On AS-Level Path Inference

1
On AS-Level Path Inference

• Jia Wang (ATT 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)

2
Discover end-to-end forwarding path between two
hosts
Internet
Qwest
Sprint
UUnet
ATT
Level3
Level3
Level3
GNN
Calren
Calren
Calren
Berkeley
Berkeley
Berkeley
CNN
company
University
3
Motivation

• Network diagnoses
• Performance optimization
• Overlay network
• Content distribution
• Network modeling

4
Example overlay routing
?
Internet
?
?
2
?
4
5
1
3
Source
Destination
5
Outline

• Related work
• Routescope
• Evaluation
• Improvements
• AS relationship inference
• First AS hop inference
• Conclusion

6
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!

7
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

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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

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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.
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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

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Valley-free rule

• After traversing a provider-customer or peer-peer
  edge, cannot traverse a customer-provider or
  peer-peer edge
• Invalid path
• gt 2 peer links
• downhill-uphill
• downhill-peer
• peer-uphill

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Example of valley-free paths
1 2 3, 1 2 6 3 are valley-free
X
X
1 4 3, 1 4 5 3 are not valley free
13
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 Dijkstras
  algorithm
• Infer the first AS hop if multiple paths returned

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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

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Paths in BGP tables
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Paths between BGP gateways
BPP yields most accurate AS path inference than
GAO and SARK
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Possible causes of mismatches

• Inaccuracy in AS relationship inference
• Especially in non-North American regions
• Multihoming

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Inaccurate AS relationship inference

• 1966 of inferred paths are longer than actual
  paths
• Significant inconsistency among AS relationship
  inference results

Solution infer more accurate AS relationships
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A new AS relationship inferencealgorithm

• 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

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AS path inference with accurate AS relationship
The accuracy is among the best of other three in
BGP table experiments and is much higher than
alternatives in BGP gateway experiments.
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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

AS T1
AS D
AS S
Inferred path
Actual path
AS T2
AS C
Solution infer the first hop AS
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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)

Assume having access to D
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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 TTL0 set by H
• Get TTL value TTL1 in ICMP response packet
  received from H
• Hop_count(H,D) TTL0 - TTL1 1
• Common value for TTL0
• 32 (Win95/98/Me)
• 64 (Linux, Compaq Tru64)
• 128 (Win NT/2000/XP)
• 255 (most UNIX systems)

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Improvement with known first AS hop
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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
• gt 28 ASes

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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