An lmproved Neural Network Cell Scheduling Algorithm for Multiple Input-Queues

An improved multiple input-queuing method(IMIQM) used in asynchronous transfer mode(ATM) switch by a Hopfield neural network(HNN) scheduling algorithm is proposed.The scheduling policy of more than one cell transferred in each input line during every time slot is employed in the IMIQM,and a new energy function is presented to accomplish this policy in HNN model.The computer simulations show that the maximum throughput is up to 0.904 of IMIQM when the switch scale N is 150 and the number of queues is 5,but it is only 0.856 and 0.886 for general multiple input-queuing method(MIQM) and window method(WM) respectively with the same scale and queue(window) number under the same traffic model and load.It means that the throughput of IMIQM is improved greatly compared with the MIQM and WM.The IMIQM,therefore,can be used in real-time optimization scheduling of large-scale ATM switches.