@article{schwefel_understanding_2017,
	title = {Understanding the relationship between network traffic correlation and queueing behavior: {A} review based on the {N}-{Burst} {ON}/{OFF} model},
	volume = {115},
	issn = {0166-5316},
	shorttitle = {Understanding the relationship between network traffic correlation and queueing behavior},
	url = {http://www.sciencedirect.com/science/article/pii/S0166531617302286},
	doi = {10.1016/j.peva.2017.07.002},
	abstract = {Understanding the impact of network traffic properties on performance behavior in bottleneck links or larger networks is of primary interest to traffic analysts and network designers. Among the contributing factors, variance and correlation properties have been thoroughly studied and a large set of individual results have been obtained. However, these individual contributing factors are not sufficient to predict performance behavior. In this paper we review a unifying and versatile class of ON/OFF models through which the relationship among these parameters can be characterized and their influence on network performance be understood. The analytic performance results from the model show that there is a radically different queueing behavior when the ON period duration follows truncated power-tail distributions (even if truncated), as opposed to model variants where these distribution types are used for the OFF periods. All these models create correlation functions that only decay slowly. This motivates the development of a simple data analysis scheme to distinguish performance relevant correlation. The scheme is described both for interarrival and count processes of traffic data and its effectiveness is shown using real data traces.},
	urldate = {2019-10-04},
	journal = {Performance Evaluation},
	author = {Schwefel, Hans-Peter and Antonios, Imad and Lipsky, Lester},
	month = oct,
	year = {2017},
	note = {Citation Key Alias: lens.org/016-979-057-202-359, pop00159
tex.type: [object Object]},
	keywords = {Long-range dependence, ON/OFF models, Power-tail distributions, Queueing models, dept.csc},
	pages = {68--91},
}

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