Intellectual method of operational evaluation of the network element state to ensure the quality of services in corporate multiservice communications networks

Abstract

The work proposes and investigates an intelligent method and algorithms for on-line assessment of the state of network elements to ensure the required quality indicators of provided communication services in corporate high-speed multiservice communication networks. The developed method and algorithms operate in a mode close to real time. One of the features of corporate multiservice communication networks is the high dynamics of changes in their state. The main task of the automated control system, which is an integral part of the corporate multiservice communication network, is to ensure the specified quality of the provided communication services to the consumer. Thus, the relevance of the research presented in the work is due to the fact that most of the management processes in corporate high-speed multiservice communication networks must be implemented in a mode close to real time with a given quality. The basis of the method for operational assessment of the state of network elements is the concept of creating and using intelligent agents. In the proposed approach, intelligent agents are created as hierarchical fuzzy situational networks, in which control solutions, in contrast to known methods based on the use of reference situations, are applied based on solving a hierarchical set of optimization problems using fuzzy mathematical programming methods. The main paradigm of their functioning is “situation -action”.

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

“…Let us represent the observations of traffic intensity values at an interface of a network element (NE), in the form of RV 𝑥(𝑖), given at discrete moments of time 𝑖 = {1, 2, … , 𝑛, … }.. Let us assume that RV 𝑥(𝑖) observations are described by an additive-multiplicative model [3,4]:…”

“…Model (11) and the assumptions determine the non-Gaussian, nonstationary Markov sequence [9]. It is required to develop a vector recurrent algorithm for evaluating the mathematical expectation of x(i), the standard deviation (SD) of the RV and its coefficient of variation according to the criterion of the minimum mean square of the error [3,4]:…”

“…where 𝑚 ̂(𝑖), 𝜎 ̂(𝑖), 𝐾 ̂𝑣(𝑖)− estimates of mathematical expectation, standard deviation and coefficient of variation RV𝑥(𝑖) at step 𝑖, а 𝑚(𝑖), 𝜎(𝑖), 𝐾𝑣(𝑖)− their values. The forecast function for the current value of the mathematical expectation RV is determined as [3,4]:…”

“…The dynamics of changes in network states and rates of provision of communication services in the CMCN makes the problem of reliable operational quality control urgent. To solve it, it is necessary to assess parameters and characteristics of network traffic [2][3][4].…”

Statistical research and modeling network traffic

“…Analysis of the above models allows you to concentrate on the last three presented in Table. 2 and use them in solving problems of modeling telecommunication systems and networks with the resulting global problems − planning the development of telecommunication networks, implementing differentiated services, evaluating the characteristics of computing resources that guarantee the required quality of service of the corresponding traffic [10][11][12].…”