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A Model Based Approach for Improving Geolocation * PowerPoint Presentation
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A Model Based Approach for Improving Geolocation *

A Model Based Approach for Improving Geolocation *

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A Model Based Approach for Improving Geolocation *

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  1. A Model Based Approach for Improving Geolocation* Péter Hága EötvösLoránd University Budapest, Hungary * ”A Model Based Approach for Improving RouterGeolocation”accepted for publication in Computer Networks, 2010.

  2. Outline • Measurement based geolocation • Detailed path latency model • To localize internal routers • Case studies • To localize end hosts • Spotter framework

  3. Motivation • Location information can be useful to both private and corporate users • Targeted advertising on the web • Restricted content delivery • Location-based security check • Web statistics • Scientific applications • Measurement visualization • Network diagnostics

  4. Geolocation in General • Passive geolocation • Extracting location information from domain names • DNS and WhoIS databases • Commercial databases • MaxMind, IPligence, Hexasoft • Large and geographically dispersed IP blocks can be allocated to a single entity • Active geolocation • Active probing • Measurement nodes with known locations • Constraint based techniques

  5. Measurement Based Geolocation • Network Delays – with active measurements • Delays can be transformed to geographic distance • Round Trip Time (ping) • One-way delay (measured in the ETOMIC Infrastucture) • Effects of delay underestimation • Effects of delay overestimation

  6. Modeling Packet Delays • A packet delay (d) can be divided into… • Queuing delay (Dq) • Processing delay (Dpc) • Transmission delay (Dtr) • Propagation delay (Dpg) • A given path: Only the propagation component has role in the geolocation n0 n1 n2 nH … • The overall packet delay for a network path:

  7. How to Estimate Propagation Delays • Assumptions used in the model • No queuing:Dq = 0 • The per-hop processing and transmission delays can be approximated by a global constant: dh = Dpc + Dtr • Based on the literature and our observationsdh = 100s • The one-way propagation delay along a given path:

  8. Distance Approximation • An upper approximation of geographical distance from source s to destination d: • where r is the velocity of signal propagation in network [in c units] d • in copper: ~0.7 • in fiber : 0.65 • Physical properties • Length • cable curvatures s

  9. 1. Round-Trip Time Constraint • Using path-latency model • Round-trip propagation delay from a landmark • Upper approximation of one-way propagation delay The node to be localized t L Landmark with known location

  10. 2. Per-link Distances • Link latency estimation • For a symmetric link e • For real links L2 ni e L1 ni-1 ni ni-1 Internet L1 RTT1 – RTT2

  11. 3. One-way Delay Constraint • Limits the geographic length of a given network path • Requires OWD measurements n2 L2 n3 L1 n1

  12. Localizing internal routers

  13. Localizing internal routers

  14. Localizing internal routers Based on one way delays:

  15. Performance Analysis

  16. Extensions • latency vs. distance distribution for each landmark • calibrated to the other landmarks • flat disks -> probability distributions Figure is from the Octant paper.

  17. Case study I. – Where are your YouTube videos?

  18. Case study I.– Where are your YouTube videos? • Where are YouTube’s content delivery servers? • MaxMind result is: Mountain View, CA • Geoloc based on active measurement: • The IP range: 74.125.0.0/16 • 8127 accessible IP addresses • 8127 nodes to be localized • Landmarks: 300 PlanetLab nodes

  19. Case study I.– Where are the YouTube servers?

  20. Case study I. – Where are the YouTube servers?

  21. Case study I. – Where are the YouTube servers? Stockholm London Bremen, Hamburg Moscow Amsterdam, ??? Dresden Dortmund,Frankfurt,??? ???

  22. Case study I. – Where are the YouTube servers?

  23. Case study I. – Where are the YouTube servers? Toronto Seattle New York Minneapolis Chicago ??? San Francisco Atlanta Los Angeles Baltimore,Washington Charlestown,Savannah

  24. Case study I. – Where are the YouTube servers? Hong Kong Tokyo Singapure Taipei • N=1 • 2<=N<10 • 10<=N

  25. Case study II. – Where do the Hungarians live?

  26. Case study II. – Where do the Hungarian live? • target IPs: • google/yahoo/baidu/bing web search • 10 words from the 100 most frequent hungarian words • 4359 globally accessible IP addresses • 4359 nodes to be localized • Landmarks: 300 PlanetLab nodes

  27. Case study II. – Where do the Hungarian live?

  28. Case study II. – Where do the Hungarian live?

  29. Case study II. – Where do the Hungarian live?

  30. Spottergeolocation framework

  31. Framework • Engine: • to evaluate the measurement data • To visualize the result (confidence regions) • store raw and evaluated date in nmVO • active probing based on Planetlab nodes • Management layer: • to reserve nodes • to execute probing • to collect measurement data

  32. Prototype – nm.vo.elte.hu/spotter • Calls the framework • http://nm.vo.elte.hu/spotter • http://nm.vo.elte.hu/spotter/test_version • Feedbacks are welcome! • C# ASP based implementation • Under development, current release is „unstable” • define targets • Filtering: • Landmarks - Planetlab sources • Results – number of „closest” data sources to evaluate

  33. Prototype – nm.vo.elte.hu/spotter

  34. Prototype – nm.vo.elte.hu/spotter

  35. Prototype – nm.vo.elte.hu/spotter

  36. Thank you for your attention!