A Tool for RApid Model Parameterization and its Applications

A Tool for RApid Model Parameterization and its Applications

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

Kun-chan Lan and John Heidemann 2003. A Tool for RApid Model Parameterization and its Applications. Proceedings of the Workshop on Models, Methods and Tools for Reproducible Network Research (MoMeTools) (Karlsruhe, Germany, Aug. 2003), to appear.

Abstract

The utility of simulations and analysis heavily relies on good models of network traffic. However, it is difficult to model and simulate the Internet traffic because of the network’s great heterogeneity and rapid change. The statistical properties of Internet traffic not only constantly change over time but also vary in other dimensions such as locations and directions. Previously we have developed a tool RAMP that supports rapid parameterization of traffic models from live network measurements. In this paper, we first extend RAMP to support near-real-time trace-driven simulation. Next, we demonstrate the applications of RAMP via three case studies: generation of realistic traffic model for simulation, generation of high bandwidth synthetic network traces, and analysis and modeling of malicious traffic. Finally, we discuss some lessons we learned from using RAMP for traffic modeling.

Reference

@inproceedings{Lan03b,
  author = {Lan, Kun-chan and Heidemann, John},
  title = {A Tool for {RApid} Model Parameterization and its Applications},
  booktitle = {Proceedings of the  Workshop on Models, Methods and Tools for Reproducible Network Research (MoMeTools)},
  year = {2003},
  sortdate = {2003-08-01},
  project = {ant, saman},
  jsubject = {traffic_modeling},
  publisher = {ACM},
  address = {Karlsruhe, Germany},
  month = aug,
  pages = {to appear},
  location = {johnh: pafile},
  keywords = {RAMP, traffic modeling, on-line traffic modeling},
  url = {http://www.isi.edu/%7ejohnh/PAPERS/Lan03b.html},
  pdfurl = {http://www.isi.edu/%7ejohnh/PAPERS/Lan03b.pdf},
  myorganization = {USC/Information Sciences Institute},
  copyrightholder = {ACM},
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}

Copyright

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.