Traffic modeling, prediction, and congestion control for high-speed networks: a fuzzy AR approach

In general, high-speed network traffic is a complex, nonlinear, nonstationary process and is significantly affected by immeasurable parameters and variables. Thus, a precise model of this process becomes increasingly difficult as the complexity of the process increases. Recently, fuzzy modeling has been found to be a powerful method to effectively describe a real, complex, and unknown process with nonlinear and time-varying properties. In this study, a fuzzy autoregressive (fuzzy-AR) model is proposed to describe the traffic characteristics of high-speed networks. The fuzzy-AR model approximates a nonlinear time-variant process with a combination of several linear local AR processes using a fuzzy clustering method. We propose that the use of this fuzzy-AR model has greater potential for congestion control of packet network traffic. The parameter estimation problem in fuzzy-AR modeling is treated by a clustering algorithm developed from actual traffic data in high-speed networks. Based on adaptive AR-prediction model and queueing theory, a simple congestion control scheme is proposed to provide an efficient traffic management for high-speed networks. Finally, using the actual ethernet-LAN packet traffic data, several examples are given to demonstrate the validity of this proposed method for high-speed network traffic control.