Open AccessINFLUENCE OF CALCULATION ACCURACY OF TRAFFIC SELF-SIMILARITY EXPONENT ON A QUALITY OF SERVICE CHARACTERISTICS
Author(s) -
A.G. Lozhkovskyi,
V.A. Turchyn,
В.С. Андріяка
Publication year - 2020
Publication title -
naukovì pracì onaz ìm. o.s. popova
Language(s) - English
Resource type - Journals
eISSN - 2518-7147
pISSN - 2518-7139
DOI - 10.33243/2518-7139-2020-1-1-88-94
Subject(s) - hurst exponent , weibull distribution , network packet , self similarity , traffic generation model , quality of service , interval (graph theory) , similarity (geometry) , traffic equations , computer science , poisson distribution , statistics , mathematics , queueing theory , computer network , artificial intelligence , combinatorics , layered queueing network , image (mathematics) , geometry
The quality of service (QoS) characteristics in any telecommunication system depend on its scheme, requirements servicing rules and, to the greatest extent, on the type of traffic generated by the system requirements flow. In packet communication networks, a mathematical model of self-similar traffic is used, where the time interval between packets is described by Pareto or Weibull distributions. With an increase in the degree of self-similarity of packet traffic, the QoS characteristics in the system significantly deteriorate compared to servicing, for example, Poisson traffic. But for such traffic there is no reliable methodology for calculating the characteristics of the quality of service. The degree of selfsimilarity of traffic is determined by the Hurst exponent H or the coefficient of self-similarity. The method of increasing the accuracy of calculating the quality of service characteristics in a packet communication network with self-similar traffic due to a more accurate determination of the self-similarity coefficient or Hurst exponent depending on the parameters of the probability function of the distribution of the time interval between packets is analyzed. For self-similar traffic, the accuracy of new formulas for calculating the traffic self-similarity coefficient based on the shape parameter of the probability distribution has been verified. After a more accurate determination of the Hurst exponent, the average value of the number of packets in the system is calculated using the Norros formula, and then, from the approximation of the distribution function of the system states, the probability of waiting for packet service is calculated. With increasing accuracy of calculating the Hurst exponent, the accuracy of calculating the very characteristics of the quality of service also increases. Simulation confirmed the higher accuracy of these methods for calculating QoS characteristics in a system with self-similar traffic. Moreover, the differences in simulation and calculation results do not exceed 3 ... 5%.