1. On Creating an Affordable Internet With Smart Data Pricing (SDP)
Soumya Sen
Information & Decision Sciences
Carlson School of Management
University of Minnesota, USA
Acknowledgements: M. Chiang, S. Ha, C. Joe-Wong

2. I. Why do we need SDP? Congestion Alleviation & Network Monetization

3. Evolution of Access Pricing The Driving Forces
S. Sen, GAIA Workshop, San Jose, Dec 4

4. Challenges to Internet’s Growth
Is it 1
year
feasible ?
to keep the
Internet
economically
viable 10 $
/GB ?
&
technologically
sustainable
100 % ?
S. Sen, GAIA Workshop, San Jose, Dec 4

5. Challenges to the Internet
Is it
Technological
factors 1 10
100
year
/GB $ %
feasible
to keep the
Internet
Human
factors
economically
viable
&
Economic
factors
technologically
sustainable ?
S. Sen, GAIA Workshop, San Jose, Dec 4

6. Challenges to the Internet
SDP as a Solution
Challenges to the Internet
feasible
economically
viable
Is it
technologically
sustainable
to keep the
Internet
& ?
Network
Engineering
Systems
Technological
factors
HCI,
Consumer
Behavior
User
Trials
Human
factors
SDP
Economics
Theory
Domain
Knowledge
Methodology
Economic
factors
S. Sen, GAIA Workshop, San Jose, Dec 4

7. II. What is SDP?

8. S. Sen, GAIA Workshop, San Jose, Dec 4
Some Pricing Examples
Time/location/congestion-dependent pricing
App based pricing/zero-rating
Sponsored content/Reverse billing
Quota/Price-aware content distribution/caching
All of the above…
S. Sen, GAIA Workshop, San Jose, Dec 4

9. Some Common Principles in SDP Research
Pricing for QoE and not just linear byte-counting
Apps have different quality requirements
Match prices to cost of delivery (e.g., Priority pricing)
Application control over physical resource allocation
User’s willingness-to-pay to govern physical medium access
Automated scheduling of traffic depending on availability (e.g., Smart Market, Auction-based pricing)
End-user devices as congestion management tools
Empowering user choices and control (e.g., HomeNets)
Smart device GUIs extend HCI opportunities
S. Sen, GAIA Workshop, San Jose, Dec 4

10. III. Example of SDP: Dynamic Day-ahead Time Dependent Pricing

11. Time Elasticity Large Peak-Valley Differential Opportunities for
Streaming
videos,
Gaming
Texting,
Weather, Finance ,
Social Network updates
Cloud,
M2M
Software
Downloads
Movies & Multimedia downloads,
P2P
Opportunities
Opportunities for
Exploiting time-elasticity
of demand
S. Sen, GAIA Workshop, San Jose, Dec 4

12. From Peak to Valley Periods
Peak-to-average ratio: 2.2
Peak-to-average ratio: 1.53
Dropbox Sync
Web browsing
iTunes download
28% decrease in PAR
Traffic
Time
S. Sen, GAIA Workshop, San Jose, Dec 4

13. Time Dependent Pricing (TDP)
Adaptive economic profiling of
users’ reactions to prices
Large scale ISP cost optimization,
taking user reaction into account
Charge users based on “when” and not simply “how much”
S. Sen, GAIA Workshop, San Jose, Dec 4

14. Theory: ISP’s Optimization Problem
Cost of overshooting capacity
Cost of rewards
Convex Optimization Problem
Refer to papers in ACM SIGCOMM 2012 & SIGCHI 2013 for details
S. Sen, GAIA Workshop, San Jose, Dec 4

15. System: TDP Architecture
S. Sen, GAIA Workshop, San Jose, Dec 4

16. User Behavior: Field Trial
TDP for 3G data
Baseline: $10/GB
Feasibility study
Prototype development
iOS (iPad, iPhone)
Trial
AT&T subscribers
50 participants
MTA Alaska Trial
Android users
Money Flow
Data Flow
S. Sen, GAIA Workshop, San Jose, Dec 4

17. Graphical User Interfaces (GUIs)
Price display
Day-ahead
Color coded: red (<10%), orange (10 ~19%),
yellow (20 ~ 29%) and green (>= 30%)
Self-education
Temporal Usage, Top 5 Apps
User control
Autopilot mode
S. Sen, GAIA Workshop, San Jose, Dec 4

18. Results: Price Sensitivity
Do users wait to use mobile data in return for a monetary discount?
Average usage decrease in high-price periods relative to the changes in low-price periods
No change
Usages changed by -10.1% in high-price and 15.7% in low-price periods
E.g. Post trial debriefing responses: Are there apps for which you usually wait?
“If I just wanted to be on a social network or check my , I would certainly wait.”
S. Sen, GAIA Workshop, San Jose, Dec 4

19. S. Sen, GAIA Workshop, San Jose, Dec 4
Optimized TDP Impact
Does the peak usage decrease with time-dependent pricing? And does this decrease come at the expense of an overall decrease in usage?
Optimized TDP reduces the peak-to-average ratio (by 30%)
Overall usage increases in off-peak times ( “Sales Day effect”)
30% PAR reduction
S. Sen, GAIA Workshop, San Jose, Dec 4

20. DataWiz Data Management App for Android and iOS (free):
Usage monitoring, alerts, history, prediction
S. Sen, GAIA Workshop, San Jose, Dec 4

21. Subsidized content Reverse billing (1-800 for content)
Trove (DataMi), NURM (UMN)
sponsors
User requests
Micropayments for Ads and data subsidies
How much sponsorship is
needed for 90s vs 30s ad?
needed for popular ads?
Matching & Auction design
Ads and data subsidies
Matching/Auction platform
S. Sen, GAIA Workshop, San Jose, Dec 4

22. III. So How Can SDP Help?

23. S. Sen, GAIA Workshop, San Jose, Dec 4
Usefulness of SDP
Lower Capex/Opex of network operators
Reduce Network Congestion
Lower Access Cost, Subsidized plans
Create Ultra Affordable Plans (UAP)
An opportunistic access class of service
S. Sen, GAIA Workshop, San Jose, Dec 4

24. Directions to Explore for the GAIA WG
Optimized WiFi Offloading (e.g., AMUSE*)
Traded Data Plans (e.g., in HK)
Sponsored Data (e.g., DataMi)
Work to deploy pricing ideas in the real-world
Help match operators/markets to pricing models
Create interdisciplinary teams to work on solutions
Design protocols to support pricing innovations
Share knowledge of ongoing research and results
*Adaptive bandwidth Management System for User Empowerment, IEEE INFOCOM 2013
S. Sen, GAIA Workshop, San Jose, Dec 4

25. S. Sen, GAIA Workshop, San Jose, Dec 4
From $10/GB to SDP
SDP Workshops:
SDP 2012, Princeton, USA
SDP 2013, Turin, Italy (with IEEE INFOCOM)
SDP 2014, Toronto, Canada (with IEEE INFOCOM)
SDP 2015, Hong Kong (with IEEE INFOCOM) – April 27th, 2015
Paper deadline: December 7, 2014
References:
S. Ha, S. Sen, C. Joe-Wong, Y. Im, M. Chiang, “TUBE: Time Dependent Pricing for Mobile Data”, ACM SIGCOMM 2012.
Y. Im, C. Joe-Wong, S. Ha, S. Sen, T. Kwon, M. Chiang, “AMUSE: Empowering Users for Cost-Aware Offloading with Throughput-Delay Tradeoffs”, IEEE INFOCOM 2013 (mini-conf).
S. Sen, C. Joe-Wong, S. Ha, M. Chiang, J. Bawa, “When the Price is Right: Enabling Time-Dependent Pricing of Broadband Data,” ACM SIGCHI 2013.
S. Sen, C. Joe-Wong, S. Ha, M. Chiang, “A Survey of Broadband Data Pricing: Past Proposals, Current Plans, and Future Trends”, ACM Computing Surveys 2014.
S. Sen, GAIA Workshop, San Jose, Dec 4

26. Resistance to Pricing (1)
Access to Internet should be “free” for all
Proponents: Taxpayers paid for its development
Reality: By 1994, NSF supported only 10% of Internet costs
Marginal cost for operators is zero
Proponents: Statistical multiplexing reduces delivery cost
Reality: Congestion and operational costs are significant
S. Sen, GAIA Workshop, San Jose, Dec 4

27. Resistance to Pricing (1)
All traffic should be treated equally
Proponents: Else can threaten net-neutrality
Reality: Empower user choices
Variance of QoE requirements of apps (bandwidth, delay)
Variance in user’s willingness to pay
Variance in operator’s cost of delivery (peak/off-peak)
Network costs are high due to billing
Proponents: Billing, not congestion dominates costs
Reality: 4-6% when depreciation for sunk costs are added
S. Sen, GAIA Workshop, San Jose, Dec 4

28. Resistance to Pricing (2)
New technologies will solve the problem
Proponents: 4G and offloading solves it
Reality: demand > supply
Spectrum crunch (275 MHz deficit by 2014, FCC)
Carriers have not bought enough backhaul to support it
Changes to pricing will increase user’s costs
Proponents: Data plans to become more expensive
Reality: Incentive mechanisms can lower bills, increase consumer choices
S. Sen, GAIA Workshop, San Jose, Dec 4

29. Resistance to Pricing (3)
SDP is too complex
Proponents: Billing systems are an untouchable legacy
Reality:
Pricing regimes are evolving around the world
Interest of operators, regulators, and consumers
Other networks (transportation, electricity) have adopted
Shared Data Plans, Sponsored content necessitates rethinking of design, architecture and protocols
Key is to understand user psychology, HCI designs
Need for greater interdisciplinary initiatives
S. Sen, GAIA Workshop, San Jose, Dec 4