基于神经网络和输入缓冲器分级的ATM信元调度方法

ATM Cell Scheduling Based on NN andInput Buffers with Different Priorities

针对ＡＴＭ中的多种业务类型，提出了将不同业务类型的信元存储于交换机中不同输入缓冲器的方法，并使用神经网络对其队首信元进行调度．实验结果表明，采用神经网络对队首信元进行调度，与开窗随机选取信元方法相比，可降低信元丢失率和排队时延；将到达信元按其业务类型分别存储于不同缓冲器中并用神经网络进行队首信元调度，可使这些信元满足各自的性能指标．

A novel neural network cell controller with a new structure of input buffers in ATM switch are proposed. For each input port of switch, there are two buffers. Cells sensitive to time delay are put in high priority buffers and cells insensitive to time delay in low priority buffers. According to certain optimization rules, head cells of input buffers are selected to be switched by a hopfield neural network controller. The simulation results show that the proposal can ensure cells to meet their QoS and the cell lost rate and cell delay are lower than those of randomly selecting cells in a open window. Also, the effects of parameters in neural network are discussed.