计算机工程2024，Vol.50Issue(5)：306-312,7.DOI:10.19678/j.issn.1000-3428.0066870
基于流量自相似性的网络队列管理算法
Queue Management Algorithm for Network Based on Traffic Self-similarity
摘要
Abstract
The self-similarity of network traffic result in a continuous burst state of data in the network.In this context,conventional queue management algorithms cannot predict the network traffic burst state in advance,thus resulting in end-to-end delays,high packet loss rates,and deteriorated network throughputs.To solve these problems,an Active Queue Management(AQM)algorithm based on prediction of the network traffic,P-ARED,is proposed.Based on the mean value and variance of network traffic,the concept of network traffic level is proposed.The relationship between the probability of network traffic level transition and Hurst parameters is discussed,and a network traffic level prediction method based on Bayesian estimation is proposed.On this basis,based on the optimization of parameters such as the average queue length and the minimum threshold for cache queue length in self-similar network environments,and based on the Hurst parameters and self-similar traffic level prediction results,the calculation method for group dropout probability in the ARED algorithm is redesigned to improve the stability of cache queue length.Simulation results show that,compared with the AQM algorithms in the existing literatures,the P-ARED algorithm reduces the end-to-end delay and packet loss rate,improves the end-to-end throughput performance,and increases the average throughput by 7.63％at the maximum.Additionally,it reduces the average packet loss rate by 17.52％at the maximum.关键词
网络流量/自相似性/主动队列管理/随机早期检测/流量等级Key words
network traffic/self-similarity/Active Queue Management(AQM)/Random Early Detection(RED)/traffic level分类
信息技术与安全科学引用本文复制引用
魏德宾,杨力,潘成胜,沈婷..基于流量自相似性的网络队列管理算法[J].计算机工程,2024,50(5):306-312,7.基金项目
国家自然科学基金(U21B2003,61931004). （U21B2003,61931004）
