Buffer management using genetic algorithms and neural networks

In ATM networks, many control mechanisms were proposed to manage buffers by introducing control parameters which are adjustable by network providers. However, it is difficult to adaptively select these control parameters in ATM networks for the traffic environmental is much more complicated. In this paper, we propose a control scheme using the genetic algorithms and the neural estimator in the buffer management of an ATM switch. Simulation results demonstrate that even if the traffic environment and the service requirements are dynamically changing, the proposed control scheme is still effective in adaptively selecting control parameters.