IP骨干网流量的自动化预测

高效、可靠的网络流量预测是网络规划、扩容建设的基础。互联网流量目前缺乏完备的理论模型,行业内大多根据工程实践特点,设计简化可操作的预测模型以满足IP网络规划需求。首先根据中国电信自身IP骨干网流量预测工作的需求及特点,使用时间序列分析的多因子回归模型和函数自适应模型对IP骨干网流量进行分析和预测,基于大量现网实际数据的仿真运算,对比两种模型的特点、优劣和适用场景,提出了一种预测模型选择和参数优化的原则和方法。在此基础上,构建了可以满足百千量级时间序列要求的自动化流量预测系统,极大简化并提升了流量预测工作的效率。最后,展望了未来IP流量预测工作的延展方向和关注重点。     

神经网络在无线网络流量预测中的应用

无线互联网的流量数据非常分离且极其不稳定,混沌理论在其身上体现得特别明显,因此对无线网络流量进行预测具有一定难度。该文使用BP神经网络建立预测模型,在常规神经网络系统进行训练之前,需要对系统内部各个层次之间的连接权值以及阈值范围实行初始化操作,但是此操作将会影响神经网络最终收敛速度,有可能造成最终结果为非最优解,使得流量预测结果不是很理想。因此这里使用布谷鸟搜索优化方式对神经网络系统内各层之间链接值与阈值进行初始化操作,提高系统预测精度。该文使用遗传优化神经网络算法和粒子群优化神经网络算法建立同样的预测模型,并与该文研究的预测模型进行对比。实例分析结果表明,初期预测结果精度较高,与实际值比较吻合,但测试数据越靠后,预测值越不稳定,这主要是累计误差造成的。但总的来说,该文使用的布谷鸟优化BP神经网络预测模型的预测性能要优于由遗传算法和粒子群算法优化的BP神经网络。     

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.     

一种融合型光网络单元的设计与实现

为了实现EPON系统和WiFi技术的网络融合,提出了一种基于ARM9 S3C2440的融合型光网络单元的设计方案.详细介绍了这种融合型光网络单元的硬件结构及其关键的功能模块、软件设计及其主要功能,并且分别在无线接入和光信号传输方面测试了融合型光网络单元的性能.性能测试结果表明:融合型光网络单元在无线信号连通性和光信号的上行传输性能方面都能满足实际应用的需求.     

业务流预测在网络编码平台的实现及验证

网络编码由于可以利用无线信道的广播特性带来的空间冗余条件,在无线自组织网络中的应用显示了巨大的潜力。但TCP业务的机会网络编码在无线自组织网络中获得的增益却很小。基于这一问题,提出对TCP业务流具有的自相似性进行FARIMA模型建模,进行数据包到达时间的预测,从而为路由节点根据网络状态进行正确的编码等待决定提供依据,仿真结果验证了TCP业务流的自相似性,预测算法具有良好的预测结果。文中详细介绍了预测算法在COPE网络编码的NS2平台中的具体实现流程。     

Video traffic prediction using source information for renegotiatedCBR

A prediction method for variable bit rate video traffic based on source information is presented. By analysing the source before encoding, any changes in the source traffic are detected and the bit rate of the current frame is predicted. In addition, a new channel rate for renegotiated constant bit rate service can be determined for future frames using the predicted bit rate and a bit rate updating algorithm. Simulation results show that the proposed method achieves a better prediction and requires a smaller buffer than the conventional approach     

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.     

一种新的卫星通信网流量预测算法

在通信网络的设计中,使用基于流量预测的网络规划已成为LTE发展的必然趋势。与地面网络不同,卫星网络由于受资源受限和拓扑时变的不利影响,其流量预测算法必须能兼顾精度和效率,这令传统的地面网络预测方法已不再适用。为了解决以上问题,提出了一种新的基于小波回声状态网络的流量预测算法,该算法通过小波多尺度分解的信号处理方法屏蔽了网络流量的噪声,而后结合了无反馈的回声状态网络联合进行预测。仿真证明,新算法相比传统算法能大幅提升网络流量的预测精度和运行效率,为卫星网络的流量规划提供了强有力的决策支持。     

Evaluation of neural network architectures for MPEG-4 video traffic prediction

Multimedia applications and particularly MPEG-coded video streams are becoming major traffic components in high speed networks. Traffic prediction is important in enhancing the reliable operation over these networks. However, MPEG video traffic exhibits periodic correlation structure and a complex bit rate distribution, making prediction difficult. Neural networks can effectively be used to overcome such problem. In the literature, the problem has been mostly evaluated using standard feed-forward neural networks. However, a significant improvement can be expected using different types of neural networks. In this paper, six separate neural network predictors (including feed-forward) that can predict the basic frame types of MPEG-4: I, P, and B are developed and evaluated using long entertainment and broadcast video sequences. The performance is also compared to the widely used linear predictor. Comparison with results published in a recent work is also presented.     

应用梯度提升树的小区域无线网络多标签流量预测

当前无线网络流量地理分布不均且可用网络资源有限,因而开展拆闲补忙工作极为必要。为合理投放无线网络资源以保证网络性能,提出了一种针对小区域范围的多标签流量预测算法。该算法结合历史流量信息,根据无线用户偏好特性建立多标签流量预测模型,并通过梯度提升树算法得到预测结果。仿真结果表明,相比于广泛应用的移动平均自回归(Autoregressive Integrated Moving Average, ARIMA)和神经网络预测方法,多标签预测模型在对小区域突发性流量的预测上具有很大的优越性。     

Designing VLSI network nodes to reduce memory traffic in a shared memory parallel computer

Serialization of memory access can be a critical bottleneck in shared memory parallel computers. The NYU Ultracomputer, a large-scale MIMD (multiple instruction stream, multiple data stream) shared memory architecture, may be viewed as a column of processors and a column of memory modules connected by a rectangular network of enhanced 2×2 buffered crossbars. These VLSI nodes enable the network to combine multiple requests directed at the same memory location. Such requests include a new coordination primitive, fetch- and-add, which permits task coordination to be achieved in a highly parallel manner. Processing within the network is used to reduce serialization at the memory modules.To avoid large network latency, the VLSI network nodes must be high-performance components. Design tradeoffs between architectural features, asymptotic performance requirements, cycle time, and packaging limitations are complex. This report sketches the Ultracomputer architecture and discusses the issues involved in the design of the VLSI enhanced buffered crossbars which are the key element in reducing serialization.This work was supported in part by the Applied Mathematical Sciences subprogram of the Office of Energy Research, U.S. Department of Energy, under contract number DEA-C0276-ER03077-V, and in part by the National Science Foundation, under grant number DCR-8413359.     

中国移动互联网省网数据流量增长分析预测

本文从推动互联网发展的关键因素入手,论述了视频、移动、云计算对互联网流量的主要影响,从而逐步分析了中国移动互联网省网数据的流量计算模型,并给出建设意见。     

Quality of experience and quality of service-aware handover for video transmission in heterogeneous networks

Heterogeneous networks offer a wide range of multimedia services, such as entertainment, advertising, and video conferences. In this multimedia scenario, users can access video content via heterogeneous wireless networks, such as LTE macro and small cells. Users also expect to receive real-time videos with Quality of Experience (QoE) support, which is a challenging task due to the great diversity of radio base stations in such heterogeneous environments. In this article, we introduce a Quality of Service (QoS-)/QoE- and Radio-aware (SER) handover management algorithm for heterogeneous networks to provide video dissemination with QoS/QoE support. SER algorithm considers the analytic hierarchy process (AHP) to adjust the degree of importance of each criterion in order to select the appropriate radio base station that the mobile node must connect, allowing efficient handover decision making for video transmission with high user experience. Simulation results show that the SER algorithm delivered videos with significant improvement on QoE than existing handover algorithms.     

Semi-supervised internet network traffic classification using a Gaussian mixture model

With a dramatic increase in the number and variety of applications running over the internet, it is very important to be capable of dynamically identifying and classifying flows/traffic according to their network applications. Meanwhile, internet application classification is fundamental to numerous network activities. In this paper, we present a novel methodology for identifying different internet applications. The major contributions are: (1) we propose a Gaussian mixture model (GMM)-based semi-supervised classification system to identify different internet applications; (2) we achieve an optimum configuration for the GMM-based semi-supervised classification system. The effectiveness of these proposed approaches is demonstrated through experimental results.     

自相似业务在WDM网络中的动态性能分析

通过在几种颇具代表性的网络拓扑结构上进行仿真,对自相似业务在波分复用（WDM）光网络中的动态路由波长分配（RWA）问题进行了研究,分析了自相似业务在不同波长路由分配策略下的动态性能,并与相同条件下的泊松业务作了对比。     

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。     

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.     

Deep belief network-based link quality prediction for wireless sensor network

After analyzing the existing link quality prediction models,a link quality prediction model for wireless sensor network was proposed,which was based on deep belief network.Support vector classification was employed to estimate link quality,so as to get link quality levels.Deep belief network was applied in extracting the features of link quality,and softmax was taken to predict the next time link quality.In different scenarios,compared with the model of link quality prediction based on logistic regression,BP neural network and Bayesian network methods,the experimental results show that the proposed prediction model achieves better precision.     

下一代光传送网络中光转发单元的研究

文章研究了光转发单元(OTU)在下一代光传送网中的应用,并给出了一种基于ITU TG.709的OTU设计和工作流程[1].     

Performance analysis of an optical network employing waveband and traffic grooming

This paper analyses an optical network architecture composed by an arrangement of nodes equipped with multi-granular optical cross-connects (MG-OXCs) in addition to the usual optical cross-connects (OXCs). Then, selected network nodes can perform both waveband as well as traffic grooming operations and our goal is to assess the improvement on network performance brought by these additional capabilities. Specifically, the influence of the MG-OXC multi-granularity on the blocking probability is evaluated for 16 classes of service over a network based on the NSFNet topology. A mechanism of fairness in bandwidth capacity is also added to the connection admission control to manage the blocking probabilities of all kind of bandwidth requirements. Comprehensive computational simulation are carried out to compare eight distinct node architectures, showing that an adequate combination of waveband and single-wavelength ports of the MG-OXCs and OXCs allow a more efficient operation of a WDM optical network carrying multi-rate traffic.