Generation of synthetic video traffic using time series

Generating traffic has always been an important part of network simulations but has turned to an even more challenging task with modern networks. The statistical properties of the input stochastic processes traced in the networks used all along Information Era turned out to be complicated and difficult to reproduce. Taking into account successful efforts in modeling Internet traffic with FARIMA time series models, this paper attempts to extend their applicability and employ them to generate synthetic video traffic. It is known that FARIMA can model both the Short Range (SRD) and Long Range Dependence (LRD) existing in video traffic; however the traces it produces fail to describe correctly the moments (mean, standard deviation, skewness, kurtosis) of the distribution behind the data. Since an efficient traffic generator should capture both the statistical properties and queuing behavior of video traffic we experiment with models such as FARIMA with Student's t errors and FARIMA-GARCH with Normal and Student's t errors, improving somewhat the accuracy of the generated traffic. Furthermore, the paper suggests the projection of the traces generated by a FARIMA model to values of a Lognormal distribution. It is shown that such a methodology produces synthetic traces that can emulate very closely the behavior of real traces. In order to quantify closeness the generated traces are fed into a simple FIFO queuing system with finite buffers, where loss probability is calculated and compared to that experienced by the corresponding real traces. Using five different real traces, MPEG-4 or H.263, it is shown that the proposed methodology produces traffic generators that can capture satisfactorily several statistical properties of the real traffic and also its queuing behavior for a wide range of buffer sizes and service rates.     

自相似网络的时延抖动性能仿真分析

自相似性对网络性能产生了影响是当前的研究热点。建立了一种基于FBM的自相似网络排队时延抖动分析模型,重点讨论了自相似流量作为输入时对排队系统的时延抖动的影响。对理论分形流量和实际测量流量进行了仿真实验,验证了结果的正确性和有效性。实验结果表明：自相似流量长相关强弱的程度对排队系统时延抖动特性具有非常不同的影响,尤其是在缓存较大的情况下。同时,还发现网络流量中长相关发生作用时状态转变与排队系统本身的参数也有关,这是新的发现,对实时业务的网络性能评价具有重要的参考意义。     

无线数据网络中的自相似性

在研究通信网络的流量控制及网络性能的时候．业务源模型是一个十分重要的组成部分。传统的业务流模型大多是基于泊松或贝努力过程的．而这些模型表现出的行为与实际网络的测量结果不符。大量业务的测量表明：在这些业务中普遍存在着自相似性(即长相关性)。采用自相似建模表征业务到达过程的长时相关特性具有较高的精度．其中Hurst系数是表征业务突发特性的重要参数。经研究发现，无线网络中仍然存在自相似性。考察CDPD(蜂窝数字分组数据网)的特性，发现自相似性对网络的排队性能有着显著的影响．可以通过增加队列长度，加大缓冲存储空间，实现降低延时。     

基于超爱尔兰分布的自相似网络排队模型研究

现有网络的业务流研究表明,多种通信业务中普遍存在自相似特性,但是如何对自相似业务流进行建模和分析一直是个难解问题。基于超爱尔兰拟合算法,建立了到达为泊松分布,服务时间为超爱尔兰分布的M/HErD/1/K自相似排队模型,推导得到呼叫损失概率、平均排队队长等排队性能的解析解。在实际业务环境下和传统的M/M/1/K排队模型进行了仿真对比,结果表明M/HErD/1/K模型不仅可有效表征业务流的快挂业务特性,而且能仿真出该网络在业务负荷较重时潜在的运营风险,对实际网络运营管理具有重要的指导作用。     

基于小波的多尺度网络流量预测模型

通过把ARIMA线性预测方法引入小波域内,提出一个基于多重分形小波模型的网络流量预测模型。通过对真实网络流量的仿真实验,结果表明该模型能够对网络流量进行比较精确的预测．     

Long-range dependence and on-chip processor traffic

Long-range dependence is a property of stochastic processes that has an important impact on network performance, especially on the buffer usage in routers. We analyze the presence of long-range dependence in on-chip processor traffic and we study the impact of long-range dependence on networks-on-chip. long-range dependence in communication traces of processor ips at the cycle-accurate level. We also study the impact of long-range dependence on a real network-on-chip using the SocLib simulation environment and traffic generators of our own. Our experiments show that long-range dependence is not an ubiquitous property of on-chip processor traffic and that its impact on the network-on-chip is highly correlated with the low level communication protocol used.     

Framework based on stochastic L-Systems for modeling IP traffic with multifractal behavior

This paper presents and compares a set of traffic models, and associated parameter fitting procedures, based on so-called stochastic L-Systems, which were introduced by biologist A. Lindenmayer as a method to model plant growth. Starting from an initial symbol, an L-System generates iteratively sequences of symbols, belonging to an alphabet, through successive application of production rules. In a traffic modeling context, the symbols are interpreted as packet arrival rates or mean packet sizes, and each iteration is associated to a finest time scale of the traffic. These models are able to capture the multiscaling and multifractal behavior sometimes observed in Internet traffic. We describe and compare four traffic models, one characterizing the packet arrival process, and the other three characterizing both the packet arrival and the packet size processes. The models are tested with several measured traffic traces: the well-known pOct Bellcore, a trace of aggregate WAN traffic and two traces of specific applications (Kazaa and Operation Flashing Point). We assess the multifractality of these traces using Linear Multiscale Diagrams. The traffic models are evaluated by comparing, for the measured traffic and for traffic generated according to the inferred models, the probability mass function, the autocovariance function and the queuing behavior. Our results show that the L-System based traffic models that characterize both the packet arrival and packet size processes can achieve very good fitting performance in terms of first- and second-order statistics and queuing behavior.     

Controlling mean queuing delay under multi-class bursty and correlated traffic

This paper presents an adaptive queue management scheme to maintain queuing delay in a router at a required level based on a comprehensive analytical model under aggregated Internet traffic flows from various traffic classes. The proposed scheme uses a closed-loop feedback control mechanism to constrain the average queuing delay by regulating traffic arrival rate implicitly through a movable queuing threshold. A discrete-time queuing model is developed to derive the relationship between average queuing delays and queuing thresholds based on a traffic model that models aggregated Internet traffic through superposition of N MMBP-2 arrival processes. The queuing threshold is adjusted dynamically with reference to the relationship derived in the analytical model and also feedback of average queuing delay measurement. Packets are dropped dynamically with respect to the changes of queuing threshold and the packet loss events serve as implicit congestion indicators. Matlab is used to perform queuing analysis and simulation. Statistical evaluation is performed to show the efficiency and accuracy of the analytical and simulation results.     

尺度变换下Internet流量的Gamma特征

网络流量特征分析与研究对流量产生和流量工程具有重要意义.现有工作集中在特定尺度下考察分析.基于不同地点采集得到的流量集合,采用尺度变换的方法,以时间间隔与数据包个数作为尺度变换的对象,分析不同粒度下流量特征,发现网络流量在特定尺度范围之上满足Gamma分布的特征.且在传输层上表现有所不同,TCP流量在特定尺度范围之上满足Gamma分布,UDP流量则在任何尺度都无明确的统计分布特征.     

Internet traffic modeling by means of Hidden Markov Models

In this work, we propose a Hidden Markov Model for Internet traffic sources at packet level, jointly analyzing Inter Packet Time and Packet Size. We give an analytical basis and the mathematical details regarding the model, and we test the flexibility of the proposed modeling approach with real traffic traces related to common Internet services with strong differences in terms of both applications/users and protocol behavior: SMTP, HTTP, a network game, and an instant messaging platform. The presented experimental analysis shows that, even maintaining a simple structure, the model is able to achieve good results in terms of estimation of statistical parameters and synthetic series generation, taking into account marginal distributions, mutual, and temporal dependencies. Moreover we show how, by exploiting such temporal dependencies, the model is able to perform short-term prediction by observing traffic from real sources.     

Analysis and modelling of traffic produced by adaptive HTTP-based video

The increase of HTTP-based video popularity causes that broadband and Internet service providers’ links transmit mainly multimedia content. Network planning, traffic engineering or congestion control requires an understanding of the statistical properties of network traffic; therefore, it is desirable to investigate the characteristic of traffic traces generated by systems which employ adaptive bit-rate streaming. Our first contribution is an investigation of traffic originating from 120 client-server pairs, situated in an emulated content distribution network, and multiplexed onto a single network link. We show that the structure of the traffic is distinct from the structure generated by the first and second generation of HTTP video systems, and furthermore, not similar to the structure of general Internet traffic. The obtained traffic exhibits negative and positive correlations, anti-persistence, and its distribution function is skewed to the right. Our second contribution is an approximation of the traffic by ARIMA/FARIMA processes blue and artificial neural networks. As we show, the obtained traffic models are able to enhance the performance of an adaptive streaming algorithm.     

网络流量的混沌特性及成因探讨

论证网络流量为一复杂的非线性系统,并运用分形、混沌等复杂性理论对网络流量的混沌特征进行实证研究,得出网络流量系统的分形特征、复杂性程度、系统演化类型,并得到相应的参数。在此基础上,用小波方法对网络流量序列进行了去长相关处理,探讨了网络流量的混沌与长相关的联系,最后指出这些结论的理论与实际意义。     

Performance Analysis of Network Traffic Predictors in the Cloud

Predicting the inherent traffic behaviour of a network is an essential task, which can be used for various purposes, such as monitoring and managing the network’s infrastructure. However, the recent surge of dynamic environments, such as Internet of Things and Cloud Computing have hampered this task. This means that the traffic on these networks is even more complex, displaying a nonlinear behaviour with specific aperiodic characteristics during daily operation. Traditional network traffic predictors are usually based on large historical data bases which are used to train algorithms. This may not be suitable for these highly volatile environments, where the strength of the force exerted in the interaction between past and current values may change quickly with time. In light of this, a taxonomy for network traffic prediction models, including the review of state of the art, is presented here. In addition, an analysis mechanism, focused on providing a standardized approach for evaluating the best candidate predictor models for these environments, is proposed. These contributions favour the analysis of the efficacy and efficiency of network traffic prediction among several prediction models in terms of accuracy, historical dependency, running time and computational overhead. An evaluation of several prediction mechanisms is performed by assessing the Normalized Mean Square Error and Mean Absolute Percent Error of the values predicted by using traces taken from two real case studies in cloud computing.     

IPv6网络中动态数据包调度模型研究

下一代互联网所承载的多样化的网络流量使得对于网络流量的测量、监测以及服务质量的控制变得越来越重要。对此提出了一种新型的数据包调度机制,每个数据包都有其预测的每跳队列延迟。通过将经验信息存储在IPv6的扩展头部中,使得其能在整个网络上传递。通过减少经验值,则可以达到减少数据包传输的抖动,提高服务质量。实验证明,整个系统运行健壮、效果良好。     

Implementation and validation of BLUE and PI queue disciplines in ns-3

High queuing delay arising out of the bufferbloat problem has reignited research in the area of Active Queue Management (AQM). The Internet Engineering Task Force (IETF) has created a new working group to discuss the deployment feasibility of existing and upcoming AQM algorithms (or queuing disciplines) in the Internet. Network simulations are essential to gain an accurate and deep understanding of the network algorithms before they are deployed in the Internet. ns-3 is among the most widely used network simulators, and the recent addition of Linux-like traffic control subsystem in ns-3 makes it highly suitable and reliable for studying the performance of queuing disciplines. However, the current traffic control subsystem in ns-3 has very few queuing disciplines. In an effort to provide support for more queuing disciplines and foster active research in this area, we implement two popular algorithms in ns-3: BLUE and Proportional Integral controller (PI). This paper discusses the implementation and validation of the proposed models in ns-3, and presents a detailed comparative study of both queuing disciplines based on the evaluation guidelines provided in RFC 7928.     

典型AQM算法的性能评价模型

利用GI/M/1/N排队系统和Internet业务流量自相似性的特点建立了一个评价AQM算法在非响应业务流量下性能的分析模型,提出了利用模型的分析计算结果而不是模拟或实验手段评价AQM算法性能的新方法.同模拟或实验手段相比,该方法能更深刻地刻画AQM算法在实际网络环境中的性能.用该模型分析比较3个经典的AQM算法——TD、RED和GRED的性能,所得的结果同其他研究者利用模拟或实验方法所得的结果一致.     

Analysis and modeling of World Wide Web traffic for capacity dimensioning of Internet access lines

A study on traffic characterization of the Internet is essential to design the Internet infrastructure. In this paper, we first characterize WWW (World Wide Web) traffic based on the access log data obtained at four different servers. We find that the document size, the request inter-arrival time and the access frequency of WWW traffic follow heavy-tail distributions. Namely, the document size and the request inter-arrival time follow log-normal distributions, and the access frequency does the Pareto distribution. For the request inter-arrival time, however, an exponential distribution becomes adequate if we are concerned with the busiest hours. Based on our analytic results, we next build an M/G/1/PS queuing model to discuss a design methodology of the Internet access network. The accuracy of our model is validated by comparing with the trace-driven simulation. We also investigate the effect of document caching at the Proxy server on the WWW traffic characteristics. The results show that the traffic volume is actually reduced by the document replacement policies, but the traffic characteristics are not much affected. It suggests that our modeling approach can be applied to the case with document caching, which is demonstrated by simulation experiments.     

Fuzzy-Based Dynamic Distributed Queue Scheduling for Packet Switched Networks

Addressing the problem of queue scheduling for the packet-switched system is a vital aspect of congestion control. In this paper, the fuzzy logic based decision method is adopted for queue scheduling in order to enforce some level of control for traffic of different quality of service requirements using predetermined values. The fuzzy scheduler proposed in this paper takes into account the dynamic nature of the Internet traffic with respect to its time-varying packet arrival process that affects the network states and performance. Three queues are defined, viz low, medium and high priority queues. The choice of prioritizing packets influences how queues are served. The fuzzy scheduler not only utilizes queue priority in the queue scheduling scheme, but also considers packet drop susceptibility and queue limit. Through simulation it is shown that the fuzzy scheduler is more appropriate for the dynamic nature of Internet traffic in a packet-switched system as compared with some existing queue scheduling methods. Results show that the scheduling strategy of the proposed fuzzy scheduler reduces packet drop, provides good link utilization and minimizes queue delay as compared with the priority queuing (PQ), first-in-first-out (FIFO), and weighted fair queuing (WFQ).     

Non-asymptotic end-to-end performance bounds for networks with long range dependent fBm cross traffic

Fractional Brownian motion (fBm) emerged as a useful model for self-similar and long-range dependent aggregate Internet traffic. Asymptotic, respectively, approximate performance measures are known for single queueing systems with fBm through traffic. In this paper end-to-end performance bounds for a through flow in a network of tandem queues under open-loop fBm cross traffic are derived. To this end, a rigorous sample path envelope for fBm is proven that complements previous approximate results. The sample path envelope and the concept of leftover service curves are employed to model the remaining service after scheduling fBm cross traffic at a queuing system. Using composition results for tandem systems from the stochastic network calculus end-to-end statistical performance bounds for individual flows in networks under fBm cross traffic are derived. The discovery is that these bounds grow in O(n(logn)^1/(2-2H)) for n systems in series where H is the Hurst parameter of the cross traffic. Explicit results on the impact of the variability and the burstiness of through and cross traffic on network performance are shown. Our analysis has direct implications on fundamental questions in network planning and service management.     

Traffic Modelling for Fast Action Network Games

A significant share of today's Internet traffic is generated by network gaming. This kind of traffic is interesting in regard to it's market potential as well as to it's real time requirements on the network. For the consideration of game traffic in network dimensioning, traffic models are required that allow to generate a characteristic load for analytical or simulative performance evaluation of networks. In this paper the fast action multiplayer game Counter Strike is evaluated based on one month of Internet traffic traces and traffic models for client and server are presented. The paper concludes with remarks on QoS metrics for an adequate assessment of performance evaluation results.