1. Routing State Distance: A Path-based Metric for Network Analysis Natali Ruchansky Gonca Gürsun, Evimaria Terzi, and Mark Crovella

2. Shortest Path Distance Distance Metrics for Analyzing Routing 2 Similarly Routed

3. Based on this distance intuition we develop a new metric based on paths and show it is good for: o Visualization of networks and routes o Characterizing routes o Detecting significant patterns o Gaining insight about routing A New Metric 3

4. We call this path-based distance metric: Routing State Distance 4

5. Conceptually… 5 Sources Destinations

6. Routing State Distance 6

7. More Formally 7

8. RSD to BGP 8 A few issues arise… 1.Missing Values 2.Multiple next hops

9. Our Data 9

10. Let’s take a look at its properties… 10

11. RSD versus Hop Distance 11 No relation between RSD and hop distance

12. Finer Grained Measure Varies smoothly and has a gradual slope. Allows fine granularity 12 Increase of 1 encompasses many prefixes

13. 1.Highly structured 2.Allows 2D visualization 13

14. 14 This happens with any random sample  Internet-wide

15. Yeah, but a cluster of what!?! 15

16. 16 Small cluster “C”Large Cluster Small cluster “C” Large cluster

17. A local atom is a set of prefixes that are routed similarly in some region of the internet. So the smaller cluster is a local atom of certain prefixes that are routed similarly by a large set of ASes 17

18. Why these specific prefixes? Level3 Hurricane Electric Sprint 18

19. 19

20. Can We Find More Clusters? 20

21. RS-Clustering Problem 21

22. Optimal is Hard 22

23. Pivot Clustering Algorithm 23

24. 5 largest clusters Clusters show a clear separation Each cluster corresponds to a local atom 24

25. 25 Size of CSize of SDestinations C115016Ukraine 83% Czech. Rep 10% C21709Romania 33% Poland 33% C31267India 93% US 2% C44848Russia 73% Czech rep. 10% C537515US 74% Australia 16% Interpreting Clusters

26. To address this we propose a formalism called Overlap Clustering and show that it is capable of extracting such clusters. We ask ourselves if a partition is really best? 26 Seek a clustering that captures overlap

27. Related Work Reported that BGP tables provide an incomplete view of the AS graph. [Roughan et. al. ‘11] Visualization based on AS degree and geo-location. [Huffaker and k. claffy ‘10] Small scale visualization through BGPlay and bgpviz Clustering on the inferred AS graph. [Gkantsidis et. al. ‘03] Grouping prefixes that share the same BGP paths into policy atoms. [Broido and k. claffy ‘01] Methods for calculating policy atoms and characteristics. [Afek et. al. ‘02] 27

28. Take-Away Analysis with typical distance metrics is hard We introduce a new one -- Routing State Distance – that is simple and based only on paths Overcome BGP hurdles and show it can be used for: o In-depth analysis of BGP o Capturing closeness useful for visualization o Uncovering surprising patterns o General setting Developed a new set of tools for extracting insight from BGP measurements 28

29. Code, data, and more information is available on our website at: csr.bu.edu/rsd 29 Code Pivot Clustering Overlap Clustering RSD Computation Data Prefix List Pairwise RSD

30. Natali Ruchansky Gonca Gürsun, Evimaria Terzi, and Mark Crovella Thank you!!