Determining the Geographic Location of Internet Hosts Venkata N. Padmanabhan Microsoft Research Lakshminarayanan Subramanian University of California at Berkeley SIGMETRICS 2001

Background  Location-aware services are relevant in the Internet context too  targeted advertising  event notification  territorial rights management  Existing approaches:  user input: burdensome, error-prone  whois: manual updates, host may not be at registered location  Goal: estimate location based on client IP address  challenging problem because an IP address does not inherently indicate location

IP2Geo Multi-pronged approach that exploits various “properties” of the Internet  DNS names of router interfaces often indicate location  Network delay tends to correlate with geographic distance  Hosts that are aggregated for the purposes of Internet routing also tend to be clustered geographically  GeoTrack  determine location of closest router with recognizable DNS name  GeoPing  use delay measurements to triangulate location  GeoCluster  extrapolate partial IP-to-location mapping information using cluster information derived from BGP routing data

GeoPing  Delay-based triangulation is conceptually simple  delay  distance  distance from 3 or more non-collinear points  location  But there are practical difficulties  network path may be circuitous  transmission and queuing delays may corrupt delay estimate  one-way delay is hard to measure  GeoPing  delay is measured from several distributed probes  minimum delay among several samples is picked  Nearest Neighbor in Delay Space (NNDS) algorithm  construct a delay map containing (delay vector,location) tuples  given a delay vector, search through the delay map for closest match  location corresponding to the closest match is our location estimate

Validation of Delay-based Approach Delay tends to increase with geographic distance

Impact of the Number of Probes Highest accuracy when 7-9 probes are used

GeoCluster  Basic idea  divide up the space of IP addresses into clusters using BGP prefixes  use partial IP-to-location mapping data to infer location of each cluster  given target IP address, find matching cluster via longest-prefix match.  location of the matching cluster is our estimate of host location  Issues  partial IP-to-location mapping information may not be entirely accurate  BGP prefixes might not correspond to geographic clusters  Sub-clustering algorithm  use partial IP-to-location mapping information to test whether a BGP prefix is likely to correspond to a geographic cluster  if the test is negative, divide the prefix into two and recursively apply the test to each half  in the end we are only left with geographically clustered prefixes  dispersion offers an indication of the accuracy of a location estimate

Performance of IP2Geo Median error: GeoCluster: 28 km,GeoTrack: 102 km, GeoPing: 382 km

Summary  IP2Geo combines several techniques that leverage different sources of information  GeoTrack: DNS names  GeoPing: network delay  GeoCluster: address aggregates used for routing  Median error varies between 20 and 400 km  Even a 30% success rate is useful especially since we can tell when the estimate is likely to be accurate  Forthcoming paper at SIGCOMM 2001  For more information visit: