复杂网络下具有预测与自我调节能力的拥塞控制算法

Congestion Control Algorithm with Prediction and Self-regulation Ability Under Complex Network

现有拥塞控制算法在复杂网络环境下存在丢包率过大的问题。为此,通过研究网络拥塞的控制问题,提出一种具有预测与自我调节能力的拥塞控制算法。采用模糊神经网络的控制器预测网络拥塞,根据缓冲器中的队列长度进行实时预测,在发生拥塞前,通过抑制控制输入端的发送速率,并结合递增参数和递减参数等变量动态调节发送速率。实验结果表明,该算法在不同信噪比下能够保持较好的收敛效果,而且网络丢包率不受网络交换速率的影响,具有较好的稳定性与保真性。

Existing congestion control algorithm still exists the problem of packet loss rate is too large in the complex network environment. In response to this phenomenon, through studying the problem of network congestion control, this paper proposes a congestion control algorithm,which has the ability of prediction and self-regulation. It uses fuzzy neural network controllers to predict the congestion in the network. According to the queue length in buffer for real-time predictioin. Before the congestion is about to occur, it uses transmission rate of the control input to suppress. It dynamicly adjusts the transmission rate combined with variables like increment parameters and decrement parameters. Experimental results show that, this algorithm can be maintained better convergence effect at different signal-to-noise ratio. And its network packet loss rate is not affected by the exchange rate of the network. It has good stability and fidelity.