Network traffic simulation

About: Network traffic simulation is a research topic. Over the lifetime, 4535 publications have been published within this topic receiving 74606 citations.

Network Traffic Demand Prediction with Confidence

Abstract: Many network resource management solutions typically employ traffic prediction algorithms to improve the performance of a network. In this paper we extend a newly developed method for prediction with confidence to time series data and apply it to the network traffic demand prediction problem. We investigate the performance of the proposed algorithm on a number of publicly available network traffic demand datasets. The experimental results are very promising.

Modified Elman neural network and its application to network traffic prediction

Abstract: The predictability of network traffic is of significant interest in many domains, including adaptive applications, congestion control, admission control, wireless and network management. An accurate traffic prediction model should have the ability to capture the prominent traffic characteristics, e.g. short and long dependence, self similarity in large-time scale and multifractal in small-time scale. For these reasons time series models are introduced in network traffic simulation and prediction. Accurate traffic prediction may be used to optimally smooth delay sensitive traffic or dynamically allocate bandwidth to traffic streams. A modified Elman neural network model is proposed for the network system in this paper. Compared to the traditional Elman neural network model, the proposed model is nonlinear, multivariable and time-varying and has higher accuracy and better adaptability. By the model, a abnormal behavior of network traffic can be found on time through the test of adaptive boundary value. The experimental results show the model is effective and feasible for Network traffic prediction.

Linking network usage patterns to traffic Gaussianity fit

Abstract: Gaussian traffic models are widely used in the domain of network traffic modeling. The central assumption is that traffic aggregates are Gaussian distributed. Due to its importance, the Gaussian character of network traffic has been extensively assessed by researchers in the past years. In 2001, researchers showed that the property of Gaussianity can be disturbed by traffic bursts. However, assumptions on network infrastructure and traffic composition made by the authors back in 2001 are not consistent with those of today's networks. The goal of this paper is to study the impact of traffic bursts on the degree of Gaussianity of network traffic. We identify traffic bursts, uncover applications and hosts that generate them and, ultimately, relate these findings to the Gaussianity degree of the traffic expressed by a goodness-of-fit factor. In our analysis we use recent traffic captures from 2011 and 2012. Our results show that Gaussianity can be directly linked to the presence or absence of extreme traffic bursts. In addition, we also show that even in a more homogeneous network, where hosts have similar access speeds to the Internet, we can identify extreme traffic bursts that might compromise Gaussianity fit.

Flow-level traffic matrix generation for various data center networks

Abstract: The number of data centers deployed by governments, enterprises, and universities has been increased affected by the development of cloud computing technologies to reduce CAPAX and OPEX. Many architectures or topologies for data center networks have been proposed to address the diverse purposes and requirements. However, the construction of data centers incurs significant costs. Moreover, there are many technologies that can affect the structure of the data center. Before building a data center, it must be confirmed that it possesses the characteristics necessary to satisfy requirements. Efficient ways to find and confirm network characteristics include simulation and tests using a traffic generation method. Our proposed method is designed to generate network traffic that address many characteristics of data center networks explored by several studies. The proposed method generates network traffic utilizing flow-level traffic matrix, not directly generates packets. We used Python programming language to create traffic matrix and iPerf to generate network packets. To evaluate it, we compared the generation results to real network traffic collected from a data center network. The result shows that the generated traffic is similar with the real network traffic.

On the complexity of Internet traffic dynamics on its topology

Abstract: Most studies of Internet traffic rely on observations from a single link. The corresponding traffic dynamics has been studied for more than a decade and is well understood. The study of how traffic on the Internet topology, on the other hand, is poorly understood and has been largely limited to the distribution of traffic among source-destination pairs inside the studied network, also called the traffic matrix. In this paper, we make a first step towards understanding the way traffic gets distributed onto the whole topology of the Internet. For this, we rely on the traffic seen by a transit network, for a period of more than a week. As we are still at the stage of understanding the topological traffic distribution, we do not try to model the traffic dynamics. Rather we concentrate on understanding the complexity of describing the traffic observed by a transit network, how it maps onto the AS-level topology of the Internet and how it changes over time. For this, we rely on well-known tools of multi-variate analysis and multi-resolution analysis. Our first observation is that the structure of the Internet topology highly impacts the traffic distribution. Second, our attempts at compressing the traffic on the topology through dimension reduction suggests two options for traffic modeling: (1) to ignore links on the topology for which we do not see much traffic, or (2) to ignore time scales smaller than a few hours. In either case, important properties of the traffic might be lost, so might not be an option to build realistic models of Internet traffic. Realistic models of Internet traffic on the topology are not out of reach though. In this paper, we identify two properties such models should have: (1) use a compact representation of the dependencies of the traffic on the topology, and (2) be able to capture the complex multi-scale nature of traffic dynamics on different types of links.