Internet Research Needs Better Models: Topologies
Proposals and Projects
Modeling Topology of Large Internetworks at Georgia Tech.
"A primary objective of our
work is therefore to support the study of large internetworks through
scalable, realistic models of internetwork structure and applications."
GT-ITM topology generator.
Internet Topologies and Simulation at the University of Washington.
"Our goal is measurement, analysis and realistic generation of Internet
"We present new Internet mapping techniques that have enabled us to
directly measure router-level ISP topologies."
Topology Modeling Group at
BRITE topology generator:
"BRITE supports multiple generation models including models for flat AS,
flat Router and hierarchical topologies. Models can be enhanced by
assigning links attributes such as bandwidth and delay."
Topology Project at
the University of Michigan.
Papers on "several power-law relationships observed on Autonomous Systems' (AS)
connectivity degree, degree frequencies, and the neighborhood size".
The Inet topology generator:
"Inet, currently at version 3.0, is an Autonomous System (AS) level
Internet topology generator."
Tiers topology generator.
The Tiers topology generator is based on a Transit-Stub model.
Topologies for ISP Level Network Simulation.
"On this page we collect realistic network topologies and information
how to generate realistic topologies using topology generators. We
concentrate on POP and router level topologies (opposite to AS level
topologies), where one topology reflects the network of one provider."
The SSFnet web page has
for Tier-1 Internet provider networks with multiple peering points
Multi-AS topologies from BGP routing tables.
contains some topology generators using the
Stanford Graph Base (SGB) topology format, and
converts topologies from SGB to ns format.
Georgia Tech Internetwork Topology Models (GT-ITM)
generates topologies using the SGB format, and
sgb2ns converts topologies from SGB to ns format.
Internet Research Needs Better Models.
David Alderson, Lun Li, Walter Willinger, and John C. Doyle,
Understanding Internet Topology: Principles, Models, and Validation,
IEEE/ACM ToN, December 2005.
"For the Internet, an improved understanding of its physical
is possible by viewing the physical connectivity as an
annotated graph that delivers raw connectivity and bandwidth to
the upper layers in the TCP/IP protocol stack."
Oliver Heckmann, Michael Piringer, Jens Schmitt, Ralf Steinmetz,
On Realistic Network Topologies for Simulation,
SIGCOMM Workshop on Models, Methods and Tools for Reproducible Network
This paper considers the BRITE, TIERS, and GT-ITM topology generators.
"To conclude, Tiers was able ... to produce topologies that
had the highest similarity to the real world ISP topologies. ...
The level of similarity that could be
reached is quite high and indicates that hierarchical topology
are able to produce realistic POP level topologies."
New Directions and Half-Baked Ideas in Topology Modeling
IPAM workshop, June, 2002.
"BGP analysis - what are the modeling needs?"
"Peer-to-peer/overlay networks - what are the modeling needs?"
"What topology models are appropriate for wireless/ad-hoc/sensor
"Can a focus on the use of models lead to improved ability to evaluate
the quality of models?"
Anagnostakis, Greenwald, and Ryger,
On the Sensitivity of Network Simulation to Topology,
"We argue, first, that the barbell topology is not representative
of the Internet. In particular,
we report that a mesurable fraction of packets pass through
multiple congestion points. Second, we argue that the distinction
between the barbell topology and more complex topologies is relevant."
A congestion point is defined not
in terms of packet drop/mark rates, but in terms of the distribution
of queueing delay. The rest of the paper shows that if you use
total aggregate goodput as the metric, then, not too surprisingly,
odd things happen
in simulation scenarios with multiple congestion points.
Thanks to Senthilkumar Ayyasamy for additions to this page.
Proposed additions to this page can be sent to
This material is based in part upon work supported by the National Science
Foundation under Grant No. 0230921.
Any opinions, findings, and conclusions or recommendations expressed
in this material are those of the author(s) and do not necessarily
reflect the views of the National Science Foundation.
Last modified: January 2008.