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Swing: realistic and responsive network traffic generation

An end-to-end network traffic matrix is significantly helpful for network management and for Software Defined Networks SDN.

However, the end-to-end network traffic matrix's inferences and estimations are a challenging problem. Moreover, attaining the traffic matrix in high-speed networks for SDN is a prohibitive challenge. This paper investigates how to estimate and recover the end-to-end network traffic matrix in fine time granularity from the sampled traffic traces, which is a hard inverse problem. Different from previous methods, the fractal interpolation is used to reconstruct the finer-granularity network traffic.

Then, the cubic spline interpolation method is used to obtain the smooth reconstruction values. To attain an accurate the end-to-end network traffic in fine time granularity, we perform a weighted-geometric-average process for two interpolation results that are obtained. The simulation results show that our approaches are feasible and effective. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Competing interests: The authors have declared that no competing interests exist. With extensive applications of new generational information technologies, smart city, Internet of Things and Software Defined Networks SDN applications have explosively grown. High-speed backbone networks for supporting these applications carry huge network traffic loads. The backbone network scale has been expanding, and its speed has continuously improved [ 1 — 2 ].

These changes have brought great challenges to network measurement techniques. To measure the performance of networks, network operators need to collect traffic data from a large number of network test nodes [ 2 — 3 ]. However, only an OC48 link can collect hourly traffic up to GB. We must spend a lot of resources to store, transfer and handle the traffic data, since incorrect methods can cease network measurements. Therefore, in next-generation networks such as SDN, large-scale and high-speed sampling techniques have become one of main choices to measure and monitor communications networks [ 3 — 5 ].

These techniques significantly reduce the amount of measuring data and can also avoid adding the extra overhead brought by network measurements. However, sampling techniques can only obtain incomplete measured data that affect the correct analysis of network monitoring, network management, and performance assessment [ 4 — 8 ].

This may lead to an incorrect final decision. Hence, how to accurately derive the end-to-end traffic in finer time granularity from the limited sampling information has aroused extensive academic attention in recent years.

End-to-end traffic estimation has received extensive research attention and now has become the most important research topic of the IP network [ 9 — 11 ].

Cao et al. Xie et al. Zhang et al. Moreover, they also investigated the end-to-end network traffic in point-to-point and point-to-multipoint cases [ 15 ]. Juva et al. Additionally, Fumo et al. Stoev et al.

Some synthetic methods have been presented to generate end-to-end network traffic in order to conduct normal network activities [ 2 ]. Spatio-temporal compressive sensing sufficiently considers sparsity in the end-to-end network traffic [ 1 ]. Current studies have found that network traffic has long-range dependence and a self-similar nature [ 19 — 22 ].

In other words, when the network traffic is measured at different time scales milliseconds to hours , we find that network traffic has similar characteristics. Different these methods, SDN provides direct network flow measurements with a chance. Our motivations include several aspects.

Firstly, an accurate end-to-end network traffic matrix is very important and helpful for performing effective network managements and traffic engineering. Unfortunately, the direct measurements of them in the traditional network is prohibitive. Moreover, the end-to-end network traffic matrix's inferences and estimations are a huge challenge, and these methods have the larger estimation errors.

Secondly, in contrast to traditional networks, SDN can provide better solutions to traffic measurements of network flows, which allows to directly obtain the end-to-end traffic via reading the flow counter in the OpenFlow switch. Compared with the estimation methods based on the Simple Network Management Protocol SNMP link load measurements, this can improve the traffic measurement accuracy. However, under the SDN framework, to obtain the measurement results in fine time granularity is still complex, difficult and prohibitive, particularly for the high-speed network.

Thereby, based on SDN idea, it is necessary to construct a light-weight accurate method to obtain network traffic for network managements and designs. Thirdly, the sampling measurements for the end-to-end flow traffic can obtain their accurate sample value by the little measurement overhead, but the sampling results are too coarse for some applications such as billing, real time traffic scheduling, traffic anomaly detection, and so forth. Fortunately, the matrix complement can obtain the finer-granularity traffic value based on the end-to-end sampling.

Therefore, in this paper, underlying the SDN idea and framework, we consider how to construct a fine-granularity inference and estimations to the end-to-end network traffic from the sampling results in coarse time granularity. The rest of this paper is organized as follows. Section 2 introduces the system model of the end-to-end network traffic reconstruction. Section 3 describes our end-to-end network traffic reconstruction methods.

Section 4 presents the simulation results and analysis. It evaluates the reconstruction errors, the impact of the sampling time granularity on performance, and the average performance improvement. Finally, we conclude our work in Section 5. End-to-end traffic in a network reflects the volume of flows from the origin to the destination.

All end-to-end traffic describes their flows in the given network. This gives us the network-wide traffic information, which is very important for traffic engineering and network design. As mentioned in [ 1 , 13 , 23 — 24 ], all end-to-end traffic traverses the network according to the routing configuration.

Simultaneously, the end-to-end traffic flows on the same link and aggregates into linked traffic. Thus, end-to-end traffic, linked traffic, and the routing configuration information meet a linear constraint.

T is the matrix transpose operator. Generally, B is obtained by network topology and routing configuration information. In contrast to linked traffic z t , end-to-end traffic y t is more significant for network management and network operations. As shown in Eq 1 , end-to-end traffic is hidden in linked traffic. The inference method is primarily used to obtain the end-to-end traffic. Although some such approaches can obtain considerably accurate values for the end-to-end traffic, reconstruction errors always exist due to indirect measurements.

In this paper, we perform the direct measurement to quickly obtain the end-to-end traffic in the coarser time granularity. Fig 1 denotes a certain direct measurement case of end-to-end traffic in coarse time granularity. From Fig 1 , we can clearly see that the sampling measurement has loss, which cannot reflect the real end-to-end traffic in the given network.

Therefore, an approach to accurately recover and reconstruct the end-to-end traffic is significantly important. For SDN applications, network traffic has an important impact on the controller decisions [ 25 — 26 ]. Therefore, traffic recovery is important for network activities [ 27 — 28 ]. Fig 2 describes the system model of the end-to-end traffic recovery from coarse time granularity to fine.

In this model, we use inverse sampling technologies to attain the accurate end-to-end traffic in fine time granularity. We use the interpolation method to achieve the inverse sampling process, which can utilize the sampling values to reconstruct the fine-time-granularity traffic. Then, we make full use of the constraint relationship denoted in Eq 1 to attain the recovered end-to-end traffic and , which correspond to and , respectively.

Moreover, according to and , we employ the weighted geometric average to the needed end-to-end traffic. Eq 2 combines the advantages of the fractal interpolation and the cubic spline interpolation methods to attain the accurate reconstruction result. The fractal interpolation result can effectively characterize the self-similar nature of network traffic, while the cubic spline interpolation estimation can capture its highly dynamic change.

This guarantees that can generate the optimal reconstruction traffic. This section discusses our algorithm, which is based on fractal interpolation, cubic spline interpolation and the weighted geometric average algorithm, according to the system model shown in Fig 2. Fractal theory can describe the regularity of many irregular things and phenomena in the world.

At present, it has a wide range of applications in natural, economic and social sciences [ 19 ]. One of the most important properties of fractal theory is that it must have a self-similarity.

Self-similarity refers to the similarity existing between the whole and part of the system or between two separate parts. Current studies show that network traffic holds an obvious long-range dependent and self-similar nature. Furthermore, researchers have found that the packet lengths of TCP, FTP, video and other data have self-similarity that do not meet the exponential distribution.

Moreover, they also discovered that the packet length distribution was a step function, and the length of the flow followed a log-normal distribution [ 20 — 21 ]. From the view of network topology, the current topology between the entire network and the segment network has the characteristics of self-similarity.

From the view of the data contents from networks, network traffic is self-correlated. Furthermore, from the transfer process of the network traffic point of view including the application layer, the network transportation layer and the physical link layer , the self-similarity nature can be observed. The application layer is the data source of network communications [ 20 ]. It displays the self-similarity properties within a wide range of time, which is reflected in the distribution of its file size and the distribution of free time.

By analyzing the message size that comes from the traffic of the world wide web server, researchers have found that the distribution of the document obeys the heavy-tailed distribution [ 21 ]. Furthermore, through the analysis of Telnet and FTP, researchers have also found that the burst degree and the data size are also in line with the heavy-tailed distribution.

The transportation layer of networks contains a series of protocols, such as the use of flow control and congestion control, to implement the upper-layer services of network communication.

Both network retransmission and congestion control mechanisms become the factors that induce self-similarity. The transportation layers of networks realize transmissions and are dependent on the availability of network resources [ 21 ]. These resource include the buffering capacity and network bandwidth.

The buffer can temporarily accommodate multiple network traffic. Its size directly affects the loss rate of packets and queuing delay.

Generating Realistic Environments for Cyber Operations Development, Testing, and Training

This paper presents Swing, a closed-loop, network-responsive traffic generator that accurately captures the packet interactions of a range of applications using a simple structural model. Starting from observed traffic at a single point in the network, Swing automatically extracts distributions for user, application, and network behavior. It then generates live traffic corresponding to the underlying models in a network emulation environment running commodity network protocol stacks. We find that the generated traces are statistically similar to the original traces. Further, to the best of our knowledge, we are the first to reproduce burstiness in traffic across a range of timescales using a model applicable to a variety of network settings.

4. Internet Traffic Profiling

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Realistic and Responsive Network Traffic Generation

An end-to-end network traffic matrix is significantly helpful for network management and for Software Defined Networks SDN. However, the end-to-end network traffic matrix's inferences and estimations are a challenging problem. Moreover, attaining the traffic matrix in high-speed networks for SDN is a prohibitive challenge. This paper investigates how to estimate and recover the end-to-end network traffic matrix in fine time granularity from the sampled traffic traces, which is a hard inverse problem. Different from previous methods, the fractal interpolation is used to reconstruct the finer-granularity network traffic. Then, the cubic spline interpolation method is used to obtain the smooth reconstruction values. To attain an accurate the end-to-end network traffic in fine time granularity, we perform a weighted-geometric-average process for two interpolation results that are obtained.

Obtaining data for the training of failure prediction algorithms has long been an issue. A framework for automating the generation of this data for the training and deployment of these algorithms has recently been introduced. Unfortunately, the framework was only tested on a single deprecated operating system. In order to generalize the approach a few key functions must be performed, one of which being realistic workload generation. Unfortunately, a workload generator capable of generating sufficient workload has not been developed for a Microsoft Windows active directory environment. This paper introduces a tool that makes the implementation of this new framework possible on a modern Microsoft operating system.

Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Swing: Realistic and Responsive Network Traffic Generation Abstract: This paper presents Swing, a closed-loop, network-responsive traffic generator that accurately captures the packet interactions of a range of applications using a simple structural model. Starting from observed traffic at a single point in the network, Swing automatically extracts distributions for user, application, and network behavior. It then generates live traffic corresponding to the underlying models in a network emulation environment running commodity network protocol stacks.

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  1. Martina B.

    PDF | This paper presents Swing, a closed-loop, network-responsive traffic generator that accurately captures the packet interactions of a range.

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