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End-to-End Inference of Router Packet Forwarding Priority

End-to-End Inference of Router Packet Forwarding Priority. Guohan Lu 1 , Yan Chen 2 , Stefan Birrer 2 , Fabian E. Bustamante 2 , Chi Yin Cheung 2 , Xing Li 1 Lab for New Generation Network, Tsinghua Univ. China Lab for Internet & Security Tech, Northwestern Univ. Background.

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End-to-End Inference of Router Packet Forwarding Priority

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  1. End-to-End Inference of Router Packet Forwarding Priority Guohan Lu1, Yan Chen2, Stefan Birrer2, Fabian E. Bustamante2, Chi Yin Cheung2, Xing Li1 Lab for New Generation Network, Tsinghua Univ. China Lab for Internet & Security Tech, Northwestern Univ.

  2. Background • Router QoS mechanisms available • Priority Queueing • Custom Queueing • Class-Based Weighted Fair Queueing • Traffic policing/shaping • ISPs do use them • Rate limiting, e.g., P2P applications • Provide bandwidth guarantee for certain applications

  3. Motivation • Packet forwarding priority affects: • measurements, loss, delay, available bandwidth • applications • Hidden rules • Users circumvent: skype, port 80 • End-to-end approach • POPI (Packet fOrwarding Priority Inference) • The first such work to the best of our knowledge

  4. Outline • Background and Motivation • Inference Methods • Evaluations • Conclusions

  5. Basic Ideas • Priority generates packet delivery differences • Measure the differences • Send different packet types • Choose a metric • Loss : the most natural choice • Delay : queuing delay maybe small • Out-of-order: not all QoS generate OOO, but very interesting work we have in progress

  6. Challenges and Building Blocks • Challenges • Background traffic fluctuations • Packet losses can be highly correlated POPI Design • Step 1: Generate the differences • Saturate low-priority queue(s) temporarily • Step 2: Detect the differences • Non-parametric statistical methods independent to the loss model and insensitive to loss correlation • Step 3: Cluster multiple packet types into groups • Hierarchical clustering method

  7. Step 1: Probing Approaches • Send bursts • Spectrum of approaches • Small bursts: less aggressive, wait for the losses • Large bursts: more aggressive, incur the losses Less intrusive Less accurate Longer period More intrusive More accurate Shorter period Small Burst Large Burst

  8. A B C B A C C B A Probing Method • nb bursts, nr rounds, k packet types • Packets randomly distributed in one burst • No bias

  9. 0.1 0.3 0.2 0.0 0.1 0.2 0.0 0.3 0.3 0.1 0.2 0.1 0.8 0.6 0.7 0.5 0.7 0.6 0.5 0.5 0.4 0.8 0.8 0.4 0.7 1 3 0.32 2 1.5 1 3 0.33 2 1.5 0.36 2 3 1 3 6 4.5 5 5 0.77 4 0.83 5 6 4.5 6 6 4 4 0.90 0.3 A B C D E F Step 2: Detect the Difference – Average Normalized Loss Ranks • Small difference for the same group • Large difference for different groups k=6, nb=4, nr=10 A B C D E F Loss ranks Loss rates Burst1 Burst2 Loss ranks Loss rates Loss ranks Burst3 Loss rates Loss ranks Loss rates Burst4 ANR

  10. Loss Rates vs. Loss Ranks • Absolute loss rate – parametric • Depends on the loss model • Loss rate ranks – non-parametric • Independent of the loss model • Ranks randomly permuted over bursts for packet types within a same priority • Non-parametric statistical approach is better

  11. G010<q G01>q G011<q G0>q G02<q Step 3: Grouping Method • Threshold derived for ANR range in the paper • Hierarchical Divisive Clustering based on ANR threshold • k-means • Details in the paper

  12. Outline • Background and Motivation • Inference Method • Evaluations • NS2 Simulations (details in the paper) • PlanetLab experiments • Conclusions

  13. PLab Evaluation Methodology • 81 random pairs (both directions) for 162 end hosts. Each from different institutes. • USA, Asia, Europe, South America • 32 bursts, 40 rounds in a burst • 32 packet types as below

  14. Evaluation of ANR Metric (I) • Except for very few paths, most ANR/q are < 0.8 or > 1.2 • Paths well separated by ANR <0.80 >1.20

  15. Evaluation of ANR Metric (II) • Choose top 30 paths w/ the largest ANR range • First 15 detected w/ multiple priorities • Large inter-group distance • Packet types within a same group are condensed

  16. Multi-Priority Paths Inferred • 4 P2P (all low), 3 for well-known applications (all high), 8 for ICMP (majority low) • 3 pairs show symmetric group pattern

  17. TTL=1 TTL=2 TTL=3 Configured Router No loss rate difference! Validation -- Methodology • Hop-by-hop method • Vary TTLs • Measure loss rates difference by counting the ICMP replies from routers • Test 30 paths: 15 multi-priority and 15 non-priority paths • Send emails to related network operators

  18. Validation -- Results • Hop-by-hop method • 5 paths could not be checked • Routers no response or hosts down • Good true positives: 13 of multi-priority paths successfully validated • No false negatives: 12 of non-priority paths show no loss difference • Inquiry Response • Sent 13 emails • 7 replies, all positive confirmations from network operators • One as standalone traffic shaper

  19. Conclusions • The first end-to-end attempt to infer router forwarding priority • Robust non-parametric method • Good inference accuracy • Several priority configurations found through PlanetLab experiments • Ongoing work • Decrease the probe overhead • Other kinds of metric (packet reordering)

  20. Software download available at • http://list.cs.northwestern.edu/popi • Questions? • Thanks !

  21. Threshold of the ANR Range • One group: • normal distribution • R decreases as nb increases • Two groups: R > 0.5 R < ANR range Range One group Two groups Normal Distribution 0.5 nb 12

  22. Related Work (I) • Shared Congestion for flows • detect shared congested queue • Two flows • Flows already congested • Our problem: detect unshared congested queue • More than two flows • Focus on router configuration, not flows

  23. Related Work (II) • Hop-by-Hop approach • Tulip, sting • Statistical method also applied • Used in our validation • Network Tomography • Infer link loss • Non-intrusive

  24. Effects of nb , nr and a • Zero under-partition for nb ≥ 16 • Smaller over-partition for a = 0.001 • Error decreases as nr increases, 40 for practical use

  25. Results • All positive confirmation from the network operators!

  26. Effects of nr • Phase 1: Under-partition • Phase 2: Under-partition and Over-partition • Phase 3: Correct Partition

  27. What if some bursts has no loss? • Method can tolerate when a fraction bursts show no loss rate different.

  28. Stability of bursts losses during the probe • Either all Bursts experience losses or none of them experience loss • Background traffic relative stable

  29. nr needed for probe • Error decreases as nr increases • Correct inference when nr is very small (less than 5) for certain paths. Possibility to decrease the probe overhead.

  30. Loss rate ranks v.s Loss rate • Three paths correctly partition by ANR • Blue points: Large ANR but small LR range • Red point: Large LR, but small ANR

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