基于扩展卡尔曼滤波器的网络队列预测  

Queue Estimation in a Computer Network Based on an Extended Kalman Filter

作　　者：余菁菁 YU Jing-jing(Software Development Department,China People’s Property Insurance Co.,Ltd,Beijing 100022,China)

机构地区：[1]中国人民财产保险股份有限公司软件研发中心,北京100022

出　　处：《软件导刊》2020年第8期212-215,共4页Software Guide

摘　　要：为解决网络队列动态预测问题,提出一个网络系统在泊松分布流量和指数服务时间下的暂态队列行为预测模型并进行仿真验证。阐述基于扩展卡尔曼滤波(Kalman)的预测模型及其具体算法,结合网络中的数据流量特性,构建基于扩展卡尔曼滤波器的网络暂态队列预测模型,并根据仿真网络中的实际数据对模型进行验证。实验结果表明,所建立的网络暂态队列实时预测模型预测效果比较理想,基本与实时队列长度保持一致。因此,该模型可以较低的代价应用于网络中的动态路由算法及拥塞控制算法中。In order to solve the problem of network queue estimator dynamically,this paper presents the derivation of the transient queue behavior for a network with Poisson traffic and exponential service times and the result is then validated.The extended Kalman filter theory is presented and a network state estimator is designed using the transient queue behavior model combing with networks traf⁃fic trait.The behavior of the network state estimator is then investigated using traffic data from the simulated network.Simulation re⁃sults show that the proposed scheme have a good prediction of queue size in network,and the prediction results are consistent with the real-time queue length,so it can be used in the algorithm of dynamic routing and congestion control algorithm with low cost.

关 键 词：通信网络 扩展卡尔曼滤波器 泊松流量 OPNET仿真 预测模型 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]