Rapid model parameteration from traffic measurement

Lan, Kun-Chan and Heidemann, John
USC/Information Sciences Institute


Kun-Chan Lan and John Heidemann 2002. Rapid model parameteration from traffic measurement. ACM Transactions on Modeling and Computer Simulations. 12, 3 (Jul. 2002), 201–229. [PDF]


The utility of simulations and analysis heavily relies on good models of network traffic. While network traffic constantly 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 tool 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.


  author = {Lan, Kun-Chan and Heidemann, John},
  title = {Rapid model parameteration from traffic measurement},
  journal = {ACM Transactions on Modeling and Computer Simulations},
  year = {2002},
  sortdate = {2002-07-01},
  project = {ant, saman},
  jsubject = {traffic_modeling},
  volume = {12},
  number = {3},
  month = jul,
  pages = {201--229},
  jlocation = {johnh: pafile},
  keywords = {ramp, model parameterization},
  url = {https://ant.isi.edu/%7ejohnh/PAPERS/Lan02c.html},
  pdfurl = {https://ant.isi.edu/%7ejohnh/PAPERS/Lan02c.pdf},
  otherurl = {http://portal.acm.org/citation.cfm?id=643117&coll=portal&dl=ACM&CFID=12087341&CFTOKEN=69397981},
  myorganization = {USC/Information Sciences Institute},
  copyrightholder = {
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