CAMEO: A Middleware for Mobile Advertisement Delivery

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