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Breaking for commercials: Characterizing Mobile Advertising

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Breaking for Commercials. Mobile ads characterization
ACM IMC'12
Boston

Published in: Technology
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Breaking for commercials: Characterizing Mobile Advertising

  1. 1. Breaking for Commercials:Characterizing Mobile AdvertisingNarseo Vallina-Rodriguez†, Jay Shah†, Alessandro Finamore‡Yan Grunenberger⋄, Hamed Haddadi§, Dina Papagiannaki⋄Jon Crowcroft†University of Cambridge†Politecnico di Torino‡Telefonica Research⋄Queen Mary University of London§ IMC 2012, Boston, MA
  2. 2. The web is becoming mobile +10 billion +25 billion* Downloads. As of January, 2012 2
  3. 3. The role of mobile ads 73% of Android Apps are free [Leontiadis,HotMobile’12] Mobile advertising is an important source of income for mobile developers 3
  4. 4. Mobile Ad Ecosystem App developer (publisher) £ Usage stats Mobile User 4
  5. 5. Traffic Flow: AdMob media.admob.com pagead2.googlesyndication.com googleads.g.doubleclick.net http://media.admob.com/sdk-core-v40.jsGET: http://googleads.g.doubleclick.net/mads/gma?... http://pagead2.googlesyndication.com/pagead/images/… DNS Lookup 2 seconds ENERGY 6 seconds DNS Server 5
  6. 6. Research QuestionHow does mobile advertisement delivery impact on the cellular network and the battery life of the user? 6
  7. 7. Paper contributions 1st characterization and evaluation of mobile ad traffic NO WEB ADS! Ad traffic is wasteful in terms of energy and spectrum  Only network activity for many apps  Strong component of users’ daily traffic  Static objects, frequently re-downloaded and distributed over CDNs  Do not tuned with the peculiarities of cellular networks Design of spectrum and energy-efficient ad delivery mechanism 7
  8. 8. Methodology 8
  9. 9. Identifying Ad Traffic Traces from app execution (tcpdump) + Controlled understanding of cause-effect relationships - Limited to ad networks used by developers Ad Networks documentation + Detailed - Not always available Inspecting traffic traces from cellular providers + Identify strategies from different players + Large scale impact - Noisy 9
  10. 10. Rule set  Set of 122 rules identifying:  Type of service (ad network, mediation service and analytics)  Type of action (request ad, configuration script, report click,…)  …Domain Object Path Type of Action Servicemedia.admob.com adk-core-v40.js Ad Network Configuration script*.g.doubleclick.net mads/gma Ad Network Get Ad*.googlesyndication.com pagead/ Ad Network Get static content*.g.doubleclick.net aclk Ad Network Report click Full rule set: http://www.retitlc.polito.it/finamore/mobileAdRegexDictionary.xlsx 10
  11. 11. Mobile traffic dataset Full day traces on a major European carrier +3 million mobile subscribers 1.7 billion TCP connections (including HTTP headers) 22TB of volume downloaded 11
  12. 12. Energy and spectrum overhead 12
  13. 13. Mobile Apps Nature Top AdMob Android AppsRank App Name Category Users (%)1 Angry Birds Arcade 11.482 Advanced Task Killer System Tools 9.773 Soccer Scores (FotMob) Sports 3.534 Drag Racing Arcade 2.695 Bubble Blast Arcade 2.69 Mobile apps are usually offline by nature!!!!! 13
  14. 14. Purity Definition: Flow A and flow B are part of the same activity period if: start(flowA) < start (flowB) < end (flowA) Pure activity period Mixed activity periodTCP Flows time 81.1%, 68.2% and 69.7% of activity periods are pure for Android, iPhone and iPad respectively 14
  15. 15. Session interleave Pure activity period Mixed activity period TCP Flows interleave time AdNets + Mediat. Serv. Analytics Serv. 1 1 0.8 0.8 Ad traffic is not tuned with the properties of cellular 0.6 Nearly 50% of Ad 0.6 networks, specially the RNCCDF requests happen 0.4 within 10 secs. 0.4 state machine Android Android 0.2 iPhone 0.2 iPhone iPad iPad 0 0 1 10 100 1000 10000 1 10 100 1000 10000 Interleave [s] Interleave [s] 15
  16. 16. L4 inefficiencies Bytes per second (Bps) AdMob 10000 1000 100 10 0 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 Time (s) Millennial Media Bytes per second (Bps) 10000 1000 100 10 0 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 Time (s)Inappropriate close of TCP connections adds additional energy and spectrum waste 16
  17. 17. Energy overhead AdMob Push AdMob Pull InMobi Millennial Media 260 300 ● 240 ● ● Current (mA) Current (mA) 220 ● 200 200 ● ● 180 ● ● ● 100 Airplane 160 Mode 20 40 60 80 20 40 60 80 20 40 60 80 20 40 60 80 Refresh Interval (s) 0 Doubling refresh rate adds 40% of energy overheadInappropriate use of TCP connections adds 10%-40% energy overhead 17
  18. 18. Energy and spectrum-aware ad networks 18
  19. 19. How to save energy?1. Avoiding redundant transmissions2. Reducing the number of transitions between the power modes in mobile networksActually, … Ad traffic is mainly composed by static content  +40% of volume are images  Content distributed over CDNs 19
  20. 20. Our approach: AdCache Exploits well-known techniques  Connectivity awareness  Flow coordination  Batching, caching and pre-fetching Supports all the features of existing ad networks 20
  21. 21. Prototype evaluation Existing ad networks AdMob Push AdMob Pull InMobi Millennial Media 260 ● 300 240 ● ● Current (mA) 220 ● 200 Current (mA) ● ● 200 180 ● ● ● 160 20 40 60 80 20 40 60 80 20 40 60 80 20 40 60 80 100 Refresh Interval (s)Airplane Prototype Evaluation Mode Animated Random Text Static 146 0 143 Fresh Start 140 ● ● 137 ● Current (mA) 134 ● ● 131 ● 122 Up to 50% energy savings! ● ● Cached Ad 119 ● 116 21
  22. 22. Conclusions 22
  23. 23. Summary Mobile app ads are responsible for energy, traffic and spectrum waste  Free applications are mainly offline  Lack of caching: users can waste hundreds of MB/day  Ad traffic is not tuned with the RNC state machine  Inappropriate use of TCP Simple traffic management techniques can be beneficial … more interesting results in the paper! … and lots of things to do! 3GPP != Ethernet 23
  24. 24. Thank you for your attention! nv240@cam.ac.uk 24
  25. 25. Additional Slides 25
  26. 26. Ad Traffic Volume 1 0.8 0.6 50% of Android devices, CDF 0.4 5% of volume Android 0.2 iPhone iPad 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Ratio Regex/Total Traffic Volume Strong component of users’ daily traffic Static objects, heavily re-downloaded  The top 10 objects account for 1% of the total volume 26
  27. 27. Players sharing the cake Android iPhone iPad 100 90 80Popularity [%] 70 Analytics 60 Services 50 Mediation 40 Services 30 Ad Networks 20 10 0 A M o In l.M A ob dia Ju on A pta M ar G jiva el i A yS t O A M Wh Bu b C l l M stly x M bfo Fl G rry dM dF i dM p re th d .A M e u il b m ic o v o ir o op x d rip r i er na ub ly t e Google services dominate the ad ecosystem but in Analytic Services 27
  28. 28. Traffic re-downloads Android Ratio ad/tot volume per device 1 50th perc avg 0.1 0.01 0.001 1 5 10 15 20 25 30 35 40 45 50 Object Popularity Rank Top 10 objects account for 1% of total volume 28
  29. 29. Volume and flows AdMob G.Analyt. AdWhirl Others 1 1 0.8 0.8 Fract. of Flows Fract. of Bytes 0.6 0.6 0.4 0.4 0.2 0.2 0 0 A Ip Ip A Ip Ip nd nd h a h a d d on on ro ro e e id id Google services dominate the ad ecosystem also in terms of flows and volume 29
  30. 30. Ad content distribution Flurry 1 1 G.Analytics 1 0.9 Mopub 1 Mobfox 1 0.8 Burstly .44 .26 .30 0.7 MobClix 1 AdWhirl 1 0.6 iAd 1 0.5 GreyStripe 1 Mojiva 1 0.4 AdMarvel 1 Jumptap .85 .15 0.3 AdFonic 1 0.2 InMobi 1 Mill.Media 1 0.1 AdMob .73 .11 .16 0 G A o gl e A am a G a zo Ed b a l G eC ros In Gri st ing Ju o b L L So pT IP 80 tlay p pac W HO r Pe P/3 STI A r 1 PO G O p M os N T o k m i lo n o a s p H I th e t. S M d m i C C 0 e e 2 N f a S g C e r di aG ro e up Multiple organizations serve ad content Usually, ad nets use services from a single organization Google and Amazon are the preferred services 30

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