An Approach to Analysis of Useful Quality Service Indicator and Traffic Service with Fuzzy Logic

  • Bayram G. Ibrahimov
  • Almaz A. AlievaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1095)


The indicators of the multimedia traffic quality service and the tasks managing the distribution resources in multiservice telecommunication networks are analyzed in the presence of the incoming load self-similarity property. The aim of the work is to consider an approach of controlling the quality of service (QoS) indicator in terms of the self-similar traffic based on the prediction of the Hurst coefficient using fuzzy logic. Based on the network traffic structure study, a new approach of analyzing the quality of useful service index and service traffic has been proposed, which is based on the theory of fuzzy sets.


Fuzzy logic Self-similar traffic QoS Information and network resource Hurst coefficient Fuzzy set Membership function 


  1. 1.
    Efimushkin, V., Ledovskikh, T., Ivanov, A., Shalaginov, V.: The role SDN/NFV technologies in the infrastructure of the digital economy. Exp. Test. Implement. Telecommun. 3(8), 27–36 (2018)Google Scholar
  2. 2.
    Shelukhin, O.: Modeling Information Systems. Telekom, Moscow (2011)Google Scholar
  3. 3.
    Ibrahimov, B., Humbatov, R., Ibrahimov, R.: Analysis performance multiservice telecommunication networks with using architectural concept future networks. T-Comm. 12(12), 84–88 (2018)Google Scholar
  4. 4.
    Bitner, V., Lizneva, Y.: Using the theory of neural networks in forecasting signal traffic. Inf. Space 2(3), 36–39 (2008)Google Scholar
  5. 5.
    Mammadov, H., Ibrahimov, B.: Efficiency methods forecasting of the office traffic signaling systems with use technologies of neural networks. In: Proceedings 4th World Conference on Soft Computing, Berkeley, vol. 296, pp. 241–245 (2014)Google Scholar
  6. 6.
    Zadeh, L.: Fuzzy sets. Inf. Control 8, 338–353 (1965)CrossRefGoogle Scholar
  7. 7.
    Belman, R., Zadeh, L.: Decision making in a fuzzy environment. Manag. Sci. 17, 141–164 (1970)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Aliev, R., Gurbanov, R., Aliev, R., Huseynov, O.: Investigation of stability of fuzzy dynamical systems. In: Proceedings of the Seventh International Conference on Applications of Fuzzy Systems and Soft Computing, pp. 158–164. Quadrat-Verlag, Siegen (2006)Google Scholar
  9. 9.
    Bellman, R., Giertz, M.: On the analytic formalism on the theory of fuzzy sets. Inf. Sci. 5, 149–157 (1974)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Azerbaijan Technical UniversityBakuAzerbaijan
  2. 2.Mingechaur State UniversityMingechaurAzerbaijan

Personalised recommendations