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

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

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

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

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

II. What is SDP?

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

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

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

Time Elasticity Large Peak-Valley Differential Opportunities for Streaming videos, Gaming Texting, Weather, Finance Email, 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

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

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

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

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

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

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

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 email, I would certainly wait.” S. Sen, GAIA Workshop, San Jose, Dec 4

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

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

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

III. So How Can SDP Help?

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

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

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

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

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

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

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