Comparative analysis of algorithms for estimating traffic parameters in corporate multiservice communication networks

Abstract

Adequate management of modern corporate communication networks is possible if many control procedures function in near real time. In this case, the processed network monitoring data must have accurate characteristics sufficient for making objective management decisions. This fully applies to the data monitoring of network traffic parameters, which determines the relevance of the proposed work. The proposed in the paper algorithm for on-line estimation of traffic parameters in corporate multiservice communication networks is based on the concept of conditional nonlinear Pareto-optimal filtering V. C. Pugachev. Its essence is that the estimation of traffic parameters is performed in two stages - in the first stage, we evaluate the forecast values of parameters, and in the second, with the next observations of random sequences, we make adjustments to their values. Traffic parameter values forecasts are constructed in a small-sized sliding window, and the adjustment is implemented on the basis of pseudo-gradient procedures whose parameters are adjusted using a fuzzy control algorithm based on the Takagi-Sugeno method. The proposed algorithm belongs to the class of adaptive algorithms with prior learning. The maximum value of the average relative error of estimation of traffic parameters was less than 8.2%, which is a sufficient value for the implementation of operational network management tasks. At the same time, the actual scientific and technical task is to analyze the comparison of the characteristics of the developed adaptive algorithm with the characteristics of the optimal algorithms, the characteristics of which are the maximum achievable. Translated with www.DeepL.com/Translator (free version). The results of a comparison of the proposed method with the optimal Coleman filtration (OKF) are presented.

Adaptive Method for Estimating Traffic Characteristics in Corporate Multi-Service Communication Networks for Transport Companies

“…Let us note that the has a finite mathematical expectation and variance. Note that model (1) and the assumptions made define a non-Gaussian, non-stationary Markov sequence model ( 1) and the assumptions made define a non-Gaussian, non-stationary Markovian sequence [7][8][9].…”

“…As a consequence of choosing a bounded step factor, the variance of RS parameter estimation will also be bounded from below. Consequently, it is necessary to find a compromise solution between speed and accuracy of RS intensity values estimation [8,9].…”

“…It is noted in [6][7][8] that traffic for various applications in CMCN can be approximated by means of random sequences (RS), the main probability distributions of which are Poisson, Pareto, Weibull, lognormal and exponential distributions. Traffic in CMCN is nonstationary in nature, and the mathematical models that adequately describe its behaviour are non-linear stochastic models [7][8][9]. This significantly complicates the development of procedures for estimating the characteristics of network traffic with the required quality [7].…”

“…As a rule, recurrence methods and algorithms are used to solve the problem of nonstationary and nonlinear RS parameter estimation, the main of which are linearized Kalman filter (LKF) and extended Kalman filter (EKF) [8,9].…”

“…In [5,8,9] it is shown that one of the constructive approaches to the solution of the problem of estimation of the vector parameters of random processes, at nonlinear models of observation, is the method of conditional nonlinear Pareto-optimal filtering. The essence of this method is that the estimation of the unknown parameter is carried out in two stages.…”