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. Problem definition G – (un)directed graph N – number of nodes E – number of edges d v – outdegree of a node v f d – frequency of an outdegree P(h) – number of pairs in the “h-hop neighborhood”

7. 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

8. Outdegree Exponent Power Law f d ~ d ^σ

9. Other places that people look for power laws…

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

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

13. Recall – the Faloutsos graph

14. 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

15. Results Erdos – Renyi + graphs

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

17. Analysis of Bias Cause Explanation –Better coverage with more measurement sources

18. DIMES Targets How we try to solve the problem

19. DIMES Platform Description Screenshot

20. Internet according to DIMES maps

21. 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

22. 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

23. Open Issues Measuring delays –Asymmetry –round trip is problematic –triangle inequality doesn’t necessarily hold Mapping interfaces to server Identifying POPs Identifying motiffs