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

1. Breaking for Commercials:
Characterizing Mobile Advertising
Narseo 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. The web is becoming mobile
+10
billion
+25
billion
* Downloads. As of January, 2012
2

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. Mobile Ad Ecosystem
App developer
(publisher)
£
Usage stats
Mobile User
4

5. Traffic Flow: AdMob
media.admob.com pagead2.googlesyndication.com googleads.g.doubleclick.net
http://media.admob.com/sdk-core-v40.js
GET: http://googleads.g.doubleclick.net/mads/gma?...
http://pagead2.googlesyndication.com/pagead/images/…
DNS Lookup
2 seconds
ENERGY
6 seconds
DNS Server
5

6. Research Question
How does mobile advertisement delivery
impact on the cellular network and the
battery life of the user?
6

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. Methodology
8

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. 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
Service
media.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. 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. Energy and spectrum
overhead
12

13. Mobile Apps Nature
Top AdMob Android Apps
Rank App Name Category Users (%)
1 Angry Birds Arcade 11.48
2 Advanced Task Killer System Tools 9.77
3 Soccer Scores (FotMob) Sports 3.53
4 Drag Racing Arcade 2.69
5 Bubble Blast Arcade 2.69
Mobile apps are usually offline by nature!!!!!
13

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 period
TCP Flows
time
81.1%, 68.2% and 69.7% of activity periods are pure for
Android, iPhone and iPad respectively
14

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 RNC
CDF
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. 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. 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 overhead
Inappropriate use of TCP connections adds 10%-40% energy overhead
17

18. Energy and spectrum-aware
ad networks
18

19. How to save energy?
1. Avoiding redundant transmissions
2. Reducing the number of transitions between the power
modes in mobile networks
Actually, …
 Ad traffic is mainly composed by static content
 +40% of volume are images
 Content distributed over CDNs
19

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. 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. Conclusions
22

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. Thank you for your attention!
nv240@cam.ac.uk
24

25. Additional Slides
25

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. Players sharing the cake
Android iPhone iPad
100
90
80
Popularity [%]
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
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Fl
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o v
o ir
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op x
d rip
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ub
ly
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Google services dominate the ad ecosystem but in
Analytic Services
27

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. 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. 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
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G eC ros
In Gri st ing
Ju o b L L
So pT IP
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Pe P/3 STI
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o a s
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th e t. S
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g C
e r di
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up
 Multiple organizations serve ad content
 Usually, ad nets use services from a single organization
 Google and Amazon are the preferred services
30