Mapping the internet topology via multiple agents
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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.

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Mapping the Internet Topology Via Multiple Agents

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Mapping the internet topology via multiple agents

Mapping the Internet Topology Via Multiple Agents


What does the internet look like

What does the internet look like?


Why do we care

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

Topics

  • Power laws in the internet topology

  • Sampling bias in existing topology measurements

  • The DIMES project

  • Potential applications

  • Open issues


Mapping the internet

Mapping the Internet

  • Required characteristics:

    • connectivity

    • delays

  • Metrics

    • In/Outdegree

    • Distance (delay – problematic definition)


Problem definition

G – (un)directed graph

N – number of nodes

E – number of edges

dv – outdegree of a node v

fd – frequency of an outdegree

P(h) – number of pairs in the “h-hop neighborhood”

Problem definition


On power law relationships of the internet topology oct 1999 faloutsos bros

On Power-law Relationships of the Internet TopologyOct. 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

Outdegree Exponent Power Law

fd ~ d^σ


Other places that people look for power laws

Other places that people look for power laws…


Mapping the internet topology via multiple agents

25

2212

SCIENCE CITATION INDEX

Nodes: papers Links: citations

Witten-Sander

PRL 1981

1736 PRL papers (1988)

P(k) ~k-

( = 3)

(S. Redner, 1998)


Sex web

Sex-web

Nodes: people (Females; Males)

Links: sexual relationships

4781 Swedes; 18-74;

59% response rate.

Liljeros et al. Nature 2001


Recall the faloutsos graph

Recall – the Faloutsos graph


Is it really power law

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

Results

  • Erdos – Renyi + graphs


Sources of sampling bias

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

Analysis of Bias Cause

  • Explanation

    • Better coverage with more measurement sources


Dimes

DIMES

  • Targets

  • How we try to solve the problem


Dimes platform

DIMES Platform

  • Description

  • Screenshot


Internet according to dimes

Internet according to DIMES

  • maps


Application

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

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

Open Issues

  • Measuring delays

    • Asymmetry

    • round trip is problematic

    • triangle inequality doesn’t necessarily hold

  • Mapping interfaces to server

  • Identifying POPs

  • Identifying motiffs


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