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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.

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Mapping the Internet Topology Via Multiple Agents

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  1. Mapping the Internet Topology Via Multiple Agents

  2. What does the internet look like?

  3. 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

  4. Topics • Power laws in the internet topology • Sampling bias in existing topology measurements • The DIMES project • Potential applications • Open issues

  5. Mapping the Internet • Required characteristics: • connectivity • delays • Metrics • In/Outdegree • Distance (delay – problematic definition)

  6. 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

  7. 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

  8. Outdegree Exponent Power Law fd ~ d^σ

  9. Other places that people look for power laws…

  10. 25 2212 SCIENCE CITATION INDEX Nodes: papers Links: citations Witten-Sander PRL 1981 1736 PRL papers (1988) P(k) ~k- ( = 3) (S. Redner, 1998)

  11. Sex-web Nodes: people (Females; Males) Links: sexual relationships 4781 Swedes; 18-74; 59% response rate. Liljeros et al. Nature 2001

  12. Recall – the Faloutsos graph

  13. 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

  14. Results • Erdos – Renyi + graphs

  15. Sources of sampling bias • Disproportional sampling of nodes • Disproportional sampling of edges • Conclusion • Identify problems in existing measurement methods (Faloutsos, Caida)

  16. Analysis of Bias Cause • Explanation • Better coverage with more measurement sources

  17. DIMES • Targets • How we try to solve the problem

  18. DIMES Platform • Description • Screenshot

  19. Internet according to DIMES • maps

  20. 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

  21. 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

  22. 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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