摘要
针对日益严重的网络拥塞现象,在传统的BLUE算法的基础上建立了一种新的主动队列管理方法(Immune clonal simulated annealing-based BLUE,IBLUE).该方法重新定义队列长度变化范围以及丢包概率,并且利用免疫克隆模拟退火算法来刻画队列长度变化情况.其次,以实际数据进行仿真实验,深入分析了影响该方法的关键因素,同时通过对比BLUE和BLUE+算法性能,结果表明IBLUE具有较好的适应性.
In order to mitigate the network congestion problem, a novel active queue management method （Immune clonal simulated annealing-based BLUE, IBLUE） is proposed by BLUE algorithm. In this method, the range of queue length and dropping rate are re-defined, and the average queue length is depicted by immune clonal simulated annealing. Then, a simulation with actual data was conducted to study key factor in IBLUE. The results show that, compared to BLUE and BLUE＋ , IBLUE has better adaptability.
出处
《微电子学与计算机》
CSCD
北大核心
2013年第10期81-84,89,共5页
Microelectronics & Computer
基金
国家自然科学基金项目(61173146)
江西省高等学校教学改革研究省级课题(JXJG-11-25-6)
关键词
主动队列管理
BLUE丢包
平均队列长度
免疫克隆模拟退火
active queue management
BLUE
dropping packet
average queue length
immune clonal simulated an-nealing
