1 End-to-End Inference of Router Packet Forwarding Priority Guohan Lu 1, Yan Chen 2, Stefan Birrer...
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![Page 1: 1 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.](https://reader033.fdocuments.us/reader033/viewer/2022061306/551475c8550346494e8b62c4/html5/thumbnails/1.jpg)
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End-to-End Inference of Router Packet Forwarding Priority
Guohan Lu1, Yan Chen2, Stefan Birrer2, Fabian E. Bustamante2, Chi Yin Cheung2, Xing Li1
1. Lab for New Generation Network, Tsinghua Univ. China
2. Lab for Internet & Security Tech, Northwestern Univ.
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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 applic
ations
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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 knowl
edge
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Outline Background and Motivation Inference Methods Evaluations Conclusions
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Basic Ideas Priority generates packet delivery diff
erences 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
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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
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Step 1: Probing Approaches Send bursts Spectrum of approaches
Small bursts: less aggressive, wait for the losses
Large bursts: more aggressive, incur the losses
Large BurstSmall Burst
More intrusive
More accurate
Shorter period
Less intrusive
Less accurate
Longer period
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Probing Method
nb bursts, nr rounds, k packet types Packets randomly distributed in one b
urst No bias
A B CC B A AC B
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Step 2: Detect the Difference – Average Normalized Loss Ranks
0.1 0.3 0.2 0.5 0.6 0.4
Burst1
A B C D E F
1 3 2 5 6 4Burst2
0.3 0.2 0.1 0.6 0.5 0.8
0.2 0.1 0.3 0.8 0.5 0.4
0.0 0.0 0.1 0.7 0.7 0.8
Burst3
Burst4 1.5 1.5 3 4.5 4.5 6
2 1 3 6 5 4
3 2 1 5 4 6
ANR 0.32
0.33
0.36
0.77
0.83
0.90
Small difference for the same group Large difference for different groups
k=6, nb=4, nr
=10
Loss ratesLoss ranksLoss ratesLoss ranksLoss ratesLoss ranksLoss rates
Loss ranks
0.7
0.3
A B C D E F
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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
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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
G0>
G01>
G010<
G011<
G02<
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Outline Background and Motivation Inference Method Evaluations
NS2 Simulations (details in the paper) PlanetLab experiments
Conclusions
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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
Protocols Type/Source Port Number
ICMP ICMP_ECHO
TCP well-known app: 20-21 (ftp), 23 (telnet), 110 (pop3), 179 (BGP), 443 (https)P2P: 1214 (fasttrack), 4661-4663(eDonkey), 6346-6347 (gnutella), 6881(bitTorrent)security-related: 161 (snmp), 136, 137, 139, 445Random: 1000, 12432, 25942, 38523, 43822, 57845
UDP SNMP: 161Random: 1000, 12432, 25942, 38523, 43822, 57845
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Evaluation of ANR Metric (I)
Except for very few paths, most ANR/ are < 0.8 or > 1.2
Paths well separated by ANR
>1.20<0.80
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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
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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
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Validation -- Methodology
Hop-by-hop method Vary TTLs Measure loss rates difference by counting the ICMP replie
s from routers Test 30 paths: 15 multi-priority and 15 non-priority paths
Send emails to related network operators
TTL=2TTL=1 TTL=3
Configured Router No loss rate difference!
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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
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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)
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Software download available at http://list.cs.northwestern.edu/popi
Questions?
Thanks !
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Threshold of the ANR Range One group:
normal distribution R decreases as nb increases
Two groups: R > 0.5
Normal Distribution
ANR rangeR <
nb
0.5
12
One group
Range
Two groups
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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
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Related Work (II) Hop-by-Hop approach
Tulip, sting Statistical method also applied Used in our validation
Network Tomography Infer link loss Non-intrusive
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Effects of nb , nr and
Zero under-partition for nb ≥ 16 Smaller over-partition for = 0.001 Error decreases as nr increases, 40 for practical use
nb
Type 8 16 32 64 128
0.01
Over Partition(%)
8.5 2.52 2.23 2.52 2.42
Under Partition 0.2 0 0 0 0
Sum 8.7 2.52 2.23 2.52 2.42
0.001
Over Partition 5.7 0.63 0.21 0.29 0.23
Under Partition 43.5 0 0 0 0
Sum 49 0.63 0.21 0.29 0.23
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Results
All positive confirmation from the network operators!
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Effects of nr
Phase 1: Under-partition Phase 2: Under-partition and Over-partition Phase 3: Correct Partition
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What if some bursts has no loss?
Method can tolerate when a fraction bursts show no loss rate different.
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Stability of bursts losses during the probe
Either all Bursts experience losses or none of them experience loss
Background traffic relative stable
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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.
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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