Mapping the Internet Topology Via Multiple Agents.
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Transcript of Mapping the Internet Topology Via Multiple Agents.
Mapping the Internet Topology Via Multiple Agents
What does the internet look like?
Why do we care?
• While communication protocols will work correctly on ANY topology
….they may not be efficient for some topologies
• Knowledge of the topology can aid in optimizing protocols
Topics
• Power laws in the internet topology
• Sampling bias in existing topology measurements
• The DIMES project
• Potential applications
• Open issues
Mapping the Internet
• Required characteristics:– connectivity– delays
• Metrics– In/Outdegree– Distance (delay – problematic definition)
Problem definition
G – (un)directed graphN – number of nodesE – number of edgesdv – outdegree of a node v
fd – frequency of an outdegreeP(h) – number of pairs in the “h-hop
neighborhood”
On Power-law Relationships of the Internet Topology
Oct. 1999, Faloutsos Bros.
Mapped the internet at the AS and router level using BGP route views
Data sets: – Nov. ’97: 3015 nodes, 5156 edges– Apr. ’98: 3530 nodes, 6432 edges– Dec. ’98: 4389 nodes, 8256 edges
Outdegree Exponent Power Law
fd ~ d^σ
Other places that people look for power laws…
SCIENCE CITATION INDEX
( = 3)
Nodes: papers Links: citations
(S. Redner, 1998)
P(k) ~k-
2212
25
1736 PRL papers (1988)
Witten-SanderPRL 1981
Sex-web
Nodes: people (Females; Males)Links: sexual relationships
Liljeros et al. Nature 2001
4781 Swedes; 18-74; 59% response rate.
Recall – the Faloutsos graph
Is It Really Power Law?
• Sampling bias could exist
• Crovella article title
• Target – find out if bias exists in prevailing measurement methods, and identify the sources for this bias.
• Configuration – graph model, sampling method, distributions, why this is similar to currently used methods
Results
• Erdos – Renyi + graphs
Sources of sampling bias
• Disproportional sampling of nodes
• Disproportional sampling of edges
• Conclusion
• Identify problems in existing measurement methods (Faloutsos, Caida)
Analysis of Bias Cause
• Explanation– Better coverage with more measurement
sources
DIMES
• Targets
• How we try to solve the problem
DIMES Platform
• Description
• Screenshot
Internet according to DIMES
• maps
Application
• Research– Simulations
• Developing new algs, protocols• Evolution (how will the internet look like in 2020?)• Testing new tools, manufacturing scenarios
– “pure” research• Studying the internet “behavior”, growth• Developing models to describe it
More Application
• Potentially commercial– Improve existing algs’ using knowledge about
the characteristics of the internet.• Multicast alg’• Low – priority packet routing
– Identify (and work around?) network vulnerabilities
Open Issues
• Measuring delays– Asymmetry– round trip is problematic– triangle inequality doesn’t necessarily hold
• Mapping interfaces to server
• Identifying POPs
• Identifying motiffs