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Advancements in the Inference of AS Relationships

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Advancements in the Inference of AS Relationships Xenofontas Dimitropoulos (Fontas) (CAIDA/GaTech) Dmitri Krioukov Bradley Huffaker k claffy George Riley – PowerPoint PPT presentation

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Title: Advancements in the Inference of AS Relationships


1
Advancements in the Inference of AS Relationships
  • Xenofontas Dimitropoulos (Fontas)
  • (CAIDA/GaTech)
  • Dmitri Krioukov
  • Bradley Huffaker
  • k claffy
  • George Riley

2
Outline
  • Background previous works.
  • Limitations of existing algorithms.
  • New algorithm to infer customer to provider (c2p)
    peer to peer (p2p) and sibling to sibling (s2s)
    relationships.

3
Motivation
  • Evolution of the Internet.
  • What are the economics of the Internet and their
    role in shaping it?
  • Connectivity doesnt mean reachability.
  • AS relationships determine the valid paths along
    which packets can flow.
  • Finding a provider or peer for ISPs.
  • Details of peering relationships arent widely
    available.
  • Want to make a more informed decision based on
    actual relationships and connectivity instead of
    brand name.
  • Determining AS rank.
  • What is the relative importance of the Internet
    players?
  • Useful for determining order for security
    notifications.

4
Valley-free model
  • AS paths have the following hierarchical
    structure
  • zero or more customer-provider (or sibling)
    links.
  • zero or one peer-peer link.
  • zero or more provider-customer (or sibling)
    links.
  • GAO On Inferring Autonomous Systems
    relationships in the Internet
  • L. Gao ACM IEEE Transactions on Networking 2001.

5
Previous Works
  • GAO
  • Based on hierarchical property of AS paths.
  • Use AS degree to find top of the hill and assign
    transit relationships to the side-hill links.
  • SARK
  • Introduce the Type-of-Relationships (ToR)
    problemGiven an undirected graph G derived from
    a set of BGP paths P, assign the edge type
    (customer-provider or peering) to every edge in G
    such that the total number of valid paths in P is
    maximized.
  • Provide a heuristic solution.
  • SARK Characterizing the Internet hierarchy from
    multiple vantage points L. Subramanian et. al
    INFOCOM 2002.

6
Previous Works
  • DPP and EHS
  • No peering can be inferred in ToR.
  • ToR is NP-complete (by reducing to MAX2SAT).
  • Mathematically rigorous approximations to ToR.
  • DPP "Computing the types of the relationships
    between Autonomous Systems", G. Di Battista et
    al. IEEE INFOCOM 2003.
  • EHS Classifying customer-provider relationships
    in the Internet, T. Erlebach et al. IASTED CCN
    2002.

7
ToR limitations
  • ToR does not provide a deterministic solution,
    because some links can be directed either way and
    produce the same number of valid paths.
  • ToR solutions may treat anomalous paths as valid,
    which results in incorrect AS relationships.
  • BGP misconfigurations and complex polices can
    violate the valley freemodel.

8
Our customer to provider algorithm
  • Generate Input
  • Produce AS graph (with no relationships).
  • Assign weight to edges based on node degree.
  • Formulate c2p inference as a multiobjective
    optimization problem
  • O1 Maximize number of valid paths.
  • O2 Maximize the sum of weights over all edges.
  • Intuition
  • Paths are now colored by their edges weights
    which allows to detect anomalous paths.

701
209
701
209
W 0
W gt 0
70
70
12
18
12
18
13
13
Valid with small weight
Invalid with large weight
9
Work in progress
  • Inference of p2p links.
  • Inference of s2s links.
  • Validation of links.
  • Create AS Rank based on AS relationships.

10
Thanks!
  • ?

Inferring AS Relationships Dead End or Lively
Beginning? 4th Workshop on Efficient and
Experimental Algorithms (WEA), 2005. http//www.pr
ism.gatech.edu/gtg053c/asrelationships.pdf
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