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Rapid model parameterization from traffic measurements

Rapid model parameterization from traffic measurements The utility of simulations and analysis heavily relies on good models of network traffic. While network traffic constantly is changing over time, existing approaches typically take years from collecting trace, analyzing the data to finally generating and implementing models. In this paper, we describe approaches and tools that support rapid parameterization of traffic models from live network measurements. Rather than treating measured traffic as a time-series of statistics, we utilize the traces to estimate end-user behavior and network conditions to generate application-level simulation models. We also show multi-scaling analytic techniques are helpful for debugging and validating the model. To demonstrate our approaches, we develop structural source-level models for web and FTP traffic and evaluate their accuracy by comparing the outputs of simulation against the original trace. We also compare our work with existing traffic generation tools and show our approach is more flexible in capturing the heterogeneity of traffic. Finally, we automate and integrate the process from trace analysis to model validation for easy model parameterization from new data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Modeling and Computer Simulation (TOMACS) Association for Computing Machinery

Rapid model parameterization from traffic measurements

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References (63)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2002 by ACM Inc.
ISSN
1049-3301
DOI
10.1145/643114.643117
Publisher site
See Article on Publisher Site

Abstract

The utility of simulations and analysis heavily relies on good models of network traffic. While network traffic constantly is changing over time, existing approaches typically take years from collecting trace, analyzing the data to finally generating and implementing models. In this paper, we describe approaches and tools that support rapid parameterization of traffic models from live network measurements. Rather than treating measured traffic as a time-series of statistics, we utilize the traces to estimate end-user behavior and network conditions to generate application-level simulation models. We also show multi-scaling analytic techniques are helpful for debugging and validating the model. To demonstrate our approaches, we develop structural source-level models for web and FTP traffic and evaluate their accuracy by comparing the outputs of simulation against the original trace. We also compare our work with existing traffic generation tools and show our approach is more flexible in capturing the heterogeneity of traffic. Finally, we automate and integrate the process from trace analysis to model validation for easy model parameterization from new data.

Journal

ACM Transactions on Modeling and Computer Simulation (TOMACS) – Association for Computing Machinery

Published: Jul 1, 2002

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