基于FBM模型的自相似流量建模仿真

网络流量建模是网络规划与性能评价的重要基础,传统的业务模型大多基于泊松模型和马尔可夫排队模型,只具有短程相关性,随着网络业务的不断研究发现,实际网络业务流在很长的时间范围内都具有长程相关性,即一种自相似性。本文采用RMD算法和Fourier变换法对网络流量的自相似模型-FBM模型进行了建模及仿真研究,生成了所需的自相似流量序列。然后分别采用R/S法和方差时间图法对其进行自相似参数检测。结果验证了仿真算法所产生的序列存在着自相似性,并同时对RMD算法和Fourier变换法的优缺点进行了分析。     

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。     

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。     

基于FARIMA-GARCH模型的网络业务预测算法

网络流量的波动性与自相似特性为其精确预测提出了挑战。为此,提出了一种基于FARIMA-GARCH模型的预测算法。该算法首先利用分段双向CUSUM检测算法对流量序列的均值进行有效检测,并在此基础上将序列零均值化;然后采用限定搜索法对分数差分阶数进行精确估计;在获得模型参数后,使用FARIMA-GARCH模型对网络流量进行预测。仿真实验表明,限定搜索法能够获得比传统算法更高的估计精度。随后采用真实网络流量验证了预测算法的性能,在保持与FARIMA预测算法等价的时间复杂度下,其均方根和相对均方根误差与RBF神经网络预测算法相当,而高于FARIMA预测算法。同时,预测算法对突发流量的跟踪和预测性能明显优于对比算法,且有更好的区间估计性能。     

WFQ流量调度算法研究

高速包交换电路常常需要为各种不同要求的服务公平地分配带宽,在公平分配带宽的同时还需要满足这些服务的服务质量(QoS)参数.不同QoS需求的业务将被复用到同一条输出链路上,要为它们公平地分配带宽就需要用到各种各样的流量调度算法.加权公平队列(WFQ)是一种常用的流量调度算法.它不仅能保证带宽分配的公平性,而且具有较好的时延性能.文章较为详细地讨论了WFQ算法的基本原理.     

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。     

改进的基于小波变换和FARIMA模型的网络流量预测算法

提出了一种改进的基于小波变换和FARIMA模型的网络流量预测算法,先对经过预处理的流量进行小波分解,再进行Mallat算法单支重构,接着用FARIMA模型分别对重构后的单支进行预测,最后合成流量。该算法较之传统的首先用FARIMA模型对小波系数进行预测再进行小波重构的算法减小了预测误差。仿真实验也验证了改进算法的预测准确性。     

Network traffic prediction model based on improved VMD and PSO-ELM

The rapid update of computing power leads to exponential data traffic growth, and the incidence of network attacks is also increasing. It is significantly important to analyze and predict network traffic accurately in the early stage and take corresponding preventive measures. The existing network flow integrated forecasting models still have some bottlenecks that are difficult to solve, for example, the slow optimization speed of modal decomposition parameters, easy falling into local optimal solutions, the slow convergence speed of the training process, and poor generalization capability. In this paper, particle swarm optimization (PSO) is utilized to improve the parameters selection process of the variational mode decomposition (VMD) algorithm and the extreme learning machine (ELM) algorithm. First, the PSO-VMD combined with multi-scale permutation entropy (MPE) is utilized to decompose the original network flow, and multiple eigenmode components are obtained. Second, the PSO-ELM is utilized to train the network traffic prediction model, and the PSO parameters in PSO-ELM are updated through adaptive weight adjustment and synchronous learning factors to increase the training and prediction speed, and the component prediction results are reconstructed to get a high-precision network flow forecasting result. Finally, through the prediction and verification of the public network flow data of the WIDE backbone, the result of this experiment indicates that the VMD-PSO-ELM can break through the bottlenecks of slow optimization speed of VMD decomposition parameters, reduce the computational complexity of ELM, accelerate the convergence speed, and increase the forecasting accuracy.     

Data center traffic scheduling strategy based on Fibonacci tree optimization algorithm

To improve traffic scheduling capabilities in network provider data centers,both network structure and network traffic flow were considered at the same time.The analysis prediction and online scheduling mechanism was proposed in data center based on software defined networking (SDN).Aiming at the multi-dimensional,multi-constrained and multi-modal problems of traffic flow scheduling in data centers,the traffic flow scheduling strategy based on Fibonacci tree optimization (FTO) algorithm was proposed.FTO algorithm was embedded into two stages of analysis prediction and online scheduling,took it advantage of global local alternating and multi-model optimization characteristics,the optimal solution and suboptimal solutions of traffic scheduling had been got at one time.The emulator result shows that,the FTO traffic scheduling strategy can schedule traffic in data centers reasonably,which improves the load balancing capability of network providers' data centers effectively.     

Network traffic prediction method based on wavelet transform and multiple models fusion

Accurate prediction of network traffic is an important premise in network management and congestion control. In order to improve the prediction accuracy of network traffic, a prediction method based on wavelet transform and multiple models fusion is presented. Mallat wavelet transform algorithm is used to decompose and reconstruct the network traffic time series. The approximate and detailed components of the original network traffic can be obtained. The characteristics of approximate components and detail components are analyzed by Hurst exponent. Then, according to the different characteristics of the components, autoregressive integrated moving average model (ARIMA) is chosen as the prediction model for the approximate component. Least squares support vector machine (LSSVM) is used to predict detail component. Meanwhile, an improved particle swarm optimization (PSO) algorithm is proposed to optimize the parameters of the LSSVM model. Gauss‐Markov estimation algorithm is adapted to fuse the predicted values of multiple prediction models. The variance of fusion prediction error is smaller than that of single prediction model, and the prediction accuracy is improved. Two actual datasets of network traffic are studied. Compared with other state‐of‐the‐art models, the case study results indicate that the proposed prediction method has a better prediction effect.     

无线网关对自相似业务流的影响

在分析具有自相似性的有线网络业务流经无线网关转发后的变化规律的基础上,经过仿真研究,结果表明,当网关工作在"实时转发"模式或"功率控制"模式时,若缓冲区大小与无线信道容量近似相等,无线网关对业务流的自相似性不会产生影响;当网关工作在"功率控制"模式时,若缓冲区大小远大于无线信道容量,EAFRP模型的拖尾系数与自相似程度增大.     

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。      

基于优化决策树的时延敏感流智能感知调度

目前流量调度策略无法做到智能按需化,尤其对于网络突发故障造成的拥塞以及高价值业务的护航场景,无法按需保障时延敏感的业务体验。通过分析研究不同网络业务流量时延敏感性属性需求,探索挖掘不同网络业务流量的行为特征与其时延敏感性需求之间的内在关联关系。然后利用AI技术对这种内在的关联关系进行学习,构建其映射关系,实现了时延敏感流智能感知调度。同时,考虑AI模型的可解释性及可部署性实际问题,采用强化学习剪枝优化可解释性决策树模型,提高模型的鲁棒性同时使模型更轻量化,易于设备部署实现。通过真实网络流量实验,强化学习优化后的决策树模型在单次推理情形下感知正确率提高1.75%,推理速度提升约30%;同时,实验也证明了使用局部微观统计特征多次推理有助于提高模型感知正确率。在所有实验中,强化学习优化的决策树模型规模缩小了60.0%～87.2%,并且Saras比Q-learning具有更好的优化表现。     

基于粒子群优化算法的数据中心网络流量调度策略

为解决当前数据中心网络存在链路负载不均衡及带宽资源浪费问题,提出了一种基于粒子群优化算法的流量调度策略.该策略结合软件定义网络控制器可获取全局网络拓扑信息的特性,依据当前链路带宽资源状况及网络流量的带宽需求建立目标函数.首先,根据流的源地址和目的地址找出最短路径集,通过定义粒子聚合度判断算法是否有陷入局部最优的趋势;然...     

基于LSTM流量预测的路由规划和切换

随着近年网络技术的不断发展和演进,用户对网络资源的需求体量越来越大,内容越来越复杂,传统的网络架构很难满足当前灵活的网络需求。软件定义网络的革命性技术思想赋予了网络可编程性和可演进性,实现网络资源更灵活的管理。为了提高网络资源利用率,提出了基于LSTM模型的流量预测,为路由规划提供理论依据。通过提前预测网络流量,在流量激增之前进行防御措施保证网络的安全性和稳定性。实现基于流量预测的路由规划、无损路径切换等功能。     

基于自相似业务的网络TCP拥塞控制算法

本文通过对自相似网络模型的分析,采用TCP协议可以直接观测的回环时间（Bound Trip Time)来进行拥塞程度预测,提出一种改进的TCP拥塞控制算法。仿真证明,改进算法在一定程度上提高了网络的整体性能,在自相似程度较高时尤为明显。     

业务优先级感知的网络流量调度机制

针对网络中业务数据流过大、分布不均匀所造成的网络拥塞,提出一种优先级感知的动态网络流量调度机制.利用令牌桶算法,根据业务优先级的不同为不同业务分配不同速率的令牌,以实现业务优先级的划分;综合考虑业务的优先级及用户节点剩余缓存空间,对不同的业务采取不同的处理方式;同时,以流量到达因素、服务因素及节点缓存为指标定义了一种网络流量调度机制性能指标——分组丢失率.数值结果表明,所提机制可以对网络中业务优先级进行合理的划分,从而有效利用网络资源,预防网络拥塞,提升网络性能,为用户提供更加稳定可靠的网络服务.     

基于量子通信的电力业务调度算法

量子通信能够有效提高电力业务传输的可靠性与安全性,但由于量子密钥成码率低,难以满足重要电力业务的加密需求,因此,需要一种队列调度算法对量子通信中的待加密电力业务进行合理调度。提出了一种改进的加权公平队列（weighted fair queuing,WFQ）算法LD-WFQ,算法通过估计待加密数据分组的预计耗时,优先处理即将超时的待加密数据分组,在保持高优先级业务量子加密时延达标率的基础上,有效降低了低优先级业务的量子加密超时率。与WFQ算法进行仿真对比,结果证明了LD-WFQ算法的优越性。     

A network traffic prediction model of smart substation based on IGSA‐WNN

The network traffic prediction of a smart substation is key in strengthening its system security protection. To improve the performance of its traffic prediction, in this paper, we propose an improved gravitational search algorithm (IGSA), then introduce the IGSA into a wavelet neural network (WNN), iteratively optimize the initial connection weighting, scalability factor, and shift factor, and establish a smart substation network traffic prediction model based on the IGSA‐WNN. A comparative analysis of the experimental results shows that the performance of the IGSA‐WNN‐based prediction model further improves the convergence velocity and prediction accuracy, and that the proposed model solves the deficiency issues of the original WNN, such as slow convergence velocity and ease of falling into a locally optimal solution; thus, it is a better smart substation network traffic prediction model.