time-dependent pricing of mobile data
DESCRIPTION
Time-Dependent Pricing of Mobile Data. Soumya Sen Princeton University Joint work with : Sangtae Ha, Carlee -Joe Wong, Mung Chiang . May 4, 2012Bell Labs, Crawford Hill. I. Motivation. Wireless Internet Usage Trends. Mobile data growing at 78% annually. Driving Forces. Mobile - PowerPoint PPT PresentationTRANSCRIPT
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May 4, 2012 Bell Labs, Crawford Hill
Time-Dependent Pricing of Mobile Data
Soumya SenPrinceton University
Joint work with:Sangtae Ha, Carlee-Joe Wong, Mung Chiang
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I. Motivation
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Wireless Internet Usage Trends
Mobile data growing at 78% annually
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Driving Forces
Mobile Video
CloudSync
Data-hungryApps
High-resDevices
A PerfectStorm
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Ultra-Heavy Tail
ISP cost structure’s fundamental problem
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But Not Heavy All the Time
Large Peak-Valley Differential
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Time Elasticity: Opportunities
Time Elasticity
Volu
me
Streamingvideos,Gaming
Texting,Weather, Finance
Email,Social
Network updates
Cloud
SoftwareDownloads
Movies & Multimedia downloads,
P2P
Opportunities
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Cost Reduction
ISP’s Spectrum, Capital, Operational costs decrease with reduced peak
Time Time
Before After
Ban
dwid
th
Peak
Peak
Ban
dwid
th
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Revenue Increase
Time Time
Before After
Ban
dwid
th
$50 for 5 GB $60 for 10 GB
Create win-win by increasing demand
Ban
dwid
th
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II. Feasibility Study
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Consumer ResponseUSA: Online Survey, 130 participants, 25 states
India: Face-to-face Surveys: 550 participants, 5 cities Professionals (36%), Students (36%), Self-employed (8%),
housewives (6%), unemployed (12%)
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Time Elasticity: Survey Results
YouTube streaming Downloads
Many applications are time-elastic
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Policy Feasibility
FCC Dec. 2010 Statement“...the importance of business
innovation to promote network investment and efficient use of networks, including measures to match price to cost, such as
usage-based pricing”
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Industry Moves: US ISPs
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Industry Moves: Indian ISPs
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Industry Moves: African ISPs
Africa dynamic pricing
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Current Practices✤ Flat Rate, throttling heavy-users
✤ Usage-based Pricing
✤ David Clark, ’95: “The fundamental problem with simple usage fees is that they impose usage costs on users regardless of whether the network is congested or not.”
✤ Dynamic Pricing
✤ MacKie-Mason, ’95: “We argue that a feedback signal in the form of a variable price for network service is a workable tool to aid network operators in controlling Internet traffic. We suggest that these prices should vary dynamically based on the current utilization of network resources.”
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History of Pricing Research
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Other Markets: Electricity
“Day Ahead” Pricing
* Sen, et al., “A Survey of Broadband Data Pricing: Past Proposals, Current Plans, and Future Trends”, 2012.
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III. Challenges
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Key Questions
✤ Optimized Price Computation?
✤ Correct Incentives? TUBE Theory
✤ Practical Economic Modeling
✤ System Design Issues
✤ How to assess TDP benefits?
✤ Will real customers respond?TUBE Trial
TUBE System
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IV. TUBE Technology
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Time Dependent Pricing (TDP)
Large scale ISP cost optimization, taking user reaction into account
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ISP’s Optimization ProblemCost of
overshooting capacity
Cost of rewards
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Estimating Waiting FunctionEconomic modeling
reward
patience indexdelay
waiting function
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Patience Index: Initialization
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TDP: Shifting Peak to Valley
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TDP Performance
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V. TUBE Princeton Trial(May 2011-January 2012)
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Princeton Trial: Money Flow
• 50 AT&T participants : 27 iPhones, 23 iPads• Faculty, staff, and students• 14 Academic units
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TUBE App: Information Screens
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TUBE App: Scheduling Screens
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VI. Princeton Trial Results
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Usage Statistics✤ How much bandwidth participants use? – ‘Heavy tailed’
✤ Which applications use the most bandwidth? – Video streaming
July-September, 2011
20%75%
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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 (iPads: -10% in high-price, 15% in low-price periods)
October 2011
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Notification Effectiveness✤ Do notifications impact usage?
✤ 80-90% of users decrease or did not change their usage after the 1st notification
✤ For all subsequent notifications, about 60-80% of the active users decrease their usage, while the others remained price-insensitive.
iPads iPhones
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Psychological Factors✤ Do users respond more to the numerical values of TDP
prices or to the color of the price indicator bar on the home screen?
Period Type 1 and 3 Period Type 1 and 2
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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 reduce the peak-to-average ratio (max reduction: 30%)
✤ Overall usage increase with TDP (demand gain in valley periods)
Peak-to-Average Ratio Peak Usage Volume
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Impact on Web Ecosystem✤ Does the application usage distribution change due to
TDP?
✤ People are motivated to use more bandwidth during low-price periods, “valley filling”.
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VII. Post-Trial Survey
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Viability✤ Will you be able to decide on “when” to use?
✤ “I think it's a great idea, ..the iPads would say, 'If you wait a half an hour, you can have...' I thought that was incredibly useful. And I would be able to make that decision.”
✤ Are there apps for which you usually wait?
✤ “[I]f I'm out in my car and I needed it for GPS, I wouldn't care how much money I'm spending… if I just wanted to be on a social network or check my email, I would certainly wait.”
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Usefulness✤ What are your main concerns with TDP?
✤ “If it's predictable, yes, I think so, because let's say I know that definitely everyday from 9 to 10 it's less, then I can plan a little bit.”
✤ Was the color-coded notification bar useful to you?
✤ “I group the colors I would see if it's a good color for me... because I couldn't always figure out what it meant in terms of the dollar amount and translate that into how much I was using”
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Opinions✤ Were you tempted to use more data when the discounts
were higher?
✤ “[laughs] Kind of! But that also goes toward my personality of if it's on sale I must buy it!”
✤ Will TDP adversely affect high-bandwidth app developers?
✤ “I don't think this will result in those kinds of applications being developed less, and I think that's because you're giving users the option”
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Related Publications:
[1] “TUBE: Time-Dependent Pricing of Mobile Data”, SIGCOMM 2012.
[2] “A Survey of Broadband Data Pricing: Past Proposals, Current Plans, and Future Trends”, under submission in ACM Computing Surveys.
Thank you
http://scenic.princeton.edu/tube/
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Princeton Workshop on
Smart Broadband Pricingsoumyas@
princeton.edu