Internet Access Traffic Measurement and Analysis

Gebert, Steffen; Pries, Rastin; Schlosser, Daniel; Heck, K.

doi:10.1007/978-3-642-28534-9_3

Cited by 66 publications

(50 citation statements)

References 6 publications

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“…However, for the same reason than previously, a single flow with constant throughput is not realistic (Gebert et al, 2011) (Olivier & Benameur, 2000).…”

Section: Network Throughput Estimation Toolsmentioning

confidence: 91%

“…A network capture shows often that mostly all the network resources are used by the TCP flows (Gebert et al, 2011). On one hand, the TCP protocol efficiently exploits the network and transmits data in an optimal time span.…”

Section: Network Throughput Estimation Toolsmentioning

confidence: 99%

“…This step may be done arbitrarily by choosing appropriate values or by selecting values from existing studies like (Gebert et al, 2011) (Olivier & Benameur, 2000).…”

Section: Statistic Profile Extraction From a Real Traffic Tracementioning

confidence: 99%

“…For instance, an Internet Service Provider (ISP) providing access for software engineering companies manage a different profile of data communications than an ISP for private individuals (Gebert et al, 2011). However, there are some common characteristics between both profiles.…”

Section: Introductionmentioning

confidence: 99%

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Realistic Network Traffic Profile Generation: Theory and Practice

Varet¹,

Larrieu²

2014

CIS

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Network engineers and designers need additional tools to generate network traffic in order to test and evaluate application performances or network provisioning for instance. In such a context, traffic characteristics are the very important part of the work. Indeed, it is quite easy to generate traffic but it is more difficult to produce traffic which can exhibit real characteristics such as the ones you can observe in the Internet. With the lack of adequate tools to generate data flows with "realistic behaviors" at the network or transport level, we needed to develop our tool entitled "SourcesOnOff". The emphasis of this article is on presenting this tool, how we implemented it and which methodology it follows to produce traffic with realistic characteristics. To do so, we chose to consider different stochastic processes in order to model the complexity of the different original traffics we wanted to replay. In our approach, we are able to consider several statistical laws and to combine their effects to model accurately the original behavior we analyzed in the real data. We then select the right parameters to consider as inputs for our SourcesOnOff tool. This approach gives really good traffic characteristics and, consequently, the generated traffic is really closed to reality as results presented at this end of this paper demonstrate it.

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“…However, for the same reason than previously, a single flow with constant throughput is not realistic (Gebert et al, 2011) (Olivier & Benameur, 2000).…”

Section: Network Throughput Estimation Toolsmentioning

confidence: 91%

Section: Network Throughput Estimation Toolsmentioning

confidence: 99%

“…This step may be done arbitrarily by choosing appropriate values or by selecting values from existing studies like (Gebert et al, 2011) (Olivier & Benameur, 2000).…”

Section: Statistic Profile Extraction From a Real Traffic Tracementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Realistic Network Traffic Profile Generation: Theory and Practice

Varet¹,

Larrieu²

2014

CIS

View full text Add to dashboard Cite

show abstract

“…Examples includes customized traffic shaping policies [31], smart content caching approaches [32], management of wide WiFi networks [33,34] and server provisioning [35]. Different approaches to perform device classification are available in the literature.…”

Section: Smartphone/laptop Classificationmentioning

confidence: 99%

Building up knowledge through passive WiFi probes

Redondi

Cesana

2018

Computer Communications

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Dynamic switch migration towards a scalable SDN control plane

Cheng

Chen

et al. 2016

Int J Communication

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SUMMARYDistributed control plane is a promising approach to scalable software-defined networking (SDN). Live migration of switches from controllers that are overloaded to those that are underutilized may be a solution to handle peak switch traffic using available control resource. However, such migration has to be performed with a well-designed mechanism to fully utilize available resources in all three resource dimensions: CPU, bandwidth, and memory. In this article, we first provide a resource model for SDN and reduce the switch migration decision to a centralized resource utilization maximization problem under constraints of CPU, bandwidth, and memory. Second, we show that the problem of maximizing resource utilization in an SDN is equivalent to that of maximizing game players' profits in the context of non-cooperative game theory. Taking controllers and switches as game players and commodities respectively, the player policy is how to migrate switches among the control plane. Finally, we implement a proof of concept, called GAME-Switch Migration (GAME-SM). The numerical experiments using Mininet emulator validate nice properties of our game model in enhancing the performance of control plane in SDN.

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Internet Access Traffic Measurement and Analysis

Cited by 66 publications

References 6 publications

Realistic Network Traffic Profile Generation: Theory and Practice

Realistic Network Traffic Profile Generation: Theory and Practice

Building up knowledge through passive WiFi probes

Dynamic switch migration towards a scalable SDN control plane

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