IDMaps:%20A%20Global%20Internet%20Host%20Distance%20Estimation%20Service

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IDMaps A Global Internet Host Distance
Estimation Service

• P. Francis, S. Jamin, C. Jin, Y. Jin, D. Raz, Y.
  Shavitt, L. Zhang
• Presenter Zhenying Liu

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Contents

• Background
• Goals
• Related work
• Architecture
• Performance Evaluation
• Conclusion

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Background

• Increasing need to learn network distances,
  bandwidth
• One method
• Measure the distance by itself(ping, traceroute)
• A useful general service quick, efficient
• SONAR, Feb. 1996
• HOPS(Host proximity Service)
• Need underlying measurement infrastructure to
  provide distance measurements

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Contents

• Background
• Goals
• Related work
• Architecture
• Performance Evaluation
• Conclusion

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IDMaps

• Internet Distance Map Service
• To be underlying service that provides the
  distance information used by SONAR/HOPS
• Goals
• Not near instantaneous information
• Determine roughly the best service given
  technology constraints
• Consider whether there are applications for which
  this level of service would be useful

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Resulting Goals

• Separation of functions
• Separation of IDMaps and the query/reply service
• Distance Metrics
• Latency(round-trip delay)
• useful, easy to provide
• Bandwidth
• Useful, difficult to provide, expensive to
  measure
• Accuracy of the distance information
• High accuracy difficult to achieve
• To obtain accuracy within a factor of 2

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Contents

• Background
• Goals
• Related work
• Architecture
• Performance Evaluation
• Conclusion

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Alternative Architectures and Related Work

• SPAND, Remos provide only distance information
  between hosts close to a distance server and
  remote hosts on the internet
• For each server scales proportionally to the
  number of destination
• For all sites in the Internet N2
• Stemm passive monitoring
• Not perturb actual internet traffic
• Only measure regions previous traversed
• Not adapt to the internet topology changes
• More human efforts

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Contents

• Background
• Goals
• Related work
• Architecture
• Performance Evaluation
• Conclusion

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IDMaps Architecture

• Address three questions
• What form does the distance information take?
• What are IDMaps components?
• How should the distance information be
  disseminated?

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Various forms of distance information
Forms Scale comments
Global IP addr. H2 H of hosts Infeasible
Addr. Prefix(AP) P2 P of APs 200,000 Easily terabytes
AS A2P ( AltltP ) A of AS, P of BGP-advertised IP addr. Blocks A 100,000 (large) Its accuracy is highly suspected
Cluster of APs B2P B of Traces If B 500, manageable Reasonable accuracy
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The form used

• There are three main components
• APs, Tracers, and the virtual links(the raw
  distance)
• AP a consecutive address range of IP addresses
• Tracers Some systems that are distributed around
  the Internet
• Assumption
• We can estimate the distance between two points
  as the sum of distances between intermediate
  points

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An assumption Triangulation
a-cltbltac ? Feasible to estimate distance?
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To support the triangulation

• Set up 2 experiments D1(1995), D2(1997)
• Fig. Shows the ratios of for all
  shortest-path triangulation in the data sets
• Between 75 an 90 of triangulation estimates
  fall within a factor of 2 of the real distance
• The resulting estimates are acceptable!

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Tracer placement

• Two problems
• How many tracers are optimal?
• Given the number of tracers, how to put to
  minimize the maximum distance between an AP and
  the nearest tracer?
• Two graph theoretic approaches that can apply
• K-HST algorithm
• Minimum K-center algorithm
• These algorithms are used to determine the
  placement of fire stations, ambulance placement,
  etc. with a priori

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k-HST decide of tracers

• 1st phase The graph is recursively partitioned
• A node is arbitrarily selected from the
  current(parent) partition, and all the nodes that
  are within a random radius from this node form a
  new node partition
• The radius of the child partition is a factor of
  k smaller than the diameter of the parent
  partition
• Recurs until each node is in a partition of its
  own

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k-HST tree

• 2nd phase virtual node is assigned to each of
  the partition on each level
• The diameter of a partition
• The furthest distance between two nodes in the
  partition
• Equals to 2 times of the length of the links from
  a virtual node to its children

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Use K-HST tree

• Devise a greedy algorithm to find the number of
  tracers when the maximum distance is bounded to D
• Push the tracers down the tree until it discovers
  a partition with diameter ltD
• The number of partitions is the minimum number of
  tracers
• Set the virtual nodes of these partitions to be
  the tracer

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Minimum K-Center Algorithm

• K-Center problem
• The placement of a given number of centers such
  that the maximum distance from a node to the
  nearest center is minimized
• NP-complete
• Willing to tolerate inaccuracies within a factor
  of 2(2-approximation)
• No worse than twice the maximum
• Observation Guarantee that the distance from a
  node to the nearest center is bounded

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Minimum K-Center Algorithm details

• G(V,E), EVV, c(e) is the cost of the shortest
  path between (v1, v2)
• All the graph edges are arranged in
  non-decreasing order by cost
• Gi2 is the graph whenever there is a path between
  u and v in Gi of at most two hops, u?v
• An independent set of a graph G(V,E) is such
  that, for all u,v?V, the edge (u,v) is not in E
• An independent set of Gi2 is thus a set of nodes
  in Gi that are at least 3 hops apart in Gi
• The maximal independent set M as an independent
  set V such that all nodes in V-V are at most
  one hop away from nodes in V

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Algorithm 2 (2-approximate minimum-center
18) details
1. Construct Gi2,G22,, Gm2 2. Compute Mi for
each Gi2 3. Find the smallest I such that
MiltK, say j 4. Mj is the set of K centers
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Tracer Heuristics

• Stub-AS
• only connected to one other AS
• Transit-AS
• connected to one or more other AS
• allows itself to be used as a conduit for traffic
  (transit traffic) between other AS's
• Most large ISPs are Transit-ASs
• Mixed
• Randomly, with uniform distribution placed on the
  network

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Virtual links

• Tracer-tracer virtual links
• Not necessary to list all B2 tracer-tracer
  distances
• Given a number of tracers in Seattle and Boston
• It would almost certainly not to be useful to
  know all of the distance between them
• Allow a sufficient distance approximation between
  hosts in Seattle and hosts in Boston

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Virtual links
C in AP1 will be directed to mirror M1 in AP3
instead of M2 in AP2 Had tracer T2 also traced to
AP1, the client would have been directed to M2

• Tracer-AP VLs
• A dedicated tracer?
• More than one tracer?

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Contents

• Background
• Goals
• Related work
• Architecture
• Performance Evaluation
• Conclusion

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Performance Evaluation

• Topology Generation
• Waxman, Tiers, Inet
• Simulating IDMaps Infrastructure
• Tracer placement Stub-AS, Transit-AS
• Distance map computation
• Tracer-tracer VLs and Tracer-AP VLs

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Performance Metric Computation

• Nearest mirror selection
• Papp the percentage of correct IDMaps answers
  over total number of clients
• Consider IDMaps server selection correct
• As long as the distance between a client and the
  nearest mirror determined by IDMaps is within a
  factor of ? times the distance between the client
  and the actual nearest mirror ( we use ?2)

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Simulation result

• Mirror selection using IDMaps gives noticeable
  improvement over random selection
• Network topology can affect IDMaps performance
• Tracer placement heuristics that do not rely on
  network topology can perform as well or better
  than algorithms that requires a priori knowledge
  of the topology

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Simulation result

• Adding more tracers gives diminishing return
• Number of tracer-tracer VLs required for good
  performance can be on the order of B with a small
  constant
• Increasing the number of tracers tracing to each
  AP improves IDMaps performance with diminishing
  return

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Mirror selection

• Transit-AS
• The probability of that at least 80 of all
  clients will be directed to the correct mirror
  is 100
• Up to 98 of all clients will be directed to the
  correct mirror is only 85

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Mirror selection

• Mirror selection using distance maps outperforms
  random selection regardless of the tracer
  placement algorithm
• Qualitatively, the results from agree with the
  conclusion
• mirror selection using distance maps outperforms
  random selection

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Effect of Topology
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Effect of Topology

• Performance on Tiers generated topology exhibit a
  qualitatively different behavior than those on
  other topologies
• The transit-AS heuristic gives better IDMaps
  performance than the k-HST algorithm on
  topologies generated from Inet and Waxman, but
  not so in the topologies generated from Tiers

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Contents

• Background
• Goals
• Related work
• Architecture
• Performance Evaluation
• Conclusion

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Conclusion

• A global distance measurement infrastructure
  called IDMaps is purposed
• It can be placed on the Internet to collect
  distance information
• Nearest mirror selection fro clients
• Significant improvement over random selection
• Do not require a full knowledge of the underling
  topology

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Conclusion

• IDMaps overhead can be minimized by grouping
  Internet addresses into APs to reduce the number
  of measurements
• Apply t-spanner to tracer-tracer VLs can result
  in linear measurement overhead with respect to
  the number of tracers in the common case
• Overall, this study has provided positive results
  to demonstrate that a useful Internet distance
  map service can indeed be built scalably

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(Stub AS)