基于元胞遗传方法的主动队列管理算法研究

The Study of Active Queue Management Algorithm Based on Cellular Genetic Method

为了解决网络拥塞现象,基于CHOKe方法建立了一种新的主动队列管理算法New-SCHOKe。该方法首先根据采样击中和队列击中定义了丢包策略和丢包概率,并且利用元胞遗传技术刻画了平均队列长度。同时,以实际数据进行仿真实验,对比分析了该算法与SCHOKe和CHOKe之间的性能,结果表明New-SCHOKe具有较好的适应性。

In order to mitigate the network congestion problem, a novel active queue management algorithm New- SCHOKe is proposed by CHOKe. In this algorithm, the dropping policy and dropping rate are defined by sampling hit and queue hit, and the average queue length is depicted by cellular genetic. Then, a simulation was conducted to re- search on the performance of New-SCHOKe and SCHOKe, as well as CHOKe algorithm with actual data. The results show that New-SCHOKe has better adaptability.