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

• Published in: Journal of physics. Conference series Vol. 2176; no. 1; pp. 12022 - 12031
• Main Authors: Ageev, S A, Ageeva, N S, Grachev, A S, Karetnikov, V V
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.06.2022
• Subjects: Adaptive algorithms; Communication; Communication networks; Communications networks; Communications traffic; Control algorithms; Control theory; Estimation; Fuzzy control; Machine learning; Monitoring; Parameters; Physics
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/2176/1/012022

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.