cameo: a middleware for mobile advertisement delivery
DESCRIPTION
CAMEO: A Middleware for Mobile Advertisement Delivery. Azeem Khan† , Kasthuri Jayarajah *, Dongsu Han ‡, Archan Misra *, Rajesh Balan *, Srinivasan Seshan ‡ * Singapore Management University ‡ Carnegie Mellon University †Oriental Institute of Management. Motivations. - PowerPoint PPT PresentationTRANSCRIPT
CAMEO: A Middleware for Mobile Advertisement Delivery
Azeem Khan†, Kasthuri Jayarajah*, Dongsu Han ‡, Archan Misra*, Rajesh Balan*, Srinivasan Seshan ‡
* Singapore Management University‡ Carnegie Mellon University
†Oriental Institute of Management
Motivations
• Improving performance of mobile advertisements delivery– Decreasing bandwidth usage– Reducing energy consumption on mobile
• Introduce monetization of advertisements by users and ISPs
Research Challenges
• Reduce overheads in delivering ads
• Provide offline access to advertisement selection
• Framework that enables dynamic negotiation in trading advertisements for connectivity
Background for addressing performance issues
• Data collection process for advertisements– Contexts: app, location, device type,
OS, carriers– Period: Every 1 minute, 2 weeks – 2
months– Procedure: Scripts on computers in
USA, Asia, Europe• Observations
– Top 100 ads account for > 50% of views– 37% of ads are seen even after a day– 2/3 or more of ad content is redundant
across ads (templated HTML)– Country specific ads (overlap < 6%)
37% ads seen after a day
48% ads seen after 6 hours
Background study for performance issues• Data collection procedure for
users– Who: 20 participants on SMU
campus for 1 month– Procedure: Custom LiveLabs
app running as a monitoring service on Android 4.0+
• Observations– On average, users switch between
WiFi and 3G networks 2-4 times per day
– Users are often connected to WiFi when charging phone
– Users are on 3G network more than 50% of the time
Heavy WiFi usageWiFi connected
Challenge #1: Reduce overheadsHow?Pre-fetching and caching of advertisements.
Why Both?
• pre-fetching– CAMEO exploits the fact that users are often on cheaper WiFi networks
more than once a day!– Advertisement contexts that matter such as location and app can be
predicted• caching
– Ads are repeated– Small number of ads account for most ad views– Overheads per ad are avoided
Caching and Pre-Fetching
CONTEXT PREDICTOR AD MANAGER
APP #1 APP #2
CAMEO
AN
AN
AN = ADVERTISEMENT NETWORK
CACHE
More than 70% savings in ad
related bandwidth is observed…
Challenge # 2: Offline Access to Ads
• Online selection of ads– AN advertisement selection (ANAS)• Bulk pre-fetch of ads, online ad selection by AN
• Offline selection of ads– Local advertisement selection (LAS)• Bulk pre-fetch of ads, AN provides selection rules
– Best effort advertisement selection (BEAS)• Bulk pre-fetch of ads, statistical selection by CAMEO
Advertisement Selection
AD MANAGER
APP #1 APP #2
CAMEO
AN = ADVERTISEMENT NETWORK
CACHE
ACCOUNTING &
VERIFICATIONRULESET
Energy gains by pre-fetching and caching
• Base case measurement procedure– Screen is lit (50% brightness on Samsung S3)– No other app/services running except OS default– WiFi of SMU campus, 3G on SingTel Singapore– Pre-fetching performed on cheaper network when phone is
charging.– ads fetched once every 45 seconds by custom app– Monsoon monitoring device measures device power
consumption• Gains in LAS and BEAS for 1000 ad views for mostly offline apps
– 99% savings in energy of radio useAnd nearly 92% savings in bandwidth
Challenge # 3: Bartering ads for connectivity
• Example Scenario: A man walks into a airport where they charge $10 for connectivity. Would it be possible for him to get access in exchange for seeing advertisements from the airport’s network?
• Implications & Assumptions– Foreground apps– Negotiations are transparent to the user
Can we trade?
CAMEOISP
2. NEGOTIATE
APP
OS
3. AD FETCH
1. BARTER?
4. AD(S)
5. BITS USED
CAMEO architecture
CONTEXT PREDICTOR
AD MANAGER
ISP NEGOTIATOR
ACCOUNTING AND VERIFICATION
APP #1 APP #2
AN# 1 LIBRARY
AN #2 LIBRARY
CAMEO
Limitations of current CAMEO implementation
• The user study is not representative• Long term context prediction may never be 100%
accurate• A small amount of space in memory will be
occupied by the cache (approx. 2 MB for 1000 ad views)
• Accounting and verification need to be robust.
These issues are currently under investigation.
Summary• #Challenge 1: Reduce overheads– Pre-fetching and caching enable significant
reduction in bandwidth and energy consumption • #Challenge 2: Offline access of ads– online and offline modes of ad selection to
preserve and enhance current economic models• #Challenge 3: Framework for trading– Initial framework proposed and implemented
*Thanks to Matt Welsh, the PC reviewers and my colleagues at SMU*
Questions?
Mobile Advertising Stakeholders
Bandwidth Quota
Energy consumption
Signaling overhead
AN ≡ advertising network
EMPIRICAL STUDY - Advertisements
Caching could be very effective Large amounts of redundant information
Small percentage of ads dominate views
EMPIRICAL STUDY - Users
Users are mostly on expensive networks Users are price conscious
Design Goals
• Lower cost of advertisement delivery• Minimize user involvement• Incentivize developers to make applications
consumer friendly• Minimal modifications to applications and
mobile advertising networks.
CONTEXT PREDICTION
Algorithm to analyze and predict context Context prediction accuracy
CAN WE TRADE?CAMEOISP
NEGOTIATOR
NegotiateAccepted
Request Ad
Thanks for all the fish
Context Specific Ad
Bye
Accounting
APPRegister (1 ad, 10KB,
TCP port 2894)
Display Ad
Success
Ad ready
Disconnect
ISPG/W
ANDROIDOS
How many bytes?Data transmission
10 KB,
TCP 2894
IP A.B.C.D
Accounting
Close
2984 Completed