基于突发分离的自相似网络流量预测

Self-similar network traffic prediction based on burst decomposition

网络流量预测在网络拥塞控制及资源分配中起着至关重要的作用。对于具有自相似性的网络业务流量,由于其存在较强突发,传统预测方法的预测精度普遍较低。本文针对存在高突发的网络流量数据,提出了一种基于数据分离的流量预测方法。具体来说,在预测步骤前,本方法首先通过控制图将网络流量中难以预测的突发流量进行有效的分离,从而得到突发流量和非突发流量两部分数据。之后分别采用人工神经网络和自适应模板匹配方法实现对非突发流量和突发流量的预测。最后通过对两部分预测结果的合并得到最终的预测结果。基于实际流量数据的实验结果表明:相较于传统流量预测方法,本文所提出的方法具有更高的流量预测精度。

Traffic prediction plays an important role for congestion control and resource allocation of network manage- ment. For self-similar network traffic, in virtue of aggregated burstiness on all time scales, the prediction accuracy of tradi- tional traffic prediction methods is generally low. In order to deal with the burstiness in traffic data, a burst decomposition based traffic prediction method is proposed in this paper. More specifically, before performing traffic prediction, the burst will be separated from the traffic by control chart, which leads to the decomposition of original traffic into the burst part and non-burst part. Then Artificial Neural Network （ANN） based method and adaptive template matching based method are used for the prediction of non-burst part and burst part respectively. Finally, the prediction results of original traffic are ob- tained through combining the predictions of the two parts. The real traffic based experimental results show that the prediction accuracy of the proposed method is higher than that of traditional methods.