一种基于混合模型的实时网络流量预测算法

流量预测是流量工程、拥塞控制和网络管理的核心问题.网络流量由大量的非线性变化部分和少量的但不可忽略的线性变化部分组成.现有的网络流量预测算法只是单一采用线性或者非线性的方法进行处理，这种片面性造成预测的准确度和实时性难以保证.针对网络流量的特点，提出了一种基于卡尔曼滤波和小波分析混合的流量预测算法.通过对网络流量的线性部分和非线性部分进行区分对待，从而提高预测的准确度和实时性.仿真结果表明，该算法与单一的线性预测算法和非线性预测算法相比，具有较高的预测精度和较好的实时性.

A Real Time Network Traffic Prediction Algorithm Based on Hybrid Model

Distributed applications use predictions of network traffic to sustain their performance by adapting their behaviors. It has been recognized that the network traffic consists of a majority of linear part and a small quantity of non-linear part which can not be neglected. However, existent network traffic prediction algorithms only utilize either linear or non-linear methods to solve the problem and can not provide enough accuracy and realtime due to the isolated adoption. A hybrid network traffic prediction algorithm, is provided, in which Kalman filter (KF) and wavelet are combined. Thus the linear part can be processed by KF and the non-linear part can be done by wavelet. Simulation results show that the proposed algorithm can guarantee higher accuracy and better realtime than those algorithms based singly on linear or non-linear method.