On the Power of Off-line Data in Approximating Internet Distances

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On the Power of Off-line Data in Approximating
Internet Distances

• Danny Raz (danny_at_cs.technion.ac.il)
• Technion - Israel Institute of Technology
• and
• Prasun Sinha (prasunsinha_at_lucent.com)
• Bell Labs., Lucent Technologies

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Outline

• Internet Distance
• Off line metrics
• Geographic distance, hops, AS, depth
• Linear Regression for Internet distance
  estimation
• Multi-variable linear regression
• Accuracy of picking closest mirror site
• The next step

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Internet Distance

• Internet Distance one way delay between hosts
• Components of Internet Distance
• Dynamic
• Server Load
• Network Congestion / Router Load
• Static
• propagation delay over the links
• Router processing delay
• Edge-router processing delay

Goal To study the power of estimating the Static
Internet Distance using off-line metrics
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Importance of Internet Distance Estimation

• Picking closest mirror-site/cache
• For use in Content Distribution Networks

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Approaches

• Dynamic
• Dynamic probing Dykes et. al. Infocom 00
• Passive monitoring Andrews et. al. Infocom 02
• Static
• Semi-active probing (IDMAPs) Jamin et. al.
  Infocom 00
• Other relevant work
• Geographic Distance and RTT Padmanabhan Sigcomm
  02

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Static Internet Distance
AS 1
AS 2
AS 3
Core Router
Edge Router

• Propagation delay geographical distance
• Router processing delay hops
• Edge-router processing delay AS

AS Autonomous System
Static Internet Distance ? geo-distance ?
hop-count ? AS-count ?
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Data Collection

• Clients 2500 public libraries in US
• Servers (mirrors/caches) 8 traceroute locations
  in US
• The location (latitude, longitude) is known for
  every host.
• For every client-server pair
• Run multiple (10) traceroutes
• Pick the traceroute result with the smallest RTT
• Compute
• Geo-distance based on latitude and longitude
• Hop-count from traceroute
• AS-count from traceroute based on names of
  routers and IP Address Prefixes

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Linear Regression(Geo-distance and Hop-count)
minRTT vs. Geo-distance SE (Std. Error) 26.93
minRTT vs. Hop-count SE (Std. Error) 25.71
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Multiple Linear Regression (Multiple metrics)
minRTT vs. Geo-distance, Hop-count SE 21.52
minRTT vs. Geo-distance, AS-count SE 23.80
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minRTT ? geo-distance ? hop-count ?
AS-count ?
Term Coefficient p-value
Geo-distance 12.53 (?) lt0.0001
Hop-count 2.45 (?) lt0.0001
AS-count -0.64 (?) 0.0387

• High correlation between hop-count and AS-count
  (highest among any other pair of metrics)
• Hop-count and AS-count should not be used together

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A new Off-line metric Depth

• Hop-count requires dynamic probing
• Introduce an alternate metric Depth
• Average Hop-count to the nearest backbone network
  (a hand-made list of 30 big core networks)
• Constant per host (client/server)
• Alternately, measure in units of time rather than
  hops
• (Client depth Server depth) as a metric

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Linear Regression (Depth)
minRTT vs. Depth SE 41.02
minRTT vs. Depth and Geo-distance SE 24.52
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Squared Errors in Estimating minRTT
Metric SE (Standard Error)
Geo-distance, Hop-count 21.52
Geo-distance, AS-count 23.80
Geo-distance, Depth 24.52
Hop-count 25.71
Geo-Distance 26.93
Depth 41.02
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Accuracy of picking the nearest mirror site
Allowed Delta Random Geo-distance Hop-count Geo-distance, Hop-count Geo-distance, Depth
0 12.50 37.84 44.32 38.41 33.98
10ms 21.15 53.07 58.98 55.91 50.45
20ms 33.75 73.18 76.70 74.89 70.91
30ms 46.25 90.91 88.75 91.36 89.43
880 clients and 8 servers
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Summary

• Combination of hop-count and geographic distance
  improves over individual metrics
• Using Depth along with Geo-distance improves
  performance and is completely off-line
• For closest mirror selection with 30 ms allowed
  deviation, almost any metric gives 90 accuracy

Is there much space to improve?
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The Next Step

• Global Data
• Collection and analysis of data based on clients
  and servers spread across the globe
• Using both off-line and on-line
• Techniques to combine the power of off line
  estimation with on-line estimation.