TY - JOUR
T1 - Understanding the relationship between network traffic correlation and queueing behavior
T2 - A review based on the N-Burst ON/OFF model
AU - Schwefel, Hans Peter
AU - Antonios, Imad
AU - Lipsky, Lester
PY - 2017/10/1
Y1 - 2017/10/1
N2 - 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.
AB - 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.
KW - Long-range dependence
KW - ON/OFF models
KW - Power-tail distributions
KW - Queueing models
UR - http://www.scopus.com/inward/record.url?scp=85027555739&partnerID=8YFLogxK
U2 - 10.1016/j.peva.2017.07.002
DO - 10.1016/j.peva.2017.07.002
M3 - Journal article
AN - SCOPUS:85027555739
SN - 0166-5316
VL - 115
SP - 68
EP - 91
JO - Performance Evaluation
JF - Performance Evaluation
ER -