摘要
针对随机早期检测及其变体算法中平均队列长度和数据包最大丢弃概率的计算对网络流量的变化反应迟缓的问题,提出了一种基于网络流量水平等级预测的自适应随机早期检测算法。基于自相似网络流量的统计特性,建立了网络流量水平等级转移概率表,设计了自相似网络流量水平等级预测方法,该方法复杂度较低且精度较高。进一步,将预测结果应用于等间隔平均队列长度计算及数据包最大丢弃概率调整中,在固定和可变瓶颈链路容量2种情况下的仿真发现,无论自相似程度如何,所提算法在丢包率和吞吐量方面都有提升,特别在Hurst参数较大且流量负载较低时,吞吐量性能提升较大。
In view of the problem that the calculation of average queue length and maximum packet drop probability in random early detection algorithm and its variants reflect the changes of network traffic slowly,an adaptive random early detection algorithm based on network traffic level grade prediction was proposed.Based on the statistical characteristics of self-similar network traffic,the transition probability table of network traffic level grade was established,and a grade prediction method of self-similar network traffic level with low complexity and high accuracy was proposed.Furthermore,the prediction results were applied to calculate the average queue length in equal interval and adjust the maximum packet drop probability.Under the condition of fixed and variable bottleneck link capacity,it is found that regardless of the degree of self-similarity of network traffic,the proposed algorithm can improve the throughput and packet loss rate,especially when the Hurst parameter is large and the traffic is light.
作者
魏德宾
潘成胜
杨力
颜佐任
WEI Debin;PAN Chengsheng;YANG Li;YAN Zuoren(School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China;School of Information Engineering,Dalian University,Dalian 116622,China;School of Electronic and Information Engineering,Nanjing University of Information Science and Technology,Nanjing 210044,China)
出处
《通信学报》
EI
CSCD
北大核心
2023年第6期154-166,共13页
Journal on Communications
基金
国家自然科学基金资助项目(No.U21B2003,No.61931004)。
关键词
主动队列管理
网络流量
自相似
流量水平等级预测
active queue management
network traffic
self-similar
traffic level grade prediction
