ABR traffic management using minimal resource allocation (neural) networks
This paper presents an adaptive available bit rate (ABR) traffic management scheme in asynchronous transfer mode (ATM) networks using the newly developed minimal resource allocation network (MRAN). MRAN generates a minimal radial basis function (RBF) neural network by adding and pruning the hidden neurons based on the input data and is well suited for on-line adaptive control of time varying nonlinear systems. In this paper, the ATM traffic is modeled using the network simulation package OPNET. The performance of MRAN-controller is compared with the conventional ABR control scheme explicit rate indication with congestion avoidance (ERICA) for different traffic scenarios such as bursty and Variable Bit Rate (VBR) traffic. Results indicate that MRAN-controller performs better than ERICA by keeping the queue length and delay to a minimum while maintaining a higher Link utilization and throughput. (C) 2002 Elsevier Science B.V. All rights reserved.
opnet erica abr flow control rbf network