[PPT] - using Large Scale Measurement Platforms Understanding the Impact of PowerPoint Presentation

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Introduction

Motivation Research Contributions

Part I Part II

Web Similarity

Introduction Methodology Results Takeaway

Q/A

Understanding the Impact of Network Infrastructure Changes using Large Scale Measurement Platforms

Vaibhav Bajpai Jacobs University, Bremen

TUM Seminar, Raitenhaslach

Dissertation Committee

Prof. Dr. Jürgen Schönwälder

Jacobs University Bremen, Germany

Dr. Kinga Lipskoch

Jacobs University Bremen, Germany

Prof. Dr. Filip De Turck

University of Ghent, Belgium Oct 11, 2016

Supported by: Flamingo Project: fmamingo-project.eu Leone Project: leone-project.eu 1 / 26

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Introduction

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

–

– – – –

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

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Introduction | Motivation

▶ Large-scale Internet Measurement Platform

An infrastructure of dedicated hardware probes that periodically runs Internet measurement tests to satisfy specifjc use-case requirements.

▶ Use-cases

Since ′98: Topology Mapping (Mature)

CAIDA Ark [1], DIMES [2], iPlane [3] et al.

Since ′08: Network Performance

SamKnows [4], BISmark [5], RIPE Atlas [6, 4], PerfSONAR [4] et al.

▶ Measure performance and reliability of broadband access networks ▶ Facilitate regulators to make better policy decisions. 3 / 26

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Introduction | Motivation

▶ Tiis dissertation expands the goal:

Since ′98: Topology Mapping Since ′08: Network Performance

▶ Measure performance and reliability of broadband access networks ▶ Facilitate regulators to make better policy decisions. ▶ Understand the impact of network infrastructure changes

1. Measuring IPv6 Performance
2. Measuring Access Network Performance

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Introduction

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Introduction Methodology Results Takeaway

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Introduction | Research Contributions

1. Survey on Internet Performance Measurement Platforms

[COMST ′15]

2. 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]

3. Measuring Access Network Performance

▶ RIPE Atlas Vantage Point Selection [∗] ▶ Dissecting Last-mile Latency Characteristics [∗] ▶ Lessons Learned from using RIPE Atlas [CCR ′15]

* entries are papers currently under review.

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Introduction

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Part I

Performance Measurements Fixed-line Access Operational Support Standardization Efgorts Internet Measurement Platforms Mobile Access Topology Discovery IETF LMAP IETF IPPM IETF xrblock BBF IEEE ITU-T SamKnows BISmark Dasu Netradar Portolan RIPE Atlas perfSONAR Benoit Donnet et al. [7] Hamed Haddadi et al. [8] Benoit Donnet et al. [9] Bajpai et al. [4]

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Introduction

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Part I Part II

Web Similarity

Introduction Methodology Results Takeaway

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Part II | Overview

TCP Connect Times Happy Eyeballs YouTube Web Similarity Measuring IPv6 Performance Ahsan et al. [10] Bajpai et al. [11] Eravuchira et al. [12] Bajpai et al. [13]

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Part II | Motivation

▶ Literature has largely focussed on measuring IPv6 adoption [14, 15, 16] (′10 −′14).

▶ Addressing ▶ Naming ▶ Routing ▶ Reachability

▶ Very little work [17] 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 [18] ▶ World IPv6 Day ′11 [19] ▶ World IPv6 Launch Day ′12 [20] ▶ RIR IPv4 Address Exhaustion [18]

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

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Introduction

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Part II | Motivation

▶ Large IPv6 broadband rollouts1 [13]. ▶ Global IPv6 adoption [21].

09/2012 0.85% 05/2016 11.48% Belgium 40.49% Switzerland 27.38% United States 23.62% Germany 21.41%

▶ 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 9 / 26

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Introduction

Motivation Research Contributions

Part I Part II

Web Similarity

Introduction Methodology Results Takeaway

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Part II | Measuring Web Similarity

TCP Connect Times Happy Eyeballs YouTube Web Similarity Measuring IPv6 Performance Ahsan et al. [10] Bajpai et al. [11] Eravuchira et al. [12] Bajpai et al. [13]

* entries are papers currently under review.

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Web Similarity | Introduction

Recent work [13], [17], [15] has compared performance

f dual-stacked websites
ver IPv4 and IPv6.

No study comparing web similarity over IPv4 / IPv6.

2010 2011 2012 2013 2014 2015 2016 0.0% 2.0% 4.0% 6.0% 8.0% 10.0% Websites W6D W6LD Websites with AAAA entries ALEXA 1M

http://www.employees.org/∼dwing/aaaa-stats

We want to know:

▶ How similar are webpages accessed over IPv6 to their IPv4 counterparts? ▶ What factors contribute to the dissimilarity over IPv4 and IPv6?

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Introduction

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Web Similarity | Introduction

We measure against ALEXA top 100 dual-stacked websites.

1. simweb : A tool for measuring web similarity over IPv4 and IPv6.
2. Websites (27%) have some fraction of webpage elements failing over IPv6.
3. Failure rates over IPv6 are largely due to DNS resolution error on images, js and CSS.
4. Both same-origin and cross-origin sources contribute to the failure rates over IPv6.

To the best of our knowledge, this is the fjrst study to: ▶ Measure webpage similarity over IPv4 and IPv6. ▶ Investigate IPv6 adoption that goes beyond the root page of a dual-stacked website.

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Web Similarity | Methodology

We use 2 well-known webpage complexity metrics from literature [22, 23]:

1. Content Complexity

Tie number & size of fetched webpage elements.

2. Service Complexity

Tie number of same-origin & cross-origin sources.

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Introduction

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Web Similarity | Methodology

▶ We use the ALEXA top 100 dual-stacked websites

as measurement targets [13].

1. www.google.com
2. www.facebook.com
3. www.youtube.com
4. www.yahoo.com
5. www.wikipedia.org
6. www.qq.com
7. www.blogspot.com
8. …

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Web Similarity | Methodology

Tie simweb test:

▶ runs twice (once for each AF). ▶ repeats every hour. ▶ uses user-agent string: Mozilla/4.0

DSL/Cable Modem SamKnows Tests Probe

ALEXA Dual-Stacked Top 100 results HTTPS POST HTTP GET

IPv6 IPv4

simweb Data Collector

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Introduction

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Part I Part II

Web Similarity

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Web Similarity | Methodology

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 probes.

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Introduction

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Introduction Methodology Results Takeaway

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Web Similarity | Results

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 17 / 26

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Web Similarity | Results

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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Introduction

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Part I Part II

Web Similarity

Introduction Methodology Results Takeaway

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Web Similarity | Results

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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Introduction

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Part I Part II

Web Similarity

Introduction Methodology Results Takeaway

Q/A

Web Similarity | Results

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. 20 / 26

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Introduction

Motivation Research Contributions

Part I Part II

Web Similarity

Introduction Methodology Results Takeaway

Q/A

Web Similarity | Results

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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Introduction

Motivation Research Contributions

Part I Part II

Web Similarity

Introduction Methodology Results Takeaway

Q/A

Web Similarity | Results

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% 22 / 26

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Introduction

Motivation Research Contributions

Part I Part II

Web Similarity

Introduction Methodology Results Takeaway

Q/A

Web Similarity | Results

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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Introduction

Motivation Research Contributions

Part I Part II

Web Similarity

Introduction Methodology Results Takeaway

Q/A

Web Similarity | Results

Which cross-origin sources span across multiple failing websites?

0 10 20 30 40 50 60 70 80 Contribution (%) *.adition.com *.creativecommons.org *.doubleclick.net *.el-mundo.net *.expansion.com *.facebook.com *.google.com *.ligatus.com *.outbrain.com *.scorecardresearch.com *.unidadeditorial.es *.wikimedia.org #2 #3 #5 #2 #2 #2 #4 #2 #2 #3 #2 #2 CROSS ORIGIN

▶

doubleclick.net spans 5 websites with a 0.54%

median contribution to failure rates. ▶

creativecommons.org has 76% median

contribution to the failure rate of 3 websites.

CROSS ORIGIN MEDIAN *.creativecommons.org 76.33% *.el-mundo.net 31.41% *.adition.com 14.20% *.ligatus.com 4.98% *.wikimedia.org 1.40% *.expansion.com 1.21% *.scorecardresearch.com 1.19% *.outbrain.com 1.06% *.unidadeditorial.es 0.94% *.doubleclick.net 0.54% *.google.com 0.31% *.facebook.com 0.06% 24 / 26

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Introduction

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Part I Part II

Web Similarity

Introduction Methodology Results Takeaway

Q/A

Web Similarity | Takeaway

▶ Metrics that measure IPv6 adoption should account for changes in IPv6-readiness. ▶ Limiting to root webpage can lead to overestimation of IPv6 adoption numbers. ▶ Unclear whether websites with failure rates can be deemed IPv6-ready. ▶ Few cross-origin sources once IPv6 enabled will help large number of websites at once.

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Introduction

Motivation Research Contributions

Part I Part II

Web Similarity

Introduction Methodology Results Takeaway

Q/A

1. Survey on Internet Performance Measurement Platforms

[COMST ′15]

2. 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]

3. Measuring Access Network Performance

▶ RIPE Atlas Vantage Point Selection [∗] ▶ Dissecting Last-mile Latency Characteristics [∗] ▶ Lessons Learned from using RIPE Atlas [CCR ′15]

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

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References

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Introduction Methodology Results Takeaway

Q/A [11]

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eyeballs,” in Proceedings of the 2016 Applied Networking Research Workshop, ser. ANRW ’16. New York, NY, USA: ACM, 2016, pp. 38–44. [Online]. Available: http://doi.acm.org/10.1145/2959424.2959429 [12]

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