On AS-Level Path Inference Jia Wang (AT&T Labs Research) Joint work with Z. Morley Mao (University of Michigan, Ann Arbor) Lili Qiu (University of Texas, Austin) Yin Zhang (University of Texas, Austin)

On AS-Level Path Inference2 June 10, 2005 Discover end-to-end forwarding path between two hosts Berkeley Internet CNN Calren Level3 GNN QwestSprintUUnet University company AT&T business ISP Berkeley Calren Level3 QwestSprintUUnet University company AT&T business ISP Berkeley Calren Level3 QwestSprintUUnet University company AT&T business ISP

On AS-Level Path Inference3 June 10, 2005 Motivation Network diagnoses Performance optimization Overlay network Content distribution Network modeling

On AS-Level Path Inference4 June 10, 2005 Example – overlay routing Source Destination ? ? ? ? Internet

On AS-Level Path Inference5 June 10, 2005 Outline Related work Routescope Evaluation Improvements  AS relationship inference  First AS hop inference Conclusion

On AS-Level Path Inference6 June 10, 2005 Related work Forwarding path discovery  With direct access to the source Router-level: traceroute AS-level: [Mao Sigcomm2003] [Mao Infocom2004]  Without direct access to the source None!

On AS-Level Path Inference7 June 10, 2005 Challenges Asymmetric routing  Over 60% of AS paths asymmetric Complicated routing policies  Not shortest path routing  Commercial relationship between ASes determines how traffic flow though the Internet Multi-homing  Very common

On AS-Level Path Inference8 June 10, 2005 Routescope Key observation: relationships among ASes play important role in determining feasible forwarding paths Approach: Infer AS-level paths by finding the shortest policy path in an AS graph obtained from BGP tables collected from multiple vantage points

On AS-Level Path Inference9 June 10, 2005 Assumptions Explicit AS relationships  Peer-peer  Provider-customer Shortest AS policy path preferred  “Valley-free” rule Uniform routing policy within an AS AS destination based uniform routing Stability These assumptions are mostly correct.

On AS-Level Path Inference10 June 10, 2005 AS relationships translate into BGP export rules Export to a provider or a peer  Allowed: its routes and routes of its customers and siblings  Disallowed: routes learned from other providers or peers Export to a customer or a sibling  Allowed: its routes, the routes of its customers and siblings, and routes learned from its providers and peers

On AS-Level Path Inference11 June 10, 2005 “Valley-free” rule After traversing a provider-customer or peer-peer edge, cannot traverse a customer-provider or peer-peer edge Invalid path:  >= 2 peer links  downhill-uphill  downhill-peer  peer-uphill

On AS-Level Path Inference12 June 10, 2005 Example of valley-free paths X X [1 2 3], [ ] are valley-free [1 4 3], [ ] are not valley free

On AS-Level Path Inference13 June 10, 2005 AS path inference algorithm Compose the AS graph based on BGP tables Infer AS relationship Classify edges based on AS relationship  Customer-provider (UP) link  Provider-customer (DOWN) link  Peer-peer (FLAT) link Compute shortest policy path conforming the “valley- free” rule using modified Dijkstra’s algorithm Infer the first AS hop if multiple paths returned

On AS-Level Path Inference14 June 10, 2005 Evaluation Based on existing AS relationship inference algorithms  Gao: based on the degree of ASes along the path  SARK: consider AS hierarchy properties  BPP: formulate as 2SAT problem and develop heuristics that yield minimum of invalid paths Compare AS-level paths  Extracted from a large number of BGP tables  Among 125 public BGP gateways

On AS-Level Path Inference15 June 10, 2005 Paths in BGP tables BGP table Algorithm Unique paths Match Match length Exact match ShorterLonger AS7018 (tier-1) Gao %80%33%18%2% SARK67%79%34%15%4% BPP84%85%37%15%0% AS2152 (tier-2) Gao %65%10%34%1% SARK48%57%29%40%3% BPP67% 12%33%0% AS8121 (tier-3) Gao %27%3%69%4% SARK14%23%3%72%4% BPP18%30%3%66%5%

On AS-Level Path Inference16 June 10, 2005 Paths between BGP gateways BGP gateway Algorithm Unique paths Match Match length Exact match ShorterLonger All Gao %51%21%15%35% SARK38%61%24%20%19% BPP18%29%15%5%66% US Gao %43%16%18%40% SARK40%57%24% 19% BPP22%42%18%10%48% BPP yields most accurate AS path inference than GAO and SARK

On AS-Level Path Inference17 June 10, 2005 Possible causes of mismatches Inaccuracy in AS relationship inference  Especially in non-North American regions Multihoming

On AS-Level Path Inference18 June 10, 2005 Inaccurate AS relationship inference 19%~66% of inferred paths are longer than actual paths Significant inconsistency among AS relationship inference results Common peer-peerCommon provider-customer Gao vs SARK229 (3.63%, 36.12%)41730 (89.43%, 94.68%) Gao vs BPP5959 (94.51%, 48.42%)39606 (84.87%, 97.74%) SARK vs BPP334 (52.68%, 2.71%)33752 (85.66%, 93.17%) Solution: infer more accurate AS relationships

On AS-Level Path Inference19 June 10, 2005 A new AS relationship inference algorithm Problem formulation: integer programming  Each edge e in the direct graph G = (V,E) Relation(e) = 1 (customer-provider), 2 (peer-peer), or 3 (provider- customer)  Constraints If r is reverse edge of e, relation(e)+relation(r) = 4. Every path in use is valley-free, i.e., for (e1,e2) on a path, relation(e1) = 1  relation(e2) = 3. For any (src,dst), if there is a path P that is shorter than actual path, then P is not valley free, i.e.,  (e1,e2) on P s.t. relation(e1) ≠ 1  relation(e1) ≠ 3. Novelty: derive additional constraints that violate valley free constraints Solution: improved random walk algorithm [Selman et al. 1993]  Handle non-binary variables  Repeatedly remove stub ASes with out-degree of 0

On AS-Level Path Inference20 June 10, 2005 AS path inference with accurate AS relationship Unique paths Match Match length Exact match ShorterLonger AS7018 (tier-1) %83%35%17%0% AS2152 (tier-2) % 10%35%0% AS8121 (tier-3) %27%3%69%4% All BGP gateways245770%73%30%22%4% US BGP gateways190760%62%27%34%4% The accuracy is among the best of other three in BGP table experiments and is much higher than alternatives in BGP gateway experiments.

On AS-Level Path Inference21 June 10, 2005 Multihoming Over half of the mismatches occur at the very first hop AS If first hop is known, over 15% of mismatches can be eliminated Solution: infer the first hop AS AS S AS T2 AS T1AS D AS C Inferred path Actual path

On AS-Level Path Inference22 June 10, 2005 First hop inference Gather candidate first hop ASes from S by launch traceroute to S from multiple vantage points Identify the transition point T that is likely to be on the path from S to D by testing hop_count(S,T) + hop_count(T,D) = hop_count(S,D) Source Destination AS S AS T2 AS T1AS D AS C Transition point T1 T2 Assume having access to D

On AS-Level Path Inference23 June 10, 2005 Hop count inference Hop_count(S,T) ≈ hop_count(T,S) Hop_count(H,D): H = S or T  Send ping packet to H  Guess the initial TTL value TTL 0 set by H  Get TTL value TTL 1 in ICMP response packet received from H  Hop_count(H,D) = TTL 0 - TTL Common value for TTL 0  32 (Win95/98/Me)  64 (Linux, Compaq Tru64)  128 (Win NT/2000/XP)  255 (most UNIX systems)

On AS-Level Path Inference24 June 10, 2005 Improvement with known first AS hop Unique pathsMatch lengthImprovement AS7018 (tier-1) %3% AS2152 (tier-2) %12% AS8121 (tier-3) %21% All BGP gateways190770%8% US BGP gateways245788%15%

On AS-Level Path Inference25 June 10, 2005 Possible causes of inaccuracy Complicated AS relationships: 15% paths  Two consecutive FLAT links  DOWN link followed by a FLAT link  FLAT link followed by UP link  Dual transit/peering relationship Routing policies  Shortest path vs. customer routes  Inconsistent advertisement to different peering locations  BGP tie-breaking rules AS prepending  > 28% ASes

On AS-Level Path Inference26 June 10, 2005 Conclusion Routescope: AS-level path inference tool without access to the source Two enhancements  AS relationship inference  First hop inference Accuracy: up to 88% inferred paths have the same length as the actual paths New metric for evaluating AS relationship inference Evaluate existing AS relationship inference algorithms