A new wavenet-based network congestion predictor - WBCP

In this paper, a wavelet neural network (WNN) (or wavenet) predictor is used to predict the congestion state for each link in the computer network. The proposed WBCP predictor generates the congestion state for each link based on the utilization values of each link measured in the previous time intervals. WNNs possess the learning and generalization capabilities of the traditional neural networks together with the local characteristics of wavelet functions that enhance network ability to deal with sudden changes and burst network load in efficient manner. The proposed predictor can be used in the context of active congestion control techniques to provide the congestion state of each computer network link.