Development of an adaptive routing mechanism in software-defined networks
Modelirovanie i analiz informacionnyh sistem, Tome 22 (2015) no. 4, pp. 521-532.

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The purpose of this work is to develop a unitary mechanism of adaptive routing of different kinds, basing on the current requirements on the quality of service. The software configuration of a network is the technology of the future. The trend in communication systems constantly confirms this fact. However, the application of this technology in its current form is justified only in large networks of technology giants and telecom operators. Today we have a large number of dynamic routing protocols to route big volume traffic in communication networks. Our task is to create the solution that can use the opportunities of each node to make a decision on the transmission of information by all possible means for each type of traffic. Achieving this goal is possible by solving the problem of the development of generalized metrics, which details the links between devices in the network, and the problem of establishing a framework of adaptive logical network topology (route management) to ensure the quality of the whole network in order to meet the current requirements on the quality of a particular type service.
Keywords: software defined network, information flow.
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A. N. Noskov; I. A. Manov. Development of an adaptive routing mechanism in software-defined networks. Modelirovanie i analiz informacionnyh sistem, Tome 22 (2015) no. 4, pp. 521-532. http://geodesic.mathdoc.fr/item/MAIS_2015_22_4_a5/

[1] Internet protocol aspects — Quality of service and network performance, ITU-T. Recommendation Y.1540, 2011

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

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

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

[5] Luc De Ghein, MPLS Fundamentals, Cisco Press, 2006

[6] Nadeau T. D., Gray K., SDN: Software Defined Networks, O'Reilly Media, 2013

[7] Veshegna Sh., Kachestvo obslujivaniya v IP-setyah, Cisco Press, 2003, 356 pp. (in Russian)

[8] Blei D. M., Ng A. Y., Jordan M. I., “Latent Dirichlet Allocation”, Journal of Machine Learning Research, 3 (2002), 993–1022

[9] Daud A, Li J.,Zhou L., Muhammad F., “Knowledge discovery through directed probabilistic topic models”, Frontiers of Computer Science in China, 4:2 (2010), 280–301 | DOI

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

[11] Vapnik V., Statistical Learning Theory, Wiley, 1998 | MR | Zbl

[12] Ferguson T. S., “A bayesian analysis of some nonparametric problems”, The Annals of Statistics, 1:2 (1973), 209–230 | DOI | MR | Zbl

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

[14] Knorr E. M., Ng R. T., “Algorithms for Mining Distance-Based Outliers in Large Datasets”, Proceedings of the 24th International Conference on Very Large Data Bases (1998), v. 1, 392–403

[15] Knorr E. M., Ng R. T., “Finding Intensional Knowledge of Distance-based Outliers”, Proceedings of the 25th International Conference on Very Large Data Bases (1999), v. 1, 211–222

[16] Minka T., Lafferty J., “Expectation-propagation for the generative aspect model”, Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence (2002)

[17] Ganeriwal S., Balzano L. K., Srivastava M. B., “Reputation-based Framework for High Integrity Sensor Networks”, ACM Transactions on Sensor Networks, 4:3 (2008), 15, 37 pp. | DOI

[18] Paramasivan B. A., Prakash M. J., Kaliappan M., “Development of a secure routing protocol using game theory model in mobile ad hoc networks”, Journal of Communications and Networks, 1:15 (2015), 75–83 | DOI

[19] Samsudin N. A., Bradley A. P., “Extended naïve bayes for group based classification Advances in Intelligent Systems and Computing”, Recent Advances on Soft Computing and Data Mining, 1st International Conference on Soft Computing and Data Mining, Advances in Intelligent Systems and Computing, 287, 2014, 497–506 | DOI

[20] Jolliffe I. T., Principal components analysis, Springer-Verlag, New York, 1986 | MR

[21] Shipman C. M., Hopkinson K. M., Lopez J., “Con-resistant trust for improved reliability in a smart-grid special protection system”, IEEE Transactions on Power Delivery, 13:1 (2015), 455–462 | DOI | MR