Verlag, 1986.

    Book  MATH  Google Scholar 

  21. Shipman, C.M., Hopkinson, K.M., and Lopez, J., Con-resistant trust for improved reliability in a smart-grid special protection system, IEEE Trans. Power Delivery, 2015, vol. 13, no. 1, pp. 455–462.

    Article  Google Scholar 

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Correspondence to A. N. Noskov.

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Original Russian Text © A.N. Noskov, I.A. Manov, 2015, published in Modelirovanie i Analiz Informatsionnykh Sistem, 2015, Vol. 22, No. 4, pp. 521–532.

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Noskov, A.N., Manov, I.A. Development of an adaptive routing mechanism in software-defined networks. Aut. Control Comp. Sci. 50, 520–526 (2016). https://doi.org/10.3103/S0146411616070166

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