一种基于元胞遗传的网络拥塞控制方法

A network congestion control method based on cellular genetic

为了解决快速增长的网络需求而带来的拥塞问题,基于元胞遗传提出了一种新的网络拥塞控制方法CGCC.首先,该方法将慢启动和拥塞避免策略进行改进,并结合元胞遗传利用定义的演化规则建立到达数据报文状态的预测方法.同时,通过实际数据进行仿真实验,对比分析了CGCC与TAHOE、RENO算法之间的优劣,实验结果表明,该算法具有较好的适应性.

In order to mitigate the network congestion problem with rapid growth of network needs, a novel network congestion control method （Cellular Genetic-based Congestion Control algorithm, CGCC） is proposed by cellular genetic. In this algorithm, the slow-start and congestion avoid tactics are im- proved at first, and the state prediction method of arrival data packet is presented by evolution rules in cellular genetic. Then, a simulation was conducted to study CGCC and TAHOE, as well as RENO algo- rithm with actual data. The results show that DFCA has better adaptability.