IP骨干网流量流向预测方法分析

从吸引系数法出发,基于中国电信IP骨干网实时监测到的流量流向数据,对吸引系数法的准确性进行相关的分析,在此基础上,提出在进行流量流向预测时应该遵循的方法和原则,并对未来在理论上对流量流向预测的思路进行探讨。     

基于时间序列的流量预测算法综述

随着我国城市化进程的加快,越来越多问题的出现对我国城市的市政系统有了更高的要求,为给相关单位的管控工作与规划提供可靠的依据,提高基于时间序列数据的流量预测的准确性是目前研究的重中之重.本文介绍了基于时间序列流量预测的传统方法以及它们的发展趋势,在此基础上指出了基于LSTM与传统神经网络的组合模型,组合模型作为一组相对比...     

线性流量预测模型

详细分析了线性流量预测的4种短相关模型：AR模型、MA模型、ARMA模型以及ARIMA模型,对其实现思想、建模过程和核心算法进行了具体的研究和比较,并使用SPSS进行建模分析。     

基于BP神经网络的流量预测分析

为提高校园IP网络流量预测的准确度,本文基于BP神经网络对未来时刻流量大小与历史数据间存在的相关性进行了拟合分析,通过数据预处理、模型参数选择、网络权重阈值训练等流程实现了对预测模型的建立,并利用仿真结果对比验证了该模型在网络流量值的预测中具有很好的准确度.     

IP城域网出口流量预测方法

介绍宽带流量预测方法,并分别使用历史趋势外推预测法和分业务预测求和法进行IP网络流量预测,最后对两种方法的优缺点进行比较。     

基于LSTM的移动通信基站流量预测

针对基站流量的周期性和波动性,提出了基于长短期记忆(LSTM)神经网络模型的移动通信基站流量预测方法.通过特有的三种门结构和记忆单元相互调节,利用LSTM可以同时获取短期特征和长期趋势这一特点,从而学习到基站流量的变化特征.实验结果表明,对比传统ARMIA预测模型,该方法在移动通信基站流量预测方面具有较高的准确性.     

基于流量预测的5G通信网络资源分配方法

为实现5G通信网络资源的最佳分配,提出基于流量预测的5G通信网络资源分配方法。在5G网络切片的资源分配情况下,形成基于Prophet-PFMGNet的5G通信网络流量预测模型,预测5G通信网络流量以及资源需求,采用布谷鸟优化资源分配结果,获取最佳5G通信网络资源分配。测试结果表明：该方法具备良好的网络流量预测效果,确定性相关系数取值均在0.92以上,可靠预测资源需求,应用后网络节点和链路两种资源的利用率均在0.93以上,用户的阻塞概率均低于0.06。     

基于改进神经网络的无线网络流量预测

考虑到无线网络流量具有极强的分散性、随机性以及混沌等特性,使用传统的ARIMA预测模型和BP神经网络模型难以对其进行精确的预测等,该文使用粒子群优化算法对BP神经网络预测模型进行优化以解决BP神经网络容易陷入局部最小值以及训练收敛速率低等问题,引入遗传算法中的自适应变异因子来以一定概率初始化部分变量解决粒子群优化算法会出现陷入局部最优解以及早熟收敛等问题。最后使用经典的CRAWDAD数据库中的无线网络流量数据对该文预测方法性能进行测试,使用稳定小波变换方法将无线网络流量数据分解,得到由1个近似分量以及3个细节分量组成的数据流。测试结果表明,该预测算法在预测性能上要优于ARIMA预测模型和BP神经网络模型。     

基于流量预测的 IP骨干网络优化

针对多媒体数据网络结构复杂、业务发展迅速的特点，提出一种基于业务流量预测分析的IP骨干网络优化方法，为网络规划设计人员提供一定的决策和设计参考依据。     

宽带IP城域网流量分析与预测

介绍了宽带IP城域网流量调查与分析的方法，提出了一种流量预测模型，确定节点的上行带宽与所承载用户数的关系，从而可以对宽带IP城域网各个层次的流量进行预测，为网络的建设和优化提供理论依据。     

Network traffic prediction based on a new time series model

Fast and accurate methods for predicting traffic properties and trend are essential for dynamic network resource management and congestion control. With the aim of performing online and feasible prediction of network traffic, this paper proposes a novel time series model, named adaptive autoregressive (AAR). This model is built upon an adaptive memory‐shortening technique and an adaptive‐order selection method originally developed by this study. Compared to the conventional one‐step ahead prediction using traditional Box–Jenkins time series models (e.g. AR, MA, ARMA, ARIMA and ARFIMA), performance results obtained from actual Internet traffic traces have demonstrated that the proposed AAR model is able to support online prediction of dynamic network traffic with reasonable accuracy and relatively low computation complexity. Copyright © 2005 John Wiley & Sons, Ltd.     

基于突发分离的自相似网络流量预测

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。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。      

WPANFIS: combine fuzzy neural network with multiresolution for network traffic prediction

A novel methodology for prediction of network traffic,WPANFIS,which relies on wavelet packet transform(WPT)for multi-resolution analysis and adaptive neuro-fuzzy inference system(ANFIS)is proposed in this article.The widespread existence of self-similarity in network traffic has been demonstrated in earlier studies,which exhibits both long range dependence(LRD)and short range dependence(SRD).Also,it has been shown that wavelet decomposition is an effective tool for LRD decorrelation.The new method uses WPT as extension of wavelet transform which can decoorrelate LRD and make more precisely partition in the high-frequency section of the original traffic.Then ANFIS which can extract useful information from the original traffic is implemented in this study for better prediction performance of each decomposed non-stationary wavelet coefficients.Simulation results show that the proposed WPANFIS can achieve high prediction accuracy in real network traffic environment.     

An adaptive network traffic prediction approach for LDoS attacks detection

Low‐rate denial of service (LDoS) attacks reduce throughput and degrade quality of service (QoS) of network services by sending out attack packets with relatively low average rate. LDoS attack flows are difficult to detect from normal traffic since it has the property of low average rate. The research on network traffic analysis and modeling shows that network traffic measurement data are irregular nonlinear time series. To characterize and analyze network traffic between attack and non‐attack situations, the adaptive normal and abnormal ν‐support vector regression (ν‐SVR) prediction models are constructed on the basis of the reconstructed phase space. In this paper, the dimension of reconstructed phase space for ν‐SVR is optimized by Bayesian information criteria method, and the parameter in the radial basis function is adaptively adjusted by minimizing the within‐class distance and maximizing the between‐class distance in the feature space. The nonthreshold decision function is obtained through calculating the prediction error of adaptive normal and abnormal ν‐SVR prediction models, which is adopted to detect LDoS attacks. Experiments in NS‐2 environment show that the adaptive ν‐SVR prediction model can effectively predict the network traffic measurement time series, and the probability distribution of time series generated by the adaptive ν‐SVR prediction model is quite similar to that of the network traffic measurement data. Experiments also clearly demonstrate the superiority of the proposed approach in LDoS attacks detection.     

基于混沌理论与改进回声状态网络的网络流量多步预测

网络流量预测是网络管理及网络拥塞控制的重要问题,针对该问题提出一种基于混沌理论与改进回声状态网络的网络流量预测方法。首先利用0-1混沌测试法与最大Lyapunov指数法对不同时间尺度下的网络流量样本数据进行分析,确定网络流量在不同时间尺度下都具有混沌特性。将相空间重构技术引入网络流量预测,通过C-C方法确定延迟时间,G-P算法确定嵌入维数。对网络流量时间序列进行相空间重构之后,利用一种改进的回声状态网络进行网络流量的多步预测。提出一种改进的和声搜索优化算法对回声状态网络的相关参数进行优化以提高预测精度。利用网络流量的公共数据集以及实际数据进行了仿真,结果表明,提出的预测方法具有更高的预测精度以及更小的预测误差。     

基于包容性检验和神经网络的网络流量预测模型

网络流量具有高度复杂的非线性特征,采用单一预测模型往往难以达到理想的预测效果,为此,提出一种包容性检验和BP神经网络相融合的网络流量预测模型（ET-BPNN）。首先采用多个单一模型对网络流量进行预测,然后通过包容性检验,根据t统计量检验选择最合适的基本模型,最后采用BP神经网络对基本模型预测结果进行组合得到最终预测结果。实验结果表明,相对于单一模型以及传统组合模型,ET-BPNN更加准确刻画了网络流量变化趋势,各项评价指标均达到更优,为实现网络流量准确预测提供了更为科学的方法。     

基于IP/MPLS网络的动态业务流量矩阵测量模型

IP网络动态业务流量矩阵的测量是业务量工程研究中的一个难点,本文提出了一种面向IP/MPLS骨干网络的基于LSP级的动态业务流量矩阵测量模型。该模型能够获取网络边界处对应于每一条LSP的路径转发信息,并根据每一条LSP上的测量结果,计算得知全网的业务流量矩阵。文中证明该测量模型是可行的,并给出了相关的算法及其性能分析。模型的优点是测量只在网络边界处进行而不涉及网络核心,故引起的网络开销较小。另外模型所需的算法复杂度低,仿真结果显示测量模型是有效的。     

SDON中基于贝叶斯模型的流量预测机制

SDON(软件定义光网络)是光网络发展的最新趋势。为了进一步提高 SDON 的智能化和对业务的动态支持,利用短期的业务流量特性预测较为长期的流量显得日益重要。文章提出了一种基于贝叶斯模型的流量预测机制,该机制充分利用贝叶斯模型对 SDON的业务流量进行长期预测。仿真结果验证了该机制的准确性和有效性。     

结合多重分形的网络流量非线性预测

通过分析树型多重分形结构的相关性发现，多重分形可以把非平稳且具有长相关（LRD）和分形特性的网络流量序列转化为可用短相关（SRD）模型表示的序列组。利用多重分形这种将时间序列分解为多层的能力，提出了一种结合多重分形的FIR神经网络流量预测模型（MF-FIR，multifractal FIR network）。MF-FIR合理地利用了流量序列的LRD信息，具有很好的多步预测性能，可以满足通信系统在线预测的要求。     

Attention-based spatio-temporal graph convolutional network considering external factors for multi-step traffic flow prediction

Traffic flow prediction is an important part of the intelligent transportation system. Accurate multi-step traffic flow prediction plays an important role in improving the operational efficiency of the traffic network. Since traffic flow data has complex spatio-temporal correlation and non-linearity, existing prediction methods are mainly accomplished through a combination of a Graph Convolutional Network (GCN) and a recurrent neural network. The combination strategy has an excellent performance in traffic prediction tasks. However, multi-step prediction error accumulates with the predicted step size. Some scholars use multiple sampling sequences to achieve more accurate prediction results. But it requires high hardware conditions and multiplied training time. Considering the spatiotemporal correlation of traffic flow and influence of external factors, we propose an Attention Based Spatio-Temporal Graph Convolutional Network considering External Factors (ABSTGCN-EF) for multi-step traffic flow prediction. This model models the traffic flow as diffusion on a digraph and extracts the spatial characteristics of traffic flow through GCN. We add meaningful time-slots attention to the encoder-decoder to form an Attention Encoder Network (AEN) to handle temporal correlation. The attention vector is used as a competitive choice to draw the correlation between predicted states and historical states. We considered the impact of three external factors (daytime, weekdays, and traffic accident markers) on the traffic flow prediction tasks. Experiments on two public data sets show that it makes sense to consider external factors. The prediction performance of our ABSTGCN-EF model achieves 7.2%–8.7% higher than the state-of-the-art baselines.