时间二维变化建模的网络流量多步预测方法  

Multi-step prediction method for network traffic based on temporal 2D-variation modeling

作　　者：宋文超 杨帆 邢泽华 张钰杰 SONG Wenchao;YANG Fan;XING Zehua;ZHANG Yujie(School of Telecommunications Engineering,Xidian University,Xi’an 710071,China)

机构地区：[1]西安电子科技大学通信工程学院,陕西西安710071

出　　处：《西安电子科技大学学报》2025年第1期22-36,共15页Journal of Xidian University

基　　金：国家重点研发计划(2020YFB1805600)。

摘　　要：准确预测网络流量的变化,可以帮助运营商提前进行资源分配和调度,最大程度减少网络拥塞。现有的网络流量多步预测方法难以捕获流量序列的长相关性,在多步预测任务上精度较低,基于此,提出了一种时间二维变化建模的网络流量多步预测方法。该方法首先利用门控循环单元对网络流量序列进行编码,以实现网络流量时间相关性的精准表征;然后利用网络流量周期特征对其进行重构,将一维的流量序列转化为二维,重构后的流量序列长度被压缩,特征更为集中,使得模型能够有效感知其长相关特征。最后通过新型卷积神经网络捕获重构后流量序列的二维特征,并进行加权融合得到最终的预测结果。仿真结果表明,相较于主流的网络流量多步预测方法,所提方法均方根误差至少降低约8.69%,平均绝对误差至少降低约8.96%,平均百分比误差至少降低约11.73%。实验结果说明所提方法能够有效挖掘网络流量长相关特征,在网络流量多步预测任务中具有更高的预测精度。Accurate prediction of network traffic variations can help operators allocate resources and schedule in advance,thus minimizing network congestion.Existing multi-step prediction methods for network traffic struggle to capture the long-range dependencies in traffic sequences,resulting in a low accuracy in multi-step prediction tasks.In response,a novel method using time two-dimensional-variation modeling for multi-step network traffic prediction is proposed which first encodes the network traffic sequence using Gated Recurrent Units(GRUs)to accurately represent the temporal correlations of network traffic and then reconstructs the traffic based on its periodic characteristics,transforming the one-dimensional traffic sequence into two dimensions.The reconstructed traffic sequence has a compressed length and more concentrated features,enabling the model to effectively perceive its long-range dependencies.Finally,a novel convolutional neural network captures the two-dimensional features of the reconstructed traffic sequence and performs weighted fusion to produce the final prediction results.Simulation results show that compared to mainstream multi-step network traffic prediction methods,the proposed method reduces the root mean square error by at least 8.69%,mean absolute error by at least 8.96%,and the mean absolute percentage error by at least 11.73%.Experimental results demonstrate that the proposed method can effectively mine long-range dependencies in network traffic,achieving a higher accuracy in multi-step traffic prediction tasks.

关 键 词：预测 网络管理 流量预测 时间二维变化建模 

分 类 号：TP393.06[自动化与计算机技术—计算机应用技术]