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

Study the power of off-line metrics in approximating Internet distances for content distribution networks. Analyze static Internet distance components and propose improvements using linear regression and new metrics like Depth. Evaluate accuracy in selecting mirror sites.

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

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  1. On the Power of Off-line Data in Approximating Internet Distances Danny Raz (danny@cs.technion.ac.il) Technion - Israel Institute of Technology and Prasun Sinha (prasunsinha@lucent.com) Bell Labs., Lucent Technologies

  2. 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

  3. 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

  4. Importance of Internet Distance Estimation • Picking closest mirror-site/cache • For use in Content Distribution Networks

  5. 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]

  6. 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 ?

  7. 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

  8. Linear Regression(Geo-distance and Hop-count) minRTT vs. Hop-count SE (Std. Error) = 25.71 minRTT vs. Geo-distance SE (Std. Error) = 26.93

  9. Multiple Linear Regression (Multiple metrics) minRTT vs. Geo-distance, Hop-count SE = 21.52 minRTT vs. Geo-distance, AS-count SE = 23.80

  10. minRTT = geo-distance +  hop-count +AS-count ? • 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

  11. 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

  12. Linear Regression (Depth) minRTT vs. Depth SE = 41.02 minRTT vs. Depth and Geo-distance SE = 24.52

  13. Squared Errors in Estimating minRTT

  14. Accuracy of picking the nearest mirror site 880 clients and 8 servers

  15. 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?

  16. 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.

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