1. RandPing: A Randomized Algorithm for IP Mapping
Michelle Liu
Yuhan Cai
11/16/2018

2. Outline Introduction Related Work Background Algorithm Overview
Experimental Evaluation
Conclusions and Future Work
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3. Introduction Motivations Problem statement Challenges
Collection of personalized information
Authorities of transactions
Problem statement
IP mapping is the problem that, given an IP address p, find the geographic location of the internet host with IP address p.
Challenges
No authorative database
IP addresses do not contain geographic information
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4. Related Work DNS based approach Delay based approach
Using DNS records from databases
IP2LL, NetGeo, and GeoTrack
DNS might not be related to locations
Delay based approach
Exploiting relationship between distances and network delays
GeoPing and CBG
Clustering based approach
Splitting IP address space into clusters
Assumption: all hosts within the same cluster are co-located
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5. Background Best line bound Above the baseline Below all data points
Closest to all data points
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6. Background (cont.) Clustering Outlier detection Scriptroute system
Partitioning Around Medoids (PAM)
Quality of a Clustering = average of the distance of an object to the medoid of its cluster
Outlier detection
O is a DB(p, D)-outlier if at least fraction p of T lies greater than distance D from O.
Scriptroute system
A system that allows network measurements conduction from remote vantage points
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7. Algorithm Overview Overall idea Major steps
Clustering probing machines
Random selection of a small set of probing machines
Reduction of search space by pruning
Major steps
Preprocessing stage
Randomized pinging
Location estimation
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8. Preprocessing Stage
Construction of RTT table and Distance table for probing machines
Computation of the best line for each probing machine
subject to the constraint:
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9. Preprocessing (cont.)
Clustering of probing machines based on their geographic locations
Transformation of the geographic system to a Cartesian coordinate system
x = 2RcosT0 (G – G0) / 360
y = 2R (T - T0) / 360
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10. Randomized Pinging Random selection of m clusters
Random selection of k probing machines within each cluster
Pinging the target machine to get n = m*k RTT measurements
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11. Location Estimation Computation of estimated distances
Determination of the best group of circles by dynamic programming
Keep track of groups of circles
Incrementally build up each group
Pick the biggest group
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12. Location Estimation (cont.)
Locating the target machine by non-linear programming
subject to the constraints:
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13. Location Estimation (cont.)
Repeat the process for r times
Computation of the centroid for the r estimated locations
Prune out distance-based outliers
Compute the centroid of the points left
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14. Experimental Results Setup Results
Machines selected from Planetlab in US
One small set of machines to be target machines, the rest to be probing machines
Results
Error distance: distance between the real location of the target machine and the estimated one
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15. Experimental Results (cont.)
City Name
Actual Location
Estimated Location
Error Distance (km)
Cornell (NY)
( , )
( , )
345.9
Duke
( , )
( , )
633
Intel (Seattle)
( , )
( , )
250.1
Northwestern
(-87.69, 42.05)
( , )
272.2
Stanford
( , )
( , )
663
Dartmouth
( , )
( , )
496.3
UCSC
( , 37.0)
( , )
270.2
UGA
(-83.36, 33.98)
( , )
591.1
UMASS
( , )
( , )
333.7
UOregon
( , 44.04)
( , )
1075
Uvirginia
( , )
( , )
536.2
CalTech
( , )
( , )
373.3
Pittsburg
( , )
( , )
53.88
Rutgers
( , )
( , )
336.7
Umich
( , )
( , )
131.2
Wisc
( , )
( , )
150.6
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16. Experimental Results (cont.)
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17. Experimental Analysis
Limited number of probing machines
Effect of randomization is not obvious
The best line estimation is too conservative.
Intersection region of the circles is too big.
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18. Conclusions
A randomized approach for IP mapping using clustering and outlier detection
Location estimation based on dynamic programming and non-linear programming
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19. Future Work Adjusting the algorithm parameters:
number of clusters
number of trials and
number of picked machines
Proving a lower bound for the difference between the accuracy of randomized algorithm and deterministic algorithm
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