On the Impact of Clustering on Measurement Reduction

May 14th, 2009

http://inl.info.ucl.ac.be

D. Saucez, B. Donnet, O. BonaventureThanks to P. François

Université catholique de Louvain

Measurements to Improve netapps/service performance

Bandwidth?

Delay?

Loss?

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? ? ? ? ? ?

? ? ? ? ? ? ? ?? ? ? ? ? ?

Scalability issues with large-scale measurements

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How to reduce themeasurement overhead?

Limit the number of measured destinations Clustering

Limit the number of measuring sources Collaboration

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Limit the number of measured destinationsGroup destinations into Clusters

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Clustering techniques

Geographic Clustering

Group nodes by city

n-agnostic clustering [1]

group nodes by /n prefix

AS Clustering [2]

group nodes by Autonomous System

BGP Clustering [3]

group nodes by longest match BGP prefix

[1] Szymaniak, M. et al., Practical large-scale latency estimation. Computer Networks, 2008[2] Krishnamurthy, B., Wang, J., Topology modeling via cluster graphs. ACM SIGCOMM Workshop on Internet Measurement (IMW), 2001[3 ]Krishnamurthy, B., Wang, J., On network-aware clustering of web clients. ACM SIGCOMM, 2000

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How clustering impacts the accuracy?

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Evaluation setup

Maxmind + Routeviews 1month traceroute traces (Archipelago)

Two monitors: san-us (San Diego, US)

bcn-es (Barcelona, SP)*

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RTT error (bcn-es)

Geographic, AS n-agnostic, BGP

15% with more than 100% error

10% with more than 200% error

90% with less than 50% error

50% with less than 10% error

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Clustering reduces the number of measured destinationswithout loosing too much accuracy...

... can we reduce the number of source of measurements?

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Limit the number of measuring sourcesMake measurement sources collaborating

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Collaboration fundamentals

Popular destinations are measured by several nodes

Popularity d: #nodes measuring d

Different collaboration approaches Centralized authority/measurement source

Distributed measurements (ICS)

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How much reduction can we obtain?

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When can we observe measurement reduction?

Clustering reduces measurements if a cluster C covers at least two measured destinations

Collaboration reduces measurements if at least two topologically closed sources have to measure the same destination

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Evaluation setup

Campus traffic UCL, 1 link to Belnet @1Gbps

1 month full NetFlow traces 7.45 TB of filtered outgoing traffic

10K sources, 36M destinations

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Will collaboration help?

74% of the destinations are contacted byonly 1 source

Some destinations are contacted by1K+ sources!

Few percents are contacted by 10+ sources

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Will clustering help?

At least 45% of the clusters cover more than 10 nodes

1E+4

1E+5

1E+6

1E+7

1E+8

#dest24-agnBGPGeoAS

# of

des

tinat

ions

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Conclusion

Clustering/Collaboration to reduce measurement overhead

Reduction/accuracy tradeoff

Simple, though efficient techniques, tend to preserve accuracy

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Questions?

http://inl.info.ucl.ac.be

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Backup

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Combine Clustering and Collaboration

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Hop error (bcn-es)

0% more than 50% error

10% more than 50% error

bigger the n, smaller the error

Geographic, AS n-hybrid, n-agnostic, BGP

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Error variation inside clusters

75th percentile

50ty percentile

25th percentile

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The reduction

Collaboration only: 40% gain

20-hyb only: 62% gain

20-hyb + Collaboration: 99% gain

Collaboration + Clustering always better than clustering or collaboration only

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Are clustering and collaboration so different?

Let C, a cluster of nodes to measure

Let SC, the set of nodes measuring C

SC is cluster

nodes in SC can collaborate

=> SC is the set of collaborating nodes

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4.43.50.24.150.50.24.200.50.2

n-hybrid Clustering

4.0.0.0/8

...

4.128.0.0/94.0.0.0/9

4.23.88/23

4.43.50/24

...

A

B C

A

B

C...

BGP clusters

4.150.48.0/20

4.200.48.0/20

20-hybrid clusters

BGP prefixes can be huge:

=> Group nodes by longest match BGP prefix down to a given length

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traceroute to 4.150.50.2 (4.150.50.2), 30 hops max, 40 byte packets 1 192.168.1.1 (192.168.1.1) 3.535 ms 3.710 ms 3.967 ms 2 c-69-180-16-1.hsd1.ga.comcast.net (69.180.16.1) 11.983 ms 13.665 ms 14.154 ms 3 ge-2-1-ur01.a2atlanta.ga.atlanta.comcast.net (68.86.108.17) 17.101 ms 17.618 ms 18.499 ms 4 te-9-1-ur02.a2atlanta.ga.atlanta.comcast.net (68.85.232.38) 17.983 ms 18.840 ms 19.282 ms 5 te-9-3-ur01.b0atlanta.ga.atlanta.comcast.net (68.86.106.54) 20.043 ms 20.624 ms 21.441 ms 6 po-4-ar01.b0atlanta.ga.atlanta.comcast.net (68.86.106.9) 21.963 ms 8.144 ms 12.080 ms 7 pos-1-3-0-0-cr01.atlanta.ga.ibone.comcast.net (68.86.90.125) 14.802 ms 14.893 ms 15.513 ms 8 te-9-1.car1.Atlanta2.Level3.net (4.71.252.29) 113.775 ms 113.945 ms 114.383 ms 9 ae-62-51.ebr2.Atlanta2.Level3.net (4.68.103.29) 16.732 ms 17.245 ms 17.630 ms10 ae-3.ebr2.Chicago1.Level3.net (4.69.132.73) 44.394 ms 45.461 ms 44.855 ms11 ae-21-52.car1.Chicago1.Level3.net (4.68.101.34) 42.847 ms ae-21-54.car1.Chicago1.Level3.net (4.68.101.98) 41.702 ms ae-21-52.car1.Chicago1.Level3.net (4.68.101.34) 42.151 ms ...

traceroute to 4.200.50.2 (4.200.50.2), 30 hops max, 40 byte packets 1 192.168.1.1 (192.168.1.1) 1.800 ms 2.745 ms 3.339 ms 2 c-69-180-16-1.hsd1.ga.comcast.net (69.180.16.1) 11.581 ms 14.657 ms 15.170 ms 3 ge-2-1-ur01.a2atlanta.ga.atlanta.comcast.net (68.86.108.17) 13.574 ms 17.884 ms 18.412 ms 4 te-9-1-ur02.a2atlanta.ga.atlanta.comcast.net (68.85.232.38) 18.855 ms 19.299 ms 19.680 ms 5 te-9-3-ur01.b0atlanta.ga.atlanta.comcast.net (68.86.106.54) 20.549 ms 21.048 ms 21.990 ms 6 po-4-ar01.b0atlanta.ga.atlanta.comcast.net (68.86.106.9) 21.430 ms 7.738 ms 9.826 ms 7 pos-1-4-0-0-cr01.atlanta.ga.ibone.comcast.net (68.86.90.121) 11.735 ms 12.293 ms 15.289 ms 8 * * * 9 ae-62-51.ebr2.Atlanta2.Level3.net (4.68.103.29) 25.935 ms 26.458 ms 26.833 ms10 ae-63-60.ebr3.Atlanta2.Level3.net (4.69.138.4) 28.142 ms ae-73-70.ebr3.Atlanta2.Level3.net (4.69.138.20) 27.507 ms ae-63-60.ebr3.Atlanta2.Level3.net (4.69.138.4) 28.508 ms11 ae-7.ebr3.Dallas1.Level3.net (4.69.134.21) 50.636 ms 49.957 ms *12 ae-3.ebr2.LosAngeles1.Level3.net (4.69.132.77) 67.687 ms 61.311 ms 77.365 ms13 ae-72-72.csw2.LosAngeles1.Level3.net (4.69.137.22) 75.953 ms ae-62-62.csw1.LosAngeles1.Level3.net (4.69.137.18) 68.112 ms 67.813 ms14 ge-9-2.core1.LosAngeles1.Level3.net (4.68.102.167) 69.337 ms ge-5-2.core1.LosAngeles1.Level3.net (4.68.102.135) 68.195 ms ge-5-1.core1.LosAngeles1.Level3.net (4.68.102.71) 71.751 ms ...

Traceroute verdict*

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N-hybrid example

4.0.0.0/84.0.0.0/94.128.0.0/94.20.90.56/294.21.103.0/244.224.56.0/244.23.112.0/244.23.113.0/244.23.114.0/244.23.88.0/234.23.88.0/244.23.89.0/244.23.92.0/224.23.92.0/234.23.94.0/234.36.118.0/24

4.38.0.0/204.38.0.0/214.38.8.0/214.43.50.0/234.43.50.0/244.43.51.0/244.67.104.0/214.67.96.0/204.67.96.0/214.78.22.0/234.78.56.0/234.79.181.0/244.79.201.0/264.79.22.0/234.79.248.0/24

Level 3: 4.0.0.0/8 4.43.50.2?

BGP: 4.43.50.0/24 20-hybrid: 4.43.50.0/24

4.150.50.2? BGP: 4.128.0.0/9 20-hybrid: 4.150.48.0/20

4.200.50.2? BGP: 4.128.0.0/9 20-hybrid: 4.200.48.0/20

BGP (Routeviews)

Natural follow up, came for free → dessin

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References[1] Xie et al., P4P: Provider Portal for Applications, in Proc. ACM SIGCOMM, 2008

[2] Aggarwal et al., Can ISPs and P2P systems co-operate for improvedperformance?, ACM SIGCOMM Computer Communications Review (CCR),37(3):29–40, July 2007

[3] Saucez et al., Interdomain Traffic Engineering in a Locator/Identifier Separation Context, Internet Network Management Workshop 2008

[4] Dabek et al., Vivaldi, a decentralized network coordinated system. ACM SIGCOMM, 2004

[5] Krishnamurthy, B., Wang, J., Topology modeling via cluster graphs. ACM SIGCOMM Workshop on Internet Measurement (IMW), 2001

[6] Szymaniak, M. et al., Practical large-scale latency estimation. Computer Networks, 2008

[7 ]Krishnamurthy, B., Wang, J., On network-aware clustering of web clients. ACM SIGCOMM, 2000