微信流量特性分析与建模

微信是现代互联网的主要应用之一,到目前为止有关微信流量特性分析与建模的研究较少.本文以微信流量为研究对象,分析验证微信流量同时具有自相似性和突发性.针对这两种特性进行微信流量建模,采用线性分形稳定噪声模型刻画微信流量特性,完成了模型的参数估算和效果分析.本文的研究成果是后续的网络性能分析、网络流量监管等的基础.     

自动驾驶中量子认知与决策问题研究

自动驾驶车辆对人类驾驶车辆和行人的意图估计及其相互作用研究是极其重要的,现有的研究不能很好的解释人类交通参与者的不确定因素和非理性行为,这对研究自动驾驶车辆在真实道路交通场景中运行形成了阻碍,本文基于量子理论和锚定效应,针对自动驾驶车辆右转时与非机动车和行人交互场景,构建量子决策模型.仿真分析和数据集实验证明了在与人类交通参与者进行交互时,锚定效应下的量子决策模型可以考虑存在不确定性因素和非理性行为时进行加速或减速的决策,且相比于累积前景理论模型(CPT)更加贴合实际情况.     

一种网络流量监控算法设计

网络流量监控是实现网络性能分析和监控的重要手段,文章阐述了采用排队论建立网络流量分析的基本模型,得出网络的流量预测方式和稳态拥塞率求解公式,结合常用的网络流量监控参数,合理的实现对网络流量的估算和监控过程。     

Available bandwidth estimation for the network paths with multiple tight links and bursty traffic

Available bandwidth (ABW) estimation is useful for various applications such as network management, traffic engineering, and rate-based multimedia streaming. Most of the ABW estimation methods are based on the fluid cross-traffic model. Inevitably, their estimation accuracy is limited in the network environments with bursty cross-traffic. In this paper, we apply packet trains (a series of probing packets) and a modified Ping to probe the ABW of a network path. Our proposed probing method can identify several tight links along a path and can infer their individual ABWs. The ABW estimation algorithm developed in this study, GNAPP, is also based on the fluid traffic model, but it can effectively filter out probing noise incurred in networks that carry bursty traffic. The algorithm employs not only the gaps of any two consecutive probing packets but also those of nonadjacent probing packets for ABW estimation. Thus, the number of samples for ABW estimation increases significantly without resorting to sending more probing packets and the estimation efficiency and accuracy are improved. In addition, two-stage filtering and moving averages are used in GNAPP for reducing estimation errors. Numerical results demonstrate that the estimation scheme based on GNAPP can achieve good accuracy even when the traffic is bursty and there are multiple tight links on the path being observed. Thus, it outperforms other well-known ABW estimation tools.     

网络流量测量中相关参数的自回归预测分析

具体分析了两个参数的自回归预测模型。通过分析数据源NLANR给出的真实网络流量数据，比较SCV和分组数的预测效果，以及对网络流量估计的影响。通过大量模拟实验和数据分析得出了文献【1】预测模型中相关参数结果，对于在网络流量测量中真正应用该模型提供了重要依据。     

基于交通流特征的实时行车速度估计方法

针对城市道路的实时行车速度估计问题,提出一种行车速度实时估计模型,采用基于城市交通流特征匹配方法,通过对交通流特征建立有效索引保证行车速度估计的实时性。利用在杭州市区部分主干道采集的数据作为测试集,与数据回归法以及主曲线建模法进行对比,结果表明,本模型在行车速度估计方面具有实用性和优越性。     

基于层叠模型的网络流量异常检测方法*

进行网络流量异常检测,需要对正常流量行为建立准确的模型,根据异常流量与正常模型间的偏离程度作出判断。针对现有网络流量模型中自相似模型与多分形模型无法全面刻画流量特征的不足,提出了一种基于流量层叠模型分析的异常检测算法,采用层叠模型对整个时间尺度上的流量特征进行更准确的描述,并运用小波变换对流量的层叠模型进行估计,分析异常流量对模型估计的影响,提出统计累计偏离量进行异常流量检测的方法。仿真结果表明,该方法能够有效检测出基于自相似Hurst系数方法不能检测的弱异常以及未明显影响Hurst系数变化的异常流。     

自相似参数辨识与汇聚无线业务尺度特性分析

Hurst参数是衡量网络流量自相似程度和突发性的重要参数,在时域R/S统计、方差 - 时间图法和频域周期图法的基础上,提出一种最优化线性回归小波模型,实现小波域内Hurst参数的准确有效快速辨识.研究了WLAN中多个输入业务源的汇聚过程以及汇聚的多输入自相似业务源统计特性.仿真实验比较了传统的以及基于最优化线性回归小波模型的Hurst参数辨识方法,验证了理论分析中汇聚自相似业务也呈现自相似性的结论,且仿真结果表明,汇聚业务的突发性得到加强而不是削弱.研究结论对网络流量的准确建模以及网络传输中流量控制和优化网络资源配置以及提高网络性能具有重要作用.     

Traffic modeling, prediction, and congestion control for high-speednetworks: a fuzzy AR approach

In this study, a fuzzy autoregressive (fuzzy-AR) model is proposed to describe the traffic characteristics of high-speed networks. The fuzzy-AR model approximates a nonlinear time-variant process with a combination of several linear local AR processes using a fuzzy clustering method. We propose that the use of this fuzzy-AR model has greater potential for congestion control of packet network traffic. The parameter estimation problem in fuzzy-AR modeling is treated by a clustering algorithm developed from actual traffic data in high-speed networks. Based on the adaptive AR-prediction model and queueing theory, a simple congestion control scheme is proposed to provide an efficient traffic management for high-speed networks. Finally, using the actual Ethernet-LAN packet traffic data, several examples are given to demonstrate the validity of this proposed method for high-speed network traffic control     

基于模拟退火的流量矩阵估计

OD（Origin-Destination）流量估计用以获得网络流量在各个OD对间的分布情况,在网络优化、管理和网络异常的检测与识别等方面具有重要意义。模拟退火算法是一种全局的最优化技术,运行效率高,将其应用于OD流估计中,有助于降低求解的复杂性,并取得较高精度。提出了一种基于模拟退火的流量矩阵估计方法,首先采用IPF算法（Iterative Proportional Fitting algorithm）校正后的历史均值作为模拟退火初始值;在模拟退火过程中,利用链路流量信息来缩小模拟退火解的搜索空间,以达到提高算法的估计精度及实时性的目的。采用Abilene网络实际数据的仿真结果表明,该文方法能够取得较高的OD流估计精度,且计算效率明显优于现有的广义重力模型方法。     

微观交通系统仿真模型可信性评价

交通系统仿真模型的可信性评价是合理、科学地运用交通系统仿真模型进行系统分析、预测以及辅助决策的关键环节。针对微观交通仿真模型开发者和使用者的不同需求,研究了交通仿真模型可信性评价内容及其与仿真模型校正、校核、验证和确认的关系;分析了三种可用于微观交通仿真模型可信性评价的方法：层次分析法、相似度法以及模糊综合评判。合肥市大东门区域的运用VISSIM软件通过实际案例分析表明,基于层次分析法的微观仿真的可信度评价方法是提高模型可用性的有效手段。     

A Multicommodity Integrated Freight Origin–destination Synthesis Model

This paper introduces a multi-commodity, single (generic) vehicle formulation of freight ODS model that combines a commodity-based model to estimate loaded truck trips and a complementary model of empty trips. This integration is important because explicit modeling of empty trips—that account for 30% to 40% of total truck trips—is required to avoid significant errors in the estimation of the directional traffic. The formulation is then applied to a case study. Two cases of the proposed model are studied. The first one uses total traffic in the estimation; while the second one is based on loaded and empty traffic. The results conclusively show that the models that consider an empty trip submodel significantly outperform the model that does not in their ability to replicate the observed traffic counts. The comparison between the results from the multi-commodity ODS and the single commodity ODS previously developed by the authors indicates that the multi-commodity formulation brings about substantial reductions in the error associated with the estimation of observed traffic counts. These reductions, in the order of 20% for empty traffic and 40% for loaded and total traffic, seem larger than the spurious improvement to be expected from the increased number of parameters, suggesting that the multi-commodity ODS formulation performs better. The results also show some minor improvements in the ability of the multi-commodity ODS formulation to estimate the OD matrices. In terms of the model's ability to correctly estimate the “true” value of the parameters of the models used, i.e., the parameter values estimated by calibrating the model directly from the OD data, it was found that the multicommodity ODS procedure is able to provide fairly good estimates Noortman and van Es's model parameters, though the parameters of the gravity models that came out to be quite different than the “true” values. The overall assessment of the formulation introduced here is that it represents a solid improvement with respect to comparable techniques.     

基于小波模糊神经网络的实时交通流预测

实时交通流预测是智能运输系统研究的重要内容之一.本文将小波分析的相关知识与模糊神经网络相结合,给出了基于小波模糊神经网络的交通流预测模型,采用小波函数作为模糊隶属度函数,用神经网络来实现模糊推理,完成对下一个周期性交通流的估计.同时,用遗传算法来优化整个网络,实测数据验证这种方法预测精度高,收敛过程平稳,适应性强.     

使用自负载降速率探针包列的可用带宽测量方法

可用带宽是一条网络链路或路径的剩余带宽,该指标对网络流量工程以及速率敏感应用具有重要意义。现有的可用带宽测量技术大多基于单跳或者流体背景流量模型,在突发背景流及多跳路径下健壮性不足。现提出一种使用自负载降速率探针包列（SLDRT）技术的可用带宽测量方法。基于NS-2的仿真实验表明,SLDRT在多跳路径并基于非流体流量模型的网络环境下比另一重要测量工具pathChirp具有更高的准确性,并且平均测量时间小于30 ms,证明其可以满足高速、高精度的可用带宽测量需求。     

Road scene analysis for determination of road traffic density

Road traffic density has always been a concern in large cities around the world, and many approaches were developed to assist in solving congestions related to slow traffic flow. This work proposes a congestion rate estimation approach that relies on real-time video scenes of road traffic, and was implemented and evaluated on eight different hotspots covering 33 different urban roads. The approach relies on road scene morphology for estimation of vehicles average speed along with measuring the overall video scenes randomness acting as a frame texture analysis indicator. Experimental results shows the feasibility of the proposed approach in reliably estimating traffic density and in providing an early warning to drivers on road conditions, thereby mitigating the negative effect of slow traffic flow on their daily lives.     

On the time scales in video traffic characterization for queueing behavior

To guarantee quality of service (QoS) in future integrated service networks, traffic sources must be characterized to capture the traffic characteristics relevant to network performance. Recent studies reveal that multimedia traffic shows burstiness over multiple time scales and long range dependence (LRD). While researchers agree on the importance of traffic correlation, there is no agreement on how much correlation should be incorporated into a traffic model for performance estimation and dimensioning of networks.In this article, we present an approach for defining a relevant time scale for the characterization of VBR video traffic in the sense of queueing delay. We first consider the Reich formula and characterize traffic by the Piecewise Linear Arrival Envelope Function (PLAEF). We then define the cutoff interval above which the correlation does not affect the queue buildup. The cutoff interval is the upper bound of the time scale which is required for the estimation of queue size and thus the characterization of VBR video traffic. We also give a procedure to approximate the empirical PLAEF with a concave function; this significantly simplifies the calculation in the estimation of the cutoff interval and delay bound with little estimation loss.We quantify the relationship between the time scale in the correlation of video traffic and the queue buildup using a set of experiments with traces of MPEG/JPEG-compressed video. We show that the critical interval, i.e. the range for the correlation relevant to the queueing delay, depends on the traffic load: as the traffic load increases, the range of the time scale required for estimation for queueing delay also increases. These results offer further insights into the implication of LRD in VBR video traffic.     

基于数据中心流量特征的端到端流量估计算法

数据中心是云计算等大型分布式计算服务的基础,有效地设计与管理数据中心需要遵循数据中心网络的端到端流量特征。然而直接地测量网络的端到端流量需要耗费巨大的软件成本和硬件成本,并且由于数据中心网络结构的特殊性,传统的计算机网络采用的流量估计方法也无法适用于现有的数据中心网络。为解决以上问题,首先依据数据中心的资源分配和链路利用率情况提取出网络的粗粒度流量特征,在此基础上提出一种基于重力模型和网络层析技术的数据中心端到端流量估计算法。与现有的流量推理算法Tomogravity和ELIA在NS3搭建的不同规模的数据中心网络中进行性能对比,实验结果表明,所提算法能有效地利用提取出的粗粒度流量特征,在保证计算效率的前提下将计算准确度大幅提升,可满足当前数据中心网络实时获取端到端流量数据的需求。     

Synergistic approaches to mobile intelligent transportation systems considering low penetration rate

This paper investigates the effect of low penetration rate on mobile phone-based traffic state estimation (M-TES) models. Synergistic approaches, including an appropriate genetic algorithm (GA) based velocity–density estimation model and a notable artificial neural network (ANN) based prediction method for unacceptably low penetration rate, are proposed. The GA-based traffic state estimation model not only improves the effectiveness but also reduces the critical penetration rate required in the M-TES model. When the critical penetration rate is reduced the error-tolerance and the scalability of the estimation model can be significantly improved. The ANN-based prediction approach is introduced to overcome the weakness remaining in the GA-based traffic state estimation model when the penetration rate becomes unacceptably low or unknown. In addition, the effect of related road segments on the prediction effectiveness is thoroughly discussed. This work, therefore, provides practical instructions in narrowing the search space for finding prediction rules of the ANN model, thus improving the computational performance without compromising the prediction accuracy. The experimental evaluations confirm the effectiveness as well as the robustness of the proposed approaches. As a result, this research contributes to accelerating the realization of mobile phone-based intelligent transportation systems (M-ITS) or, of the M-TES systems in specific, since the essential issue of low penetration rate has been solved.     

基于蚁群算法的IP网络流量矩阵估计

针对IP网络流量矩阵(TM)估计的高度病态性,导致很难精确估计网络流量矩阵,因此提出了一种基于蚁群优化(ACO)算法的IP网络流量矩阵估计方法。通过适当的建模,将流量矩阵估计问题转化为最优化问题,再通过蚁群算法求解模型,有效解决了网络流量矩阵估计。通过测试结果分析,与现存的方法相比,所提算法的精度比最大熵和二次规划稍差,但这两种方法复杂度太高,不适用于大规模网络,因此,在网络规模较大的情况下,算法是较优的,可提高流量矩阵估计的精度。     

A VoIP Traffic Identification Scheme Based on Host and Flow Behavior Analysis

With the development of network and multimedia coding techniques, more and more Voice over Internet Protocol (VoIP) applications have emerged. The traffic identification on VoIP applications becomes an important issue in network management and traffic analysis. In this paper, a new traffic identification scheme, which combines traffic flow statistic analysis with host behavior estimation, is proposed to identify the VoIP traffic at the transport layer of the Internet. The host IP addresses and the port numbers are examined as the host behavior to distinguish the VoIP traffic from traditional traffic flows. The packet size has been modeled by a function of entropy while the inter-packet time has been modeled by the self-adaptive estimation. The experiment results show that our scheme could obtain a stable performance. At the same time, the proposed scheme could maintain its validity when existing VoIP applications are updated or the new ones admitted. Both accuracy and flexibility can be improved.