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E-raamat: Smart Data Pricing [Wiley Online]

(University of Minnesota), (Professor, Princeton University), (Professor, University of Colorado-Boulder), (Princeton University)
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A comprehensive text addressing the high demand for network, cloud, and content services through cutting-edge research on data pricing and business strategies

Smart Data Pricing tackles the timely issue of surging demand for network, cloud, and content services and corresponding innovations in pricing these services to benefit consumers, operators, and content providers. The pricing of data traffic and other services is central to the core challenges of network monetization, growth sustainability, and bridging the digital divide. In this book, experts from both academia and industry discuss all aspects of smart data pricing research and development, including economic analyses, system development, user behavior evaluation, and business strategies.

Smart Data Pricing:

Presents the analysis of leading researchers from industry and academia surrounding the pricing of network services and content.

Discusses current trends in mobile and wired data usage and their economic implications for content providers, network operators, end users, government regulators, and other players in the Internet ecosystem.

Includes new concepts and background technical knowledge that will help researchers and managers effectively monetize their networks and improve user quality-of-experience.

Provides cutting-edge research on business strategies and initiatives through a diverse collection of perspectives.

Combines academic and industry expertise from multiple disciplines and business organizations.

The ideas and background of the technologies and economic principles discussed within these chapters are of real value to practitioners, researchers, and managers in identifying trends and deploying new pricing and network management technologies, and will help support managers in identifying new business directions and innovating solutions to challenging business problems.
Foreword xv
Preface xvi
Contributors xx
I SMART DATA PRICING IN TODAY'S ECOSYSTEM
1(66)
1 Will Smart Pricing Finally Take Off?
3(32)
Andrew Odlyzko
1.1 Introduction
3(4)
1.2 Telecom Mistakes
7(3)
1.3 Voice and Other Missed Opportunities in Telecom
10(2)
1.4 The Telecom Industry and Innovation
12(1)
1.5 The Large Telecommunications Revenues
12(1)
1.6 The High Potential for Profits in Telecommunications
13(1)
1.7 Telco (R)evolutions
14(2)
1.8 Capital Intensity
16(2)
1.9 Mysteries of Investment, Costs, Profits, and Prices
18(2)
1.10 A Historical Vignette: Bridger Mitchell and Flat Rates
20(4)
1.11 Another Historical Vignette: Flat Rates for Data
24(1)
1.12 Directions for Smart Pricing Research and Deployment
25(1)
1.13 Growth in Demand
26(1)
1.14 Technology Trends
27(1)
1.15 Conclusions
28(7)
Acknowledgments
29(1)
References
29(6)
2 Customer Price Sensitivity to Broadband Service Speed: What Are the Implications for Public Policy
35(12)
Victor Glass
Stela Stefanova
Ron Dibelka
2.1 Introduction
35(3)
2.2 Model
38(1)
2.3 Data
39(1)
2.4 Variable Descriptions
39(2)
2.5 Results
41(3)
2.6 Conclusions
44(3)
References
45(2)
3 Network Neutrality with Content Caching and Its Effect on Access Pricing
47(20)
Fatih Kocak
George Kesidis
Serge Fdida
3.1 Introduction
47(2)
3.2 Background
49(2)
3.3 Two Different Eyeball ISPs
51(1)
3.4 Three Different Congestion Points Per ISP, Fixed Caching Factors
52(3)
3.5 One Congestion Point Per ISP, Fixed Caching Factors
55(1)
3.6 Three Different Congestion Points Per ISP, Fixed Caching Factors, Multiple Providers of One of the Types
56(1)
3.7 Numerical Experiments
57(5)
3.8 Future Work
62(5)
References
64(3)
II TECHNOLOGIES FOR SMART DATA PRICING
67(100)
4 Pricing under Demand Flexibility and Predictability
69(28)
Ozgur Dalkilic
John Tadrous
Atilla Eryilmaz
Hesham El-Gamal
4.1 Introduction
69(2)
4.2 Pricing Under Demand Flexibilities
71(9)
4.2.1 The Day-Ahead Electricity Market with Flexible Consumers
72(5)
4.2.2 Optimal Time-Dependent Pricing under Convexity Assumptions
77(1)
4.2.3 Optimal Bundle Pricing under Discreteness Assumptions
78(1)
4.2.4 Numerical Examples and Insights
79(1)
4.3 Pricing Under Predictable Demand
80(17)
4.3.1 Pricing for Demand Shaping and Proactive Download in Data Networks
83(3)
4.3.2 Cost Minimization via Proactive Data Service and Demand Shaping
86(3)
4.3.3 Pricing Policies Attaining Modified Profiles
89(3)
References
92(5)
5 Dual Pricing Algorithms by Wireless Network Duality for Utility Maximization
97(30)
Chee Wei Tan
Liang Zheng
5.1 Introduction
97(2)
5.2 Utility Maximization
99(4)
5.3 The Wireless Network Duality
103(16)
5.3.1 Wireless Network Duality and Algorithms
105(1)
5.3.2 Smooth and Nonsmooth Utility
105(1)
5.3.3 Nonsmooth Special Case: U(γ) = min l=1,...,L γl/βl
106(2)
5.3.4 Wireless Network Duality
108(4)
5.3.5 Interference Load Minimization
112(1)
5.3.6 Utility Maximization Algorithm
113(3)
5.3.7 A Software Implementation
116(1)
5.3.8 Connection between Dual Algorithm and Pricing Function in Game Theory
117(2)
5.4 Numerical Examples
119(3)
5.5 Conclusion
122(5)
References
123(4)
6 Human Factors in Smart Data Pricing
127(40)
Soumya Sen
Carlee Joe-Wong
Sangtae Ha
Mung Chiang
6.1 Introduction
127(1)
6.2 Methodology
128(7)
6.2.1 Designing Systems with Users in Mind
128(4)
6.2.2 Expert Evaluations
132(1)
6.2.3 Conducting a Field Trial
133(2)
6.2.4 Choosing an Evaluation Method
135(1)
6.3 HCI Lessons from the Energy Market
135(1)
6.4 User Psychology in Home Networks
136(4)
6.4.1 Network Management and QoS Control
136(2)
6.4.2 Implications of Throttling
138(1)
6.4.3 Response to Capping
139(1)
6.5 User Psychology in Bandwidth Pricing
140(3)
6.5.1 Effects of Variable Pricing
140(1)
6.5.2 Effects of Speed-Tier Pricing
141(1)
6.5.3 Effects of Dynamic Time-Dependent Pricing
142(1)
6.6 Day-Ahead Dynamic TDP
143(1)
6.7 Perspectives of Internet Ecosystem Stakeholders
144(4)
6.7.1 Operator Perspectives
144(1)
6.7.2 Consumer Viewpoints
145(1)
6.7.3 Content Provider Considerations
146(1)
6.7.4 Application Developer Concerns
147(1)
6.7.5 Policy Evolution
147(1)
6.8 Lessons from Day-Ahead Dynamic TDP Field Trials
148(14)
6.8.1 Trial Objectives
148(1)
6.8.2 Trial Structure
148(4)
6.8.3 Application User Interface
152(3)
6.8.4 Trial Results
155(7)
6.9 Discussions and Conclusions
162(5)
Acknowledgments
164(1)
References
164(3)
III USAGE-BASED PRICING
167(100)
7 Quantifying the Costs of Customers for Usage-Based Pricing
169(26)
Laszlo Gyarmati
Rade Stanojevic
Michael Sirivianos
Nikolaos Laoutaris
7.1 Introduction
169(1)
7.2 The Cost of a Customer in a Network
170(2)
7.2.1 Datasets Used in the Case Studies
171(1)
7.3 Discrepancy, the Metric of Comparing Different Cost-Sharing Policies
172(1)
7.4 How Do We Compute the Costs of the Customers?
173(7)
7.4.1 Case Study: F-Discrepancy in Backbone Networks
175(5)
7.5 Where Do We Meter the Traffic?
180(3)
7.5.1 Case Study: M-Discrepancy in Backbone Networks
181(2)
7.6 What Is the Impact of the Diverse Costs of the Devices?
183(2)
7.6.1 Case Study: TCO Discrepancy in Backbone Networks
184(1)
7.7 Who is Liable for the Incurred Costs?
185(5)
7.7.1 Case Study: L-Discrepancy in Backbone Networks
188(2)
7.8 Related Work
190(1)
7.9 Conclusions
191(4)
References
191(4)
8 Usage-Based Pricing Differentiation for Communication Networks: Incomplete Information and Limited Pricing Choices
195(46)
Shuqin Li
Jianwei Huang
8.1 Introduction
195(3)
8.1.1 Related Work
197(1)
8.2 System Model
198(2)
8.3 Complete Price Differentiation Under Complete Information
200(5)
8.3.1 User's Surplus Maximization Problem in Stage 2
200(1)
8.3.2 Service Provider's Pricing and Admission Control Problem in Stage 1
200(4)
8.3.3 Properties
204(1)
8.4 Single Pricing Scheme
205(4)
8.4.1 Problem Formulation and Solution
205(1)
8.4.2 Properties
206(3)
8.5 Partial Price Differentiation Under Complete Information
209(8)
8.5.1 Three-Level Decomposition
210(2)
8.5.2 Solving Level 2 and Level 3
212(2)
8.5.3 Solving Level 1
214(3)
8.6 Price Differentiation Under Incomplete Information
217(3)
8.6.1 Extensions to Partial Price Differentiation under Incomplete Information
220(1)
8.7 Connections with the Classical Price Differentiation Taxonomy
220(1)
8.8 Numerical Results
221(6)
8.8.1 When is Price Differentiation Most Beneficial?
221(5)
8.8.2 What is the Best Trade-Off of Partial Price Differentiation?
226(1)
8.9 Conclusion
227(14)
Appendix 8.A
228(1)
8.A.1 Complete Price Differentiation Under Complete Information with General Utility Functions
228(3)
8.A.2 Proof of Proposition 8.1
231(1)
8.A.3 Proof of Lemma 8.2
232(1)
8.A.4 Proof of Theorem 8.4
233(5)
8.A.5 Proof of Theorem 8.6
238(1)
References
238(3)
9 Telecommunication Pricing: Smart Versus Dumb Pipes
241(26)
Atanu Lahiri
9.1 Introduction
241(2)
9.2 Uniform Ordering
243(12)
9.2.1 Dumb Pipe
244(3)
9.2.2 Smart Pipe
247(2)
9.2.3 Smart Pipe Versus Dumb Pipe
249(6)
9.3 Nonuniform Ordering
255(9)
9.3.1 Smart Pipe Versus Dumb Pipe Revisited
255(9)
9.4 Conclusion
264(3)
References
266(1)
IV CONTENT-BASED PRICING
267(96)
10 Economic Models of Sponsored Content in Wireless Networks with Uncertain Demand
269(20)
Matthew Andrews
Ulas Ozen
Martin I. Reiman
Qiong Wang
10.1 Introduction
269(7)
10.1.1 Research Questions
270(1)
10.1.2 Previous Work
271(1)
10.1.3 Designing Contracts Under Uncertain Demand
272(1)
10.1.4 The Models
273(3)
10.2 Analyzing Sponsored Content When EUs Pay Per Byte
276(6)
10.2.1 Content Provider's Problem
276(1)
10.2.2 Service Provider's Problem
277(2)
10.2.3 A Pareto Analysis of the Two-Parameter Contract
279(1)
10.2.4 Summary of the Analysis with a Contract Price C and Additional Revenue from End Users
280(1)
10.2.5 Numerical Example
281(1)
10.3 Analyzing Sponsored Content in the Case of EU Quotas
282(5)
10.3.1 Case 1: Sponsorship-Insensitive Transition Probabilities
284(1)
10.3.2 Case 2: Sponsorship-Sensitive Transition Probabilities
285(2)
10.4 Summary
287(2)
References
287(2)
11 CDN Pricing and Investment Strategies under Competition
289(32)
Yang Song
Lixin Gao
Arun Venkataramani
11.1 Introduction
289(2)
11.2 Related Works
291(3)
11.2.1 The Pricing of a Monopoly CDN
291(1)
11.2.2 CDNs in Content Delivery Supply Chain
292(1)
11.2.3 Compare CDN and Other Multiple-Choice Markets
293(1)
11.3 Background
294(6)
11.3.1 Static Analysis
294(1)
11.3.2 Predictive Analysis
295(1)
11.3.3 Dynamic Analysis
296(4)
11.3.4 Summary
300(1)
11.4 Content Producers' CDN Selection Problem
300(2)
11.4.1 Precise-Coverage Model
300(1)
11.4.2 Approximate-Coverage Model
301(1)
11.5 CDN Pricing Game Under Competition
302(6)
11.5.1 Two-CDN Pricing Games
302(5)
11.5.2 The n-CDN Pricing Games
307(1)
11.6 CDN Competition Under Market Structure Change
308(9)
11.6.1 Assumptions
309(1)
11.6.2 Market State Change Through CDN Federation
309(2)
11.6.3 The Dynamic CDN Game
311(6)
11.7 Conclusion
317(4)
Acknowledgments
318(1)
References
318(3)
12 Smart Pricing and Market Formation in Hybrid Networks
321(20)
Aris M. Ouksel
Doug Lundquist
Sid Bhattacharyya
12.1 Spectrum Shortage
321(2)
12.2 Peer-To-Peer Networking
323(2)
12.3 Commercial Viability
325(3)
12.4 Self-Balancing Supply/Demand
328(2)
12.5 Hybrid Network Model Overview
330(2)
12.5.1 Organization
330(1)
12.5.2 Algorithms
331(1)
12.5.3 Hardware
331(1)
12.5.4 Distributed Accounting
331(1)
12.5.5 Network Security
332(1)
12.6 Incentive Modeling
332(1)
12.7 Flow Model
333(3)
12.8 Prioritization Model
336(2)
12.8.1 Divisible Incentives
337(1)
12.8.2 Indivisible Incentives
338(1)
12.9 Conclusion
338(3)
References
339(2)
13 To Tax or To Subsidize: The Economics of User-Generated Content Platforms
341(22)
Shaolei Ren
Mihaela van der Schaar
13.1 Introduction
341(2)
13.2 Model
343(3)
13.2.1 Intermediary
344(1)
13.2.2 Content Producers
345(1)
13.2.3 Content Viewers
346(1)
13.3 Profit Maximization on User-Generated Content Platforms
346(10)
13.3.1 Definition of Equilibrium
346(1)
13.3.2 Optimal Content Viewing
347(3)
13.3.3 Equilibrium Content Production
350(2)
13.3.4 Optimal Payment Rate
352(4)
13.3.5 Overjustification Effects
356(1)
13.4 Extension to Heterogeneous Production Costs
356(5)
13.5 Conclusion
361(2)
References
361(2)
V MANAGING CONTENT DELIVERY
363(90)
14 Spare Capacity Monetization by Opportunistic Content Scheduling
365(26)
Bell Labs
Alcatel-Lucent
14.1 Summary
365(2)
14.2 Background
367(1)
14.3 The Plutus Approach
368(7)
14.3.1 Pricing Model
371(4)
14.4 Architecture and Design
375(8)
14.4.1 Components
376(6)
14.4.2 Client-Side Monitoring of Available Capacity
382(1)
14.5 Performance Evaluation
383(4)
14.5.1 Network Utilization
383(1)
14.5.2 Delay
384(2)
14.5.3 User Experience
386(1)
14.6 Conclusions and Future Work
387(4)
Acknowledgments
387(1)
References
388(3)
15 Asynchronous Content Delivery and Pricing in Cellular Data Networks
391(24)
Vijay Gabale
Umamaheswari Devi
Ravi Kokku
Shivkumar Kalyanraman
15.1 Introduction
391(2)
15.1.1 Surging Mobile Data Traffic and Declining Operator Profits
391(1)
15.1.2 Traffic Variations and Peak-Time Congestion
392(1)
15.1.3 Yield Management through Smart Pricing
392(1)
15.2 User Survey
393(5)
15.2.1 Setup and Goals
393(1)
15.2.2 State of User QoE
394(1)
15.2.3 Delay Tolerance
394(1)
15.2.4 Delay Elasticity by Traffic Type
395(1)
15.2.5 Price Sensitivity
396(1)
15.2.6 Adoption
396(1)
15.2.7 Pricing Interface
397(1)
15.3 Time-Shifting Traffic
398(4)
15.3.1 Time-Shifting Taxonomy
398(2)
15.3.2 Comparison of the Time-Shifting Alternatives
400(2)
15.4 Pricing to Enable Delivery-Shifting
402(4)
15.4.1 Computing (Price, EDT) Options
402(2)
15.4.2 Integration with an Mno's Infrastructure
404(2)
15.5 Simulation Results
406(5)
15.5.1 Performance Measures
406(1)
15.5.2 Simulation Setup
406(2)
15.5.3 Results
408(3)
15.6 Conclusion
411(4)
References
412(3)
16 Mechanisms for Quota Aware Video Adaptation
415(26)
Jiasi Chen
Amitabha Ghosh
Mung Chiang
16.1 Introduction
415(2)
16.1.1 Two Conflicting Trends
415(1)
16.1.2 Current Approaches in Practice
416(1)
16.2 Related Work
417(1)
16.2.1 Video Adaptation
417(1)
16.2.2 Video Streaming Protocols
417(1)
16.2.3 Quota Aware Video Adaptation
418(1)
16.3 A Potential Solution: QAVA
418(3)
16.3.1 Trading off Quality Versus Cost Versus Volume
418(1)
16.3.2 Incentives for Players in QAVA Ecosystem
419(1)
16.3.3 Design Considerations
420(1)
16.4 QAVA System Design
421(4)
16.4.1 A Modular Architecture Design
421(2)
16.4.2 Module Placement
423(1)
16.4.3 QAVA Operational Example
424(1)
16.5 Stream Selection
425(5)
16.5.1 Video Request, Utility, and Cost Model
425(2)
16.5.2 Stream Selection as Knapsack Problems
427(2)
16.5.3 Solving Finite-Horizon Markov Decision Process
429(1)
16.6 User and Video Profilers
430(3)
16.6.1 Profiling User Behavior
430(2)
16.6.2 Profiling Video Cost and Utility
432(1)
16.7 Performance Evaluation
433(5)
16.7.1 Experimental Setup
433(1)
16.7.2 Comparing Stream Selection Algorithms
434(1)
16.7.3 Single-User Examples
434(1)
16.7.4 Multiuser Stream Selection
434(3)
16.7.5 Sensitivity to Prediction Error
437(1)
16.8 Conclusions
438(3)
References
438(3)
17 The Role of Multicast in Congestion Alleviation
Alan D. Young
17.1 Congestion in Cellular Networks
441(1)
17.2 Video, The Application
442(2)
17.3 Why is Unicast not Ideal for All Video?
444(1)
17.4 Why is Multicast Better for Video in Some Circumstances?
445(2)
17.5 Broadcast, Multicast, and Unicast Architectures for the Delivery of Video
447(2)
17.6 Future Potential Architectures Mixing Broadcast, Multicast and Unicast
449(1)
17.7 Conclusions
450(3)
Reference
451(2)
VI PRICING IN THE CLOUD
453(48)
18 Smart Pricing of Cloud Resources
455(22)
Yu Xiang
Tian Lan
18.1 Data Center VM Instance Pricing
457(4)
18.1.1 Dynamic Scheduling and Server Consolidation for Fixed Pricing Scheme
457(1)
18.1.2 Price Estimation for the Uniform Pricing Scheme
458(3)
18.2 Data Center SLA-Based Pricing
461(5)
18.3 Data Center Time-Dependent Pricing
466(8)
18.3.1 Electricity Cost
467(1)
18.3.2 Workload Constraints
468(6)
18.4 Conclusion and Future Work
474(3)
References
474(3)
19 Allocating and Pricing Data Center Resources with Power-Aware Combinatorial Auctions
477(24)
Benjamin Lubin
David C. Parkes
19.1 Introduction
477(3)
19.1.1 Related Work
478(2)
19.2 A Market Model of Data Center Allocation
480(9)
19.2.1 Buyer Valuation Model
482(4)
19.2.2 Defining The Goods in the Market
486(1)
19.2.3 Seller Cost Model
487(2)
19.3 Experimental Results
489(4)
19.3.1 Scalability and Complexity
492(1)
19.4 Going Beyond Processing and Power
493(2)
19.5 Pricing
495(2)
19.6 Conclusions
497(4)
Acknowledgments
498(1)
References
498(3)
Index 501
SOUMYA SEN is an Assistant Professor of Information & Decision Sciences at the Carlson School of Management of the University of Minnesota. He is a co-founder of the Smart Data Pricing Forum and a startup called DataMi. His interdisciplinary research on the economics of network systems has been published by the IEEE, ACM, and AIS. He received his MS and PhD in Electrical & Systems Engineering from the University of Pennsylvania and did his postdoctoral research at Princeton University.

CARLEE JOE-WONG is a PhD candidate at Princeton University's Program in Applied and Computational Mathematics. She co-founded DataMi, a start-up company commercializing smart data pricing, in 2012. She received her AB in Mathematics in 2011 and her MA in Applied and Computational Mathematics in 2013, both from Princeton University.

SANGTAE HA is an Assistant Professor in the Department of Computer Science at the University of Colorado at Boulder with a joint appointment in the Interdisciplinary Telecommunications Program. He co-founded DataMi in 2012 as the founding CTO. Prior to this, he was an Associate Research Scholar in the Department of Electrical Engineering at Princeton University from 2009 to 2013. He received his PhD in Computer Science from North Carolina State University in 2009.

MUNG CHIANG is the Arthur LeGrand Doty Professor of Electrical Engineering at Princeton University, the Director of the Keller Center for Innovations in Engineering Education, and the Chair of Princeton Entrepreneurship Advisory Committee. He is the recipient of the 2013 Alan T. Waterman Award.
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