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

Shortest Path Distance Distance Metrics for Analyzing Routing 2 Similarly Routed

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

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

Conceptually… 5 Sources Destinations

Routing State Distance 6

More Formally 7

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

Our Data 9

Let’s take a look at its properties… 10

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

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

1.Highly structured 2.Allows 2D visualization 13

14 This happens with any random sample  Internet-wide

Yeah, but a cluster of what!?! 15

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

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

Why these specific prefixes? Level3 Hurricane Electric Sprint 18

19

Can We Find More Clusters? 20

RS-Clustering Problem 21

Optimal is Hard 22

Pivot Clustering Algorithm 23

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

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

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

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

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

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

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