COMPARATIVE ANALYSIS OF THE EFFECTIVENESS OF TELECOMMUNICATION NETWORK OVERLOAD DETECTION SCHEMES (Information systems and technologies security, 2023)
The scheme of congestion detection and regulation of input data flow based on the analysis of the sensitivity function of the telecommunication network performance is considered. The gradient of the sensitivity function characterizes the rate of change of this function and provides the optimal direction for adjusting the speed of the data source. To determine the sensitivity function, the use of a simple neural network model of a dynamic system is proposed. Determination of the gradient on the current value of the sign of the sensitivity function of the performance indicator is based on the algorithm of additive increase / multiple decrease. This algorithm is an alternative to the system of overload prediction and flow control, based on the control of the current value of the queue in comparison with a given threshold. The neural model for multi-step prediction of the queue state on the side of the telecommunication network receiver is considered. The results of comparative analysis of congestion control methods based on queue length analysis and sensitivity analysis with 1-step and 3-step horizons predicting network status are presented. The study was conducted for sinusoidal function of the narrow queue. It is shown that the key performance indicators for the sensitivity function-based scheme are better than for the queue length analysis scheme. The queue size-based scheme is more sensitive to changes in queue maintenance speed, and data source speed fluctuations are less sensitive for the sensitivity-based scheme. For systems based on sensitivity function analysis, a 3-step horizon predictor provides better performance and a smaller maintenance queue than a 1-step horizon scheme.
Keywords: telecommunication network; congestion forecasting; sensitivity function; gradient; neural system; queue management; prediction horizon