A Model Based Approach for Improving Geolocation *

A Model Based Approach for Improving Geolocation*

Péter HágaEötvös Loránd University

Budapest, Hungary

* ”A Model Based Approach for Improving Router Geolocation”accepted for publication in Computer Networks, 2010.

Outline

• Measurement based geolocation• Detailed path latency model

– To localize internal routers

• Case studies– To localize end hosts

• Spotter framework

• 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

Motivation

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

• 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

Measurement Based Geolocation

Modeling Packet Delays

• A packet delay (d) can be divided into…– Queuing delay (Dq)

– Processing delay (Dpc)

– Transmission delay (Dtr)

– Propagation delay (Dpg)

• The overall packet delay for a network path:

n0 n1 n2 nH…

Only the propagation component has role in the

geolocation

• A given path:

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 observations dh = 100s

• The one-way propagation delay along a given path:

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]

s

d

• Physical properties• Length • cable curvatures

• in copper: ~0.7• in fiber : 0.65

1. Round-Trip Time Constraint

• Using path-latency model– Round-trip propagation delay from a landmark

• Upper approximation of one-way propagation delay

L

t

The nodeto be localized

Landmark with known location

2. Per-link Distances

• Link latency estimation– For a symmetric link e

– For real links

L1

ni-1

ni

Internet

RTT1 – RTT2

ni-1

nie

L1

L2

3. One-way Delay Constraint

• Limits the geographic length of a given network path• Requires OWD measurements

L1 n1

L2n2

n3

Localizing internal routers

Localizing internal routers

Based on one way delays:

Performance Analysis

Extensions

• latency vs. distance distribution for each landmark• calibrated to the other landmarks• flat disks -> probability distributions

Figure is from the Octant paper.

Case study I. – Where are your YouTube videos?

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

Case study I.– Where are the YouTube servers?

Case study I. – Where are the YouTube servers?


London

Amsterdam, ???

Dortmund,Frankfurt,

???

Moscow

Stockholm

Bremen, Hamburg

Dresden

???


Seattle

San Francisco

Los Angeles

Chicago

Minneapolis

Toronto

New York

Baltimore,Washington

???

Atlanta

Charlestown,Savannah


• N=1

• 2<=N<10

• 10<=N

Hong Kong

Singapure

Tokyo

Taipei

Case study II. – Where do the Hungarians live?

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

Case study II. – Where do the Hungarian live?

Spotter geolocation framework

• Engine:– to evaluate the measurement data– To visualize the result (confidence regions)– store raw and evaluated date in nmVO

Framework

• active probing based on Planetlab nodes• Management layer:

– to reserve nodes– to execute probing– to collect measurement data

• Calls the framework• http://nm.vo.elte.hu/spotter• http://nm.vo.elte.hu/spotter/test_version

• Feedbacks are welcome!

Prototype – nm.vo.elte.hu/spotter

• C# ASP based implementation• Under development, current release is „unstable”• define targets• Filtering:

– Landmarks - Planetlab sources– Results – number of „closest” data sources to evaluate

Prototype – nm.vo.elte.hu/spotter

Thank you for your attention!

A Model Based Approach for Improving Geolocation *

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