基于QPSO-LMK的自相似性网络流量预测  

Prediction of self-similar traffic based on QPSO-LMK

作　　者：毛蓝[1] 刘渊[1] 梅娟[1] 

机构地区：[1]江南大学信息工程学院,江苏无锡214122

出　　处：《计算机工程与设计》2007年第18期4401-4402,4406,共3页Computer Engineering and Design

基　　金：国防预研基金项目(A1420061266)。

摘　　要：现在对高性能、高效性流量测量的研究表明网络流量呈现统计上的自相似性。因此,网络预测在网络管理中占据重要地位。使用QPSO(quantum-behaved particle swarm optimization)对预测自相似性网络流量的最小均值峰度(LMK)方法进行优化,能够获得较小的信噪比SNR-1(signal to noise ratio)。通过对真实网络流量的仿真实验,表明该方法能比LMK(最小均值峰度)算法更精确的预测网络流量。Recent studies of high quality, high resolution traffic measurements have revealed that network traffic appears to be statistically self-similar. Thus, traffic prediction plays an important role in network management. Least mean kurtosis （LMK） based on QPSO, which can obtain signal error ratio less than LMK, is proposed to predict the self similar traffic. The simulation results with the real traffic traces show the accuracy efficiency of the model.

关 键 词：自相似性 最小均值峰度 QPSO算法 流量预测 信噪比 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]