Measuring IPv6 Performance Nov 25, 2016 TU Munich, Germany Prof. - - PowerPoint PPT Presentation

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Measuring IPv6 Performance Nov 25, 2016 TU Munich, Germany Prof. - - PowerPoint PPT Presentation

Overview Measuring IPv6 Performance Nov 25, 2016 TU Munich, Germany Prof. Dr. Jrg Ott Aalto University, Finland Saba Ahsan SamKnows Limited, London, UK Steffje Jacob Eravuchira | Sam Crawford Jacobs University Bremen, Germany Prof. Dr.


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SLIDE 1

Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

Measuring IPv6 Performance

Vaibhav Bajpai Jacobs University, Bremen

Munich Internet Research Retreat, Raitenhaslach, Germany

Joint Work with

  • Prof. Dr. Jürgen Schönwälder

Jacobs University Bremen, Germany Steffje Jacob Eravuchira | Sam Crawford SamKnows Limited, London, UK Saba Ahsan Aalto University, Finland

  • Prof. Dr. Jörg Ott

TU Munich, Germany Nov 25, 2016

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Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

Tiis research would not have been possible without these amazing people!

– – – –

  • What’s ¡missing: ¡Many ¡things, ¡but ¡in

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SLIDE 3

Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

Overview

TCP Connect Times Happy Eyeballs YouTube Web Similarity Measuring IPv6 Performance Ahsan et al. [1] Bajpai et al. [2] Eravuchira et al. [3] Bajpai et al. [4]

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Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

Overview | Motivation

▶ Literature has largely focussed on measuring IPv6 adoption [5, 6, 7] (′10 −′14).

▶ Addressing ▶ Naming ▶ Routing ▶ Reachability

▶ Very little work [8] on measuring performance of service delivery over IPv6. ▶ Largely due to lack of available content over IPv6. ▶ A number of signifjcant events occured during the span of this dissertation.

▶ IANA IPv4 Address Exhaustion [9] ▶ World IPv6 Day ′11 [10] ▶ World IPv6 Launch Day ′12 [11] ▶ RIR IPv4 Address Exhaustion [9]

APNIC Apr′11 RIPE Sep′12 LACNIC Jun′14 ARIN Sep′15

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Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

Overview | Motivation

▶ Large IPv6 broadband rollouts1 [4]. ▶ Global IPv6 adoption [12].

09/2012 0.85% 11/2016 12.46% Belgium 47.38% United States 30.12% Switzerland 26.95% Germany 26.61%

▶ Tiis study closes the gap. ▶ It measures IPv6 performance of operational dual-stacked content delivery services.

1Comcast, Deutsche Telekom AG, AT&T, Verizon Wireless, T-Mobile USA 5 / 27

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Overview TCP connect times

Trends Who connects faster? Preference

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Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

Overview | Measurement Trial

NETWORK TYPE # RESIDENTIAL 55 NREN / RESEARCH 11 BUSINESS / DATACENTER 09 OPERATOR LAB 04 IXP 01 RIR # RIPE 42 ARIN 29 APNIC 07 AFRINIC 01 LACNIC 01

We measure from 80 dual-stacked SamKnows [13] probes.

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Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

Overview | TCP Connect Times

TCP Connect Times Happy Eyeballs YouTube Web Similarity Measuring IPv6 Performance Ahsan et al. [1] Bajpai et al. [2] Eravuchira et al. [3] Bajpai et al. [4]

* entries are papers currently under review.

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SLIDE 8

Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

TCP Connect Times | Trends (2013 - 2016)

∆sa(u) = t4(u) − t6(u)

where t(u) is the time taken to establish TCP connection to website u.

−150 −100 −50 50 www.bing.com www.facebook.com www.wikipedia.org www.youtube.com 2013 2014 2015 2016 02 05 08 11 02 05 08 11 02 05 08 11 02 05 −60 −40 −20 20 ∆sa (ms) www.blogspot.* www.google.* www.netflix.com www.yahoo.com

▶ TCP connect times to popular websites over IPv6 have considerably improved over time.

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Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

TCP Connect Times | Who connects faster?

ALEXA top 10K websites (as of May 2016):

▶ 18% are faster over IPv6. ▶ 91% of the rest are at most 1 ms slower. ▶ 3% are at least 10 ms slower. ▶ 1% are at least 100 ms slower.

−1.0 −0.5 0.0 0.5 1.0 ∆sa (ms) 0.0 0.2 0.4 0.6 0.8 1.0 CDF netflix yahoo google youtube linkedin microsoft facebook wikipedia ALEXA (10K) [05/2016] ∆sa(u) = t4(u) − t6(u) 9 / 27

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Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

TCP Connect Times | IPv6 Preference

96% 97% 98% 99% 100% 0.0 0.2 0.4 0.6 0.8 1.0 CCDF PROBES (80) ALEXA (10K) [2013 - 2016] Preference (300 ms)

▶ A 300 ms HE timer value leaves 2% chance for IPv4. ▶ 99% of top 10K ALEXA prefer IPv6 98% of time.

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Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

Overview | Measuring YouTube

TCP Connect Times Happy Eyeballs YouTube Web Similarity Measuring IPv6 Performance Ahsan et al. [1] Bajpai et al. [2] Eravuchira et al. [3] Bajpai et al. [4] * entries are papers currently under review.

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SLIDE 12

Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

YouTube | Latency

▶ TCP connect times

▶ < 1 ms slower over IPv6 ▶ Higher towards webpages

▶ Prebufgering durations

▶ > 25 ms slower over IPv6

▶ Startup delay

▶ > 100 ms slower over IPv6

−5 −4 −3 −2 −1 ∆t (ms) TCP Connect Times Web −0.4 −0.3 −0.2 −0.1 0.0 ∆t (ms) TCP Connect Times Audio Video −120 −80 −40 ∆p (ms) Prebuffering Duration Oct Jan 2015 Apr Jul Oct Jan 2016 Apr −400 −300 −200 −100 ∆s (ms) Startup Delay

∆t(y) = tc4(y) − tc6(y) ∆p(y) = pd4(y) − pd6(y) ∆s(y) = sd4(y) − sd6(y)

Latency is consistently higher over IPv6.

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SLIDE 13

Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

YouTube | IPv6 Preference

95% 96% 97% 98% 99% 100% 0.0 0.2 0.4 0.6 0.8 1.0 CCDF Web (458) Audio (458) Video (458)

['14 - '16]

IPv6 Preference ▶ Media streams are preferred over IPv6 more than 97% of the time.

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SLIDE 14

Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

Overview | Measuring Efgects of Happy Eyeballs

TCP Connect Times Happy Eyeballs YouTube Web Similarity Measuring IPv6 Performance Ahsan et al. [1] Bajpai et al. [2] Eravuchira et al. [3] Bajpai et al. [4] * entries are papers currently under review.

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SLIDE 15

Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

Happy Eyeballs | Preference

10-1 100 101 102 103 104 TCP Connect Times (ms) 0.0 0.2 0.4 0.6 0.8 1.0 CDF 300 ms

IPv6 (89K) IPv4 (89K)

[2013 - 2016]

▶ Only ∼1% of samples above HE timer value > 300 ms

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Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

Happy Eyeballs | Slowness

Samples where HE prefers IPv6 −

▶ HE prefers slower IPv6

connections 90% of the time.

▶ Absolute difgerence is not that far

apart from IPv4

▶ 30% − at least 1 ms slower. ▶ 7% − at least 10 ms slower.

−40 −30 −20 −10 10 ∆sa (ms) 0.0 0.2 0.4 0.6 0.8 1.0 CDF 1% 2% 7% 30% 93% 99% 89K [2013 - 2016]

∆sa(u) = t4(u) − t6(u) ∆sr(u) = t4(u)−t6(u)

t4(u)

Can a lower HE timer provide same preference over IPv6 but not penalise IPv4 when it’s faster?

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SLIDE 17

Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

Happy Eyeballs | Lowering HE Timer

▶ We control two2 parameters and

lower the HE timer value.

▶ Each data point is the 1th

percentile preference towards ALEXA 10K websites.

▶ Lowering to 150 ms retains

preference levels over IPv6.

▶ We get margin benefjt of 10%

(18.9K) because timer cuts early.

50 100 150 200 250 300 HE timer (ms) 0% 20% 40% 60% 80% 100% Preference 150 ms ALEXA (10K) ['13 - '16] −0.6 −0.4 −0.2 0.0 0.2 ∆sr 0.0 0.2 0.4 0.6 0.8 1.0 CDF 80% 189K ['13 - '16] Slowness (150ms) 299% ALEXA top 10K websites prefer IPv6 connections 98.6% of the time 17 / 27

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SLIDE 18

Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

Overview | Measuring Web Similarity

TCP Connect Times Happy Eyeballs YouTube Web Similarity Measuring IPv6 Performance Ahsan et al. [1] Bajpai et al. [2] Eravuchira et al. [3] Bajpai et al. [4] * entries are papers currently under review.

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Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

Web Similarity | Success Rate

Can we fetch all webpage elements over IPv6?

▶ 27% of websites show some rate of failure over IPv6. ▶ 9% exhibit more than 50% failures over IPv6. ▶ 6% show complete failure (0% success) over IPv6.

20 40 60 80 100 Success Rate (%) 0.0 0.2 0.4 0.6 0.8 1.0 CDF IPv6 (100) IPv4 (100)

# Webpage Success Rate (%) W6LD IPv6(↓) IPv4 01 www.bing.com 100 ✓ 02 www.detik.com 100 ✓ 03 www.engadget.com 100 ✓ 04 www.nifty.com 100 05 www.qq.com 100 06 www.sakura.ne.jp 100 07 www.flipkart.com 09 99 ✓ 08 www.folha.uol.com.br 13 100 09 www.aol.com 48 100 ✓ 10 www.comcast.net 52 100 ✓ 11 www.yahoo.com 72 100 ✓ 12 www.mozilla.org 84 100 ✓ 13 www.orange.fr 86 100 ✓ 14 www.seznam.cz 89 100 ✓ 15 www.mobile.de 90 100 ✓ 16 www.wikimedia.org 90 100 17 www.t-online.de 93 100 ✓ 18 www.free.fr 95 100 19 www.usps.com 95 100 20 www.vk.com 95 100 ✓ 21 www.wikipedia.org 95 100 ✓ 22 www.wiktionary.org 95 100 23 www.elmundo.es 96 100 ✓ 24 www.uol.com.br 96 100 ✓ 25 www.marca.com 97 100 ✓ 26 www.terra.com.br 98 100 ✓ 27 www.youm7.com 99 100 19 / 27

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Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

Web Similarity | Success Rate

ALEXA top 100 dual-stacked websites: ▶ 6% show complete failure over IPv6.

# Webpage Success Rate (%) W6LD IPv6(↓) IPv4 01 www.bing.com 100 ✓ 02 www.detik.com 100 ✓ 03 www.engadget.com 100 ✓ 04 www.nifty.com 100 05 www.qq.com 100 06 www.sakura.ne.jp 100

▶ Metrics that measure IPv6 adoption should account for changes in IPv6-readiness.

100 101 102 103 www.bing.com 102 103 www.detik.com 100 101 102 103 www.engadget.com 102 103 www.nifty.com 100 101 102 103 104 www.qq.com Jan 2013 Jan 2014 Jan 2015 Jan 2016 Jul Jul Jul 102 103 www.sakura.ne.jp IPv6 IPv4 TCP Connect Times (ms)

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Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

Web Similarity | Causality Analysis

Where in the network does the failure occur?

30 60 90 www.youm7.com (1%) www.terra.com.br (2%) www.marca.com (3%) www.uol.com.br (4%) www.elmundo.es (4%) www.wiktionary.org (5%) www.wikipedia.org (5%) www.vk.com (5%) www.usps.com (5%) www.free.fr (5%) www.t-online.de (7%) www.wikimedia.org (10%) www.mobile.de (10%) www.seznam.cz (11%) www.orange.fr (14%) www.mozilla.org (16%) www.yahoo.com (28%) www.comcast.net (48%) www.aol.com (52%) www.folha.uol.com.br (87%) www.flipkart.com (91%) www.sakura.ne.jp (100%) www.qq.com (100%) www.nifty.com (100%) www.engadget.com (100%) www.detik.com (100%) www.bing.com (100%) Network Level

CURLE_OK CURLE_COULDNT_RESOLVE_HOST CURLE_COULDNT_CONNECT CURLE_OPERATION_TIMEDOUT CURLE_GOT_NOTHING CURLE_RECV_ERROR

30 60 90 Contribution (%) Content Level

*/css */html */javascript, */json */octet-stream */plain */rdf */xml image/*

30 60 90 Service Level

SAME ORIGIN CROSS ORIGIN

Website failing over IPv6

CURLE_COULDNT_RESOLVE_HOST is the major contributor to failure rates.

▶ AAAA entries missing for these webpage elements in the DNS.

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Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

Web Similarity | Causality Analysis

Which type of objects fail more than others?

30 60 90 www.youm7.com (1%) www.terra.com.br (2%) www.marca.com (3%) www.uol.com.br (4%) www.elmundo.es (4%) www.wiktionary.org (5%) www.wikipedia.org (5%) www.vk.com (5%) www.usps.com (5%) www.free.fr (5%) www.t-online.de (7%) www.wikimedia.org (10%) www.mobile.de (10%) www.seznam.cz (11%) www.orange.fr (14%) www.mozilla.org (16%) www.yahoo.com (28%) www.comcast.net (48%) www.aol.com (52%) www.folha.uol.com.br (87%) www.flipkart.com (91%) www.sakura.ne.jp (100%) www.qq.com (100%) www.nifty.com (100%) www.engadget.com (100%) www.detik.com (100%) www.bing.com (100%) Network Level

CURLE_OK CURLE_COULDNT_RESOLVE_HOST CURLE_COULDNT_CONNECT CURLE_OPERATION_TIMEDOUT CURLE_GOT_NOTHING CURLE_RECV_ERROR

30 60 90 Contribution (%) Content Level

*/css */html */javascript, */json */octet-stream */plain */rdf */xml image/*

30 60 90 Service Level

SAME ORIGIN CROSS ORIGIN

Website failing over IPv6

image/*, */javascript, */json and */css content contribute to the majority of the failure over IPv6. 22 / 27

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Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

Web Similarity | Causality Analysis

Where do the failing objects originate from?

30 60 90 www.youm7.com (1%) www.terra.com.br (2%) www.marca.com (3%) www.uol.com.br (4%) www.elmundo.es (4%) www.wiktionary.org (5%) www.wikipedia.org (5%) www.vk.com (5%) www.usps.com (5%) www.free.fr (5%) www.t-online.de (7%) www.wikimedia.org (10%) www.mobile.de (10%) www.seznam.cz (11%) www.orange.fr (14%) www.mozilla.org (16%) www.yahoo.com (28%) www.comcast.net (48%) www.aol.com (52%) www.folha.uol.com.br (87%) www.flipkart.com (91%) www.sakura.ne.jp (100%) www.qq.com (100%) www.nifty.com (100%) www.engadget.com (100%) www.detik.com (100%) www.bing.com (100%) Network Level

CURLE_OK CURLE_COULDNT_RESOLVE_HOST CURLE_COULDNT_CONNECT CURLE_OPERATION_TIMEDOUT CURLE_GOT_NOTHING CURLE_RECV_ERROR

30 60 90 Contribution (%) Content Level

*/css */html */javascript, */json */octet-stream */plain */rdf */xml image/*

30 60 90 Service Level

SAME ORIGIN CROSS ORIGIN

Website failing over IPv6

▶ Both same and cross origin sources contribute to the failure of webpage elements over IPv6.

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SLIDE 24

Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

Web Similarity | Causality Analysis

What is failure contribution of same-origin sources?

30 60 90 Contribution (%) www.youm7.com (1%) www.terra.com.br (2%) www.marca.com (3%) www.uol.com.br (4%) www.elmundo.es (4%) www.wiktionary.org (5%) www.wikipedia.org (5%) www.vk.com (5%) www.usps.com (5%) www.free.fr (5%) www.t-online.de (7%) www.wikimedia.org (10%) www.mobile.de (10%) www.seznam.cz (11%) www.orange.fr (14%) www.mozilla.org (16%) www.yahoo.com (28%) www.comcast.net (48%) www.aol.com (52%) www.folha.uol.com.br (87%) www.flipkart.com (91%) www.sakura.ne.jp (100%) www.qq.com (100%) www.nifty.com (100%) www.engadget.com (100%) www.detik.com (100%) www.bing.com (100%) *.youm7.com *.terra.com.br *.marca.com *.uol.com.br *.elmundo.es *.wiktionary.org *.wikipedia.org *.vk.com *.usps.com *.free.fr *.t-online.de *.wikimedia.org *.mobile.de *.seznam.cz *.orange.fr *.mozilla.org *.yahoo.com *.comcast.net *.aol.com *.uol.com.br *.flipkart.com *.sakura.ne.jp *.qq.com *.nifty.com *.engadget.com *.detik.com *.bing.com SAME ORIGIN

▶ 12% of websites have more than 50% webpage elements that belong to the same origin source and fail over IPv6.

# Webpage Same Origin (↓) 01 www.bing.com 100% 02 www.detik.com 100% 03 www.engadget.com 100% 04 www.nifty.com 100% 05 www.usps.com 100% 06 www.qq.com 100% 07 www.sakura.ne.jp 100% 08 www.comcast.net 85% 09 www.yahoo.com 83% 10 www.terra.com.br 74% 11 www.marca.com 70% 12 www.wikimedia.org 65% 13 www.elmundo.es 37% 14 www.vk.com 31% 15 www.t-online.de 30% 16 www.youm7.com 24% 17 www.wiktionary.org 22% 18 www.wikipedia.org 22% 19 www.free.fr 13% 20 www.folha.uol.com.br 12% 21 www.mozilla.org 7% 22 www.uol.com.br 7% 23 www.mobile.de 7% 24 www.aol.com 5% 25 www.orange.fr 5% 26 www.seznam.cz 4% 27 www.flipkart.com 1% 24 / 27

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SLIDE 25

Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

Web Similarity | Causality Analysis

What is failure contribution of cross-origin sources?

30 60 90 Contribution (%) www.youm7.com (1%) www.terra.com.br (2%) www.marca.com (3%) www.uol.com.br (4%) www.elmundo.es (4%) www.wiktionary.org (5%) www.wikipedia.org (5%) www.vk.com (5%) www.usps.com (5%) www.free.fr (5%) www.t-online.de (7%) www.wikimedia.org (10%) www.mobile.de (10%) www.seznam.cz (11%) www.orange.fr (14%) www.mozilla.org (16%) www.yahoo.com (28%) www.comcast.net (48%) www.aol.com (52%) www.folha.uol.com.br (87%) www.flipkart.com (91%) www.sakura.ne.jp (100%) www.qq.com (100%) www.nifty.com (100%) www.engadget.com (100%) www.detik.com (100%) www.bing.com (100%) CROSS ORIGIN

*.adition.com *.ajax.googleapis.com *.aolcdn.com *.cimcontent.net *.creativecommons.org *.d5nxst8fruw4z.cloudfront.net *.demdex.net *.dmtry.com *.doubleclick.net *.el-mundo.net *.elmundo.es *.expansion.com *.f.i.uol.com.br *.flixcart.com *.globaliza.com *.images1.folha.com.br *.imedia.cz *.imguol.com *.imguol.com.br *.interactivemedia.net *.ioam.de *.jsuol.com.br *.leguide.com *.ligatus.com *.mail.ru *.mozilla.net *.navdmp.com *.netbiscuits.net *.omtrdc.net *.optimizely.com *.outbrain.com *.proxad.net *.quantserve.com *.sblog.cz *.scorecardresearch.com *.szn.cz *.tag.navdmp.com *.telva.com *.theadex.com *.toi.de *.trrsf.com *.unidadeditorial.es *.voila.fr *.woopic.com *.www1.folha.com.br *.xiti.com

▶ Some of the cross-origin sources contribute to the failure of multiple websites.

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SLIDE 26

Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

Takeway

▶ ISPs should ensure CDN caches are dual-stacked form the very outset. ▶ ISPs should put latency as a fjrst-class citizen. ▶ Measurements should be used to inform protocol-engineering. ▶ Metrics that measure IPv6 adoption should account for changes in IPv6-readiness. ▶ Limiting to root webpage can lead to overestimation of IPv6 adoption numbers. ▶ Let’s deem a website IPv6-ready when there is no partial failure over IPv6.

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SLIDE 27

Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

Impact

▶ Measuring IPv6 Performance

▶ Measuring TCP Connect Times [NETWORKING ′15] ▶ Measuring YouTube Performance [PAM ′15] ▶ Measuring Efgects of Happy Eyeballs [ANRW ′16] ▶ Measuring Web Similarity [CNSM ′16]

▶ Relevance:

▶ Network operators in early stages of IPv6 deployment. ▶ Content providers to see how their service delivery over IPv6 compares to IPv4. ▶ Drive related standards work in the IETF.

www.vaibhavbajpai.com v.bajpai@jacobs-university.de | @bajpaivaibhav

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SLIDE 28

Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

Takeway Q/A

References

[1]

  • S. Ahsan, V. Bajpai, J. Ott, and J. Schönwälder, “Measuring YouTube

from Dual-Stacked Hosts,” in Passive and Active Measurement - 16th International Conference, PAM 2015, New York, NY, USA, March 19-20, 2015, Proceedings, 2015, pp. 249–261. [Online]. Available: http://dx.doi.org/10.1007/978-3-319-15509-8_19 [2]

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SLIDE 29

Overview TCP connect times

Trends Who connects faster? Preference

YouTube

Latency Preference

Happy Eyeballs

Preference Slowness Lowering HE Timer

Web Similarity

Success Rate Causality Analysis

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