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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Information and Communication Technology
- Sprache: Englisch
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.
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Table of Contents
Cover
Series
Title Page
Copyright
Foreword
Preface
Acknowledgments
Contributors
I: Smart Data Pricing in Today's Ecosystem
Chapter 1: Will Smart Pricing Finally Take Off?
1.1 Introduction
1.2 Telecom Mistakes
1.3 Voice and Other Missed Opportunities in Telecom
1.4 The Telecom Industry and Innovation
1.5 The Large Telecommunications Revenues
1.6 The High Potential for Profits in Telecommunications
1.7 Telco (R)evolutions
1.8 Capital Intensity
1.9 Mysteries of Investment, Costs, Profits, and Prices
1.10 A Historical Vignette: Bridger Mitchell and Flat Rates
1.11 Another Historical Vignette: Flat Rates for Data
1.12 Directions for Smart Pricing Research and Deployment
1.13 Growth in Demand
1.14 Technology Trends
1.15 Conclusions
Acknowledgments
References
Chapter 2: Customer Price Sensitivity to Broadband Service Speed: What are the Implications for Public Policy?
2.1 Introduction
2.2 Model
2.3 Data
2.4 Variable Descriptions
2.5 Results
2.6 Conclusions
References
Chapter 3: Network Neutrality with Content Caching and Its Effect on Access Pricing*
3.1 Introduction
3.2 Background
3.3 Two Different Eyeball ISPs
3.4 Three Different Congestion Points Per ISP, Fixed Caching Factors
3.5 One Congestion Point Per ISP, Fixed Caching Factors
3.6 Three Different Congestion Points Per ISP, Fixed Caching Factors, Multiple Providers of One of the Types
3.7 Numerical Experiments
3.8 Future Work
References
II: Technologies for Smart Data Pricing
Chapter 4: Pricing under Demand Flexibility and Predictability
4.1 Introduction
4.2 Pricing Under Demand Flexibilities
4.3 Pricing Under Predictable Demand
References
Chapter 5: Dual Pricing Algorithms by Wireless Network Duality for Utility Maximization
5.1 Introduction
5.2 Utility Maximization
5.3 The Wireless Network Duality
5.4 Numerical Examples
5.5 Conclusion
References
Chapter 6: Human Factors in Smart Data Pricing
6.1 Introduction
6.2 Methodology
6.3 Hci Lessons From the Energy Market
6.4 User Psychology in Home Networks
6.5 User Psychology in Bandwidth Pricing
6.6 Day-Ahead Dynamic TDP
6.7 Perspectives of Internet Ecosystem Stakeholders
6.8 Lessons From Day-Ahead Dynamic TDP Field Trials
6.9 Discussions And Conclusions
Acknowledgments
References
III: Usage-Based Pricing
Chapter 7: Quantifying the Costs of Customers for Usage-Based Pricing
7.1 Introduction
7.2 The Cost of a Customer in a Network
7.3 Discrepancy, The Metric of Comparing Different Cost-Sharing Policies
7.4 How Do We Compute the Costs of the Customers?
7.5 Where Do We Meter the Traffic?
7.6 What is the Impact of the Diverse Costs of the Devices?
7.7 Who is Liable for the Incurred Costs?
7.8 Related Work
7.9 Conclusions
References
Chapter 8: Usage-Based Pricing Differentiation for Communication Networks: Incomplete Information and Limited Pricing Choices*
8.1 Introduction
8.2 System Model
8.3 Complete Price Differentiation Under Complete Information
8.4 Single Pricing Scheme
8.5 Partial Price Differentiation Under Complete Information
8.6 Price Differentiation Under Incomplete Information
8.7 Connections With the Classical Price Differentiation Taxonomy
8.8 Numerical Results
8.9 Conclusion
Appendix 8.A
8.A.1 Complete Price Differentiation Under Complete Information With General Utility Functions
8.A.2 Proof of Proposition 8.1
8.A.3 Proof of Lemma 8.2
8.A.4 Proof of Theorem 8.4
8.A.5 Proof of Theorem 8.6
References
Chapter 9: Telecommunication Pricing: Smart Versus Dumb Pipes*
9.1 Introduction
9.2 Uniform Ordering
9.3 Nonuniform Ordering
9.4 Conclusion
References
IV: Content-Based Pricing
Chapter 10: Economic Models of Sponsored Content in Wireless Networks with Uncertain Demand
10.1 Introduction
10.2 Analyzing Sponsored Content When EUs Pay Per Byte
10.3 Analyzing Sponsored Content in the Case of EU Quotas
10.4 Summary
References
Chapter 11: CDN Pricing and Investment Strategies under Competition
11.1 Introduction
11.2 Related Works
11.3 Background
11.4 Content Producers’ CDN Selection Problem
11.5 CDN Pricing Game Under Competition
11.6 CDN Competition Under Market Structure Change
11.7 Conclusion
Acknowledgments
References
Chapter 12: Smart Pricing and Market Formation in Hybrid Networks
12.1 Spectrum Shortage
12.2 Peer-To-Peer Networking
12.3 Commercial Viability
12.4 Self-Balancing Supply/Demand
12.5 Hybrid Network Model Overview
12.6 Incentive Modeling
12.7 Flow Model
12.8 Prioritization Model
12.9 Conclusion
References
Chapter 13: To Tax or To Subsidize: The Economics of User-Generated Content Platforms
13.1 Introduction
13.2 Model
13.3 Profit Maximization on User-Generated Content Platforms
13.4 Extension to Heterogeneous Production Costs
13.5 Conclusion
References
V: Managing Content Delivery
Chapter 14: Spare Capacity Monetization by Opportunistic Content Scheduling
14.1 Summary
14.2 Background
14.3 The Plutus Approach
14.4 Architecture and Design
14.5 Performance Evaluation
14.6 Conclusions and Future Work
Acknowledgments
References
Chapter 15: Asynchronous Content Delivery and Pricing in Cellular Data Networks
15.1 Introduction
15.2 User Survey
15.3 Time-Shifting Traffic
15.4 Pricing to Enable Delivery-Shifting
15.5 Simulation Results
15.6 Conclusion
References
Chapter 16: Mechanisms for Quota Aware Video Adaptation
16.1 Introduction
16.2 Related Work
16.3 A Potential Solution: QAVA
16.4 QAVA System Design
16.5 Stream Selection
16.6 User and Video Profilers
16.7 Performance Evaluation
16.8 Conclusions
References
Chapter 17: The Role of Multicast in Congestion Alleviation
17.1 Congestion in Cellular Networks
17.2 Video, The Application
17.3 Why is Unicast not Ideal for all Video?
17.4 Why is Multicast Better for Video in Some Circumstances?
17.5 Broadcast, Multicast, and Unicast Architectures for the Delivery of Video
17.6 Future Potential Architectures Mixing Broadcast, Multicast and Unicast
17.7 Conclusions
References
VI: Pricing in the Cloud
Chapter 18: Smart Pricing of Cloud Resources
18.1 Data Center VM Instance Pricing
18.2 Data Center SLA-Based Pricing
18.3 Data Center TIME-DEPENDENT Pricing
18.4 Conclusion and Future Work
References
Chapter 19: Allocating and Pricing Data Center Resources with Power-Aware Combinatorial Auctions
19.1 Introduction
19.2 A Market Model of Data Center Allocation
19.3 Experimental Results
19.4 Going Beyond Processing and Power
19.5 Pricing
19.6 Conclusions
Acknowledgments
References
Index
Series
End User License Agreement
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Guide
Cover
Table of Contents
Preface
I: Smart Data Pricing in Today's Ecosystem
Chapter 1: Will Smart Pricing Finally Take Off?
List of Illustrations
Figure 1
Figure 3.1
Figure 3.2
Figure 3.3
Figure 3.4
Figure 3.5
Figure 3.7
Figure 3.8
Figure 3.9
Figure 3.10
Figure 3.11
Figure 3.12
Figure 3.13
Figure 4.1
Figure 4.2
Figure 4.3
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Figure 4.5
Figure 4.6
Figure 4.7
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Figure 4.9
Figure 4.10
Figure 4.11
Figure 4.12
Figure 4.13
Figure 5.1
Figure 5.2
Figure 5.3
Figure 5.4
Figure 5.6
Figure 5.5
Figure 5.7
Figure 5.8
Figure 5.9
Figure 6.1
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Figure 6.3
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Figure 8.14
Figure 9.1
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Figure 9.6
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Figure 10.1
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Figure 11.1
Figure 11.2
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Figure 11.6
Figure 13.1
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Figure 15.1
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Figure 15.13
Figure 16.1
Figure 16.2
Figure 16.3
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Figure 17.1
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Figure 18.1
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Figure 19.1
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Figure 19.10
List of Tables
Table 1.1
Table 1.2
Table 1.3
Table 2.1
Table 2.2
Table 2.3
Table 2.4
Table 4.1
Table 5.1
Table 6.1
Table 6.2
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Table 7.6
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Table 7.8
Table 8.1
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Table 8.3
Table 9.1
Table 9.2
Table 11.1
Table 11.2
Table 11.3
Table 11.4
Table 11.5
Table 11.6
Table 15.1
Table 15.2
Table 16.1
Table 16.2
Table 16.3
Table 18.2
Smart Data Pricing
Applications and Challenges
Edited by
Soumya Sen, Carlee Joe-Wong, Sangtae Ha, and Mung Chiang
Copyright © 2014 by John Wiley & Sons, Inc. All rights reserved
Published by John Wiley & Sons, Inc., Hoboken, New Jersey
Published simultaneously in Canada
No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permission.
Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.
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Library of Congress Cataloging-in-Publication Data:
Smart data pricing / edited by Soumya Sen, Carlee Joe-Wong, Sangtae Ha, and Mung Chiang
pages cm
Includes index.
ISBN 978-1-118-61166-1 (hardback)
1. Telecommunication–Pricing. 2. Resource allocation. I. Sen, Soumya, 1982-
HE7631.S57 2014
384′.043–dc23
2013051204
Foreword
Smart phones, tablets, and other video and music streaming devices fuel an exploding demand for network, cloud, and content services. Providers find it difficult to increase revenue to match the investments required to address this demand. The wireless networks are getting stressed and the quality of service suffers. The experience of other industries suggests that smarter pricing mechanisms might improve the matching of resources and users and the revenue of providers, thereby increasing user welfare both in the short term and long term. Researchers are exploring this possibility and a number of recent workshops on this topic attest to the perceived urgency of developing effective approaches.
This collection of papers presents the analysis of the pricing of network services and content conducted by leading researchers from industry and academia. The topics include the following: the tension between the users’ preference for simple tariffs and potential benefits of more complex schemes; the users’ sensitivity to quality of service and their willingness to shift demand; economic incentives for efficient caching and infrastructure improvements; and pricing schemes for content and for cloud resources.
Researchers will welcome this timely and broad coverage of Smart Data Pricing (SDP).
JEAN WALRAND
University of California, Berkeley, CA
Preface
As the demand for data in both wired and wireless broadband networks continues to grow every year, Internet Service Providers (ISPs) are increasingly turning to pricing both as a congestion management tool and as a revenue generation model. This evolution in the pricing regime is evidenced by the elimination of flat-rate plans in favor of $10/GB or higher usage based overage fees in the United States and various other countries in Asia and Europe. This rapid transition from unlimited data plans to a reign of penalty-based mechanisms, including throttling, capping, and usage-based fees, all within a span of just 4 years as witnessed in the United States is shown in Figure 1. Consequently, Smart Data Pricing (SDP) will play a major role in the future of mobile, broadband, and content. SDP refers to a departure from the traditional flat-rate or byte-counting models to considering pricing as a network management solution. Thus, SDP will impact not only end users and network operators, but will also engage content providers, policy makers, mobile advertisers, vendors, and device suppliers. SDP incorporates the following principles:
Figure 1 Timeline of the evolution in pricing plans in the United States.
SDP can refer to (a) time/location/app/congestion dependent dynamic pricing, (b) usage-based pricing with throttling/booster, (c) WiFi offloading/proactive caching, (d) two-sided pricing/reverse billing/sponsored content, (e) quota-aware content distribution, (f) shared data pricing, and any combination or extension of the above. For instance, two-sided pricing can include QoE enhancements, or it may simply refer to content providers partially subsidizing data. SDP can benefit end users, network operators, and content providers by improving users’ Quality of Experience; lowering ISP congestion and CapEx/OpEx, thus increasing their revenue/profit margin and decreasing churn, and encouraging more consumption and ad revenue for content/app providers. But to realize these benefits, SDP requires pricing models that capture the interplay between technical and economic factors, as well as interfaces between network providers and content & application providers; effective user interface designs; field trials; and a combination of smart ideas, systematic execution, and informed policy.
This volume of collected essays on SDP has immensely benefitted from the annual SDP Forum, which organizes workshops to bring together industry experts, academics, and regulators for in-depth discussions on the topic. SDP 2012 was held in Princeton, New Jersey, and the SDP 2013 and 2014 Workshops were was held in conjunction with IEEE INFOCOM in Turin, Italy and Toronto, Canada. The workshops have been attended by professionals from AT&T, Verizon, Comcast, NECA, Alcatel-Lucent, Cisco Systems, Qualcomm, Microsoft, ACS, and many other leading networking companies. It therefore comes with little surprise that several of the chapters in this volume have been contributed by industry researchers and showcase some cutting-edge research in this area.
The first three chapters of this book discuss SDP's feasibility in the current Internet ecosystem. The first chapter looks back on previous efforts to promote SDP and asks whether the current market climate will be more receptive. The next chapter approaches SDP's feasibility from a customer perspective, using empirical data to examine their price sensitivity. Finally, the third chapter incorporates regulatory concerns by examining network neutrality in the context of content caching.
The next three chapters address SDP's technical feasibility. The first chapter in this section develops a pricing model that accounts for the flexibility and predictability of customer demand. The second chapter focuses on wireless networks, showing how pricing can be used to make wireless resource allocation more efficient. The last chapter focuses on SDP's interface between ISPs and users, examining how the ISP can communicate prices to users through interfaces on their devices.
The next three chapters of the book shift to variants on usage-based pricing, a particular form of SDP. The first chapter examines whether usage-based pricing can in fact help ISPs by quantifying the distribution of infrastructure costs among ISP customers. The next two chapters then turn to differentiated pricing: the first of these develops a model for differentiated usage-based pricing, while the secondexamines the benefits of non-differentiated and differentiated pricing for ISPs and end users.
Another form of SDP, content-based pricing, is discussed in the next four chapters. The first chapter discusses a variant of usage-based or capped pricing, in which content providers subsidize the delivery of their content to end users, sponsoring users’ Internet access. The second chapter shifts the focus to content delivery networks and the impact of competition on their pricing and investment, while the third chapter discusses the economics of a hybrid model in which content delivery can be offloaded to a secondary P2P network during congested times. The last chapter considers the economics of content providers, focusing on how the owners of user-generated content platforms, e.g., social networking websites, can best monetize this content.
The next four chapters discuss technical aspects of realizing economically efficient models of content delivery. The first chapter investigates the idea of opportunistic content transfer, offloading traffic to times of lower congestion with a monetary discount given during times of lower congestion. The next chapter considers a similar idea, in which sessions like content transfers can be spread over time, but with prices determined by the deadline of each session's completion. The third chapter focuses on video content, and shifts the focus away from ISPs to consider how a user might distribute a budget for consuming videos over time. Finally, the last chapter considers multicast technology and how it can alleviate network congestion.
The last two chapters of the book consider pricing in the cloud. The first chapter investigates and compares three different schemes for pricing data center resources, namely real-time instance pricing, deadline-based service level agreements, and time-dependent pricing. The last chapter proposes using combinatorial auctions to price and allocate resources in a data center while taking into account its electricity constraints.
The diversity of topics explored in these book chapters reflects SDP's broad potential impact. Indeed, SDP brings together ideas from such diverse fields as network engineering, economics, human-computer interaction, data science, and technology policy to answer fundamental questions about broadband pricing. Yet there remain significant emerging themes which this book does not cover. For instance, little rigorous analysis has been done on shared data plans, which have recently become mainstream in the U.S. Perhaps more significantly, “network neutrality” is emerging as a fundamental issue, with new regulations from the FCC and Netflix's agreement with Comcast to pay for a separate “fast lane” for its streaming traffic. And as more and more devices become connected to the Internet, pricing for the Internet of Things is becoming an important question. The emergence of these and other topics will ensure that SDP remains an exciting and relevant research topic in the years to come.
SOUMYA SEN, CARLEE JOE-WONG, SANGTAE HA, AND MUNG CHIANG
Acknowledgments
We would like to thank all of the participants of the first, second, and third Smart Data Pricing Workshops, held respectively in Princeton, New Jersey on July 30 and 31, 2012; Turin, Italy on April 19, 2013; and Toronto, Canada on May 2, 2014. We are also grateful to all of the contributing authors for their time and effort, as well as our colleagues who served as reviewers for the contributions.
1http://www.m3i.org/
Contributors
M
ATTHEW
A
NDREWS
, Bell Labs, Alcatel-Lucent, Murray Hill, NJ
R
ANDEEP
B
HATIA
, Bell Labs, Alcatel-Lucent, Murray Hill, NJ
S
ID
B
HATTACHARYYA
, Department of Information and Decision Sciences, University of Illinois at Chicago, Chicago, IL
J
IASI
C
HEN
, Princeton University, Princeton, NJ
M
UNG
C
HIANG
, Princeton University, Princeton, NJ
O
ZGUR
D
ALKILIC
, The Ohio State University, Columbus, OH
U
MAMAHESWARI
D
EVI
, IBM Research, Bangalore, India
R
ON
D
IBELKA
, National Exchange Carrier Association, Inc., Whippany, NJ
H
ESHAM
E
L
-G
AMAL
, The Ohio State University, Columbus, OH
A
TILLA
E
RYILMAZ
, The Ohio State University, Columbus, OH
S
ERGE
F
DIDA
, UPMC, Paris, France
V
IJAY
G
ABALE
, IBM Research, Bangalore, India
L
IXIN
G
AO
, University of Massachusetts, Amherst, MA
A
MITABHA
G
HOSH
, UtopiaCompression Corporation, Los Angeles, CA
V
ICTOR
G
LASS
, National Exchange Carrier Association, Inc. Whippany, NJ
B
HAWNA
G
UPTA
, Bell Labs, Alcatel-Lucent, Murray Hill, NJ
L
ÁSZLÓ
G
YARMATI
, Telefonica Research, Barcelona, Spain
S
ANGTAE
H
A
, University of Colorado, Boulder, CO
J
IANWEI
H
UANG
, The Chinese University of Hong Kong, Hong Kong, China
C
ARLEE
J
OE
-W
ONG
, Princeton University, Princeton, NJ
S
HIVKUMAR
K
ALYANRAMAN
, IBM Research, Bangalore, India
G
EORGE
K
ESIDIS
, The Pennsylvania State University, University Park, State College, PA
F
ATIH
K
OCAK
, The Pennsylvania State University, University Park, State College, PA
R
AVI
K
OKKU
, IBM Research, Bangalore, India
A
TANU
L
AHIRI
, University of Washington, Seattle, WA
T
IAN
L
AN
, George Washington University, Washington, DC
N
IKOLAOS
L
AOUTARIS
, Telefonica Research, Barcelona, Spain
S
HUQIN
L
I
, Alcatel-Lucent Shanghai
B
ENJAMIN
L
UBIN
, Boston University Boston, MA; Harvard University, Cambridge, MA
D
OUG
L
UNDQUIST
, Department of Information and Decision Sciences, University of Illinois at Chicago, Chicago, IL
A
NDREW
O
DLYZKO
, University of Minnesota, Minneapolis, MN
A
RIS
M. O
UKSEL
, Department of Information and Decision Sciences, University of Illinois at Chicago, Chicago, IL
U
LAS
O
ZEN
, Ozyegin University, Istanbul, Turkey
D
AVID
C. P
ARKES
, Harvard University, Cambridge, MA
M
ARTIN
I. R
EIMAN
, Alcatel-Lucent Bell Labs
S
HAOLEI
R
EN
, Florida International University, Miami, FL
M
IHAELA
V
AN
D
ER
S
CHAAR
, University of California, Los Angeles, Los Angeles, CA
S
OUMYA
S
EN
, Carlson School of Management, University of Minnesota, Minneapolis, MN
M
ICHAEL
S
IRIVIANOS
, Cyprus University of Technology, Limassol, Cyprus
Y
ANG
S
ONG
, University of Massachusetts, Amherst, MA
R
ADE
S
TANOJEVIC
, Telefonica Research, Barcelona, Spain
S
TELA
S
TEFANOVA
, National Exchange Carrier Association, Inc., Whippany, NJ
J
OHN
T
ADROUS
, The Ohio State University, Columbus, OH
C
HEE
W
EI
T
AN
, City University of Hong Kong, Hong Kong, China
A
RUN
V
ENKATARAMANI
, University of Massachusetts, Amherst, MA
Q
IONG
W
ANG
, University of Illinois Urbana-Champaign, Champaign, IL
Y
U
X
IANG
, George Washington University, Washington, DC
A
LAN
D. Y
OUNG
, P & Y Associates, LLC
L
IANG
Z
HENG
, City University of Hong Kong, Hong Kong, China
PART I
Smart Data Pricing in Today's Ecosystem
Chapter 1Will Smart Pricing Finally Take Off?
ANDREW ODLYZKO
1.1 Introduction
Will smart pricing dominate telecommunications? We certainly do see growth in sophisticated pricing in many areas of the economy. Congestion charges for cars entering central business districts and “smart” electric meter deployments are spreading. Airlines are even beginning to auction seat upgrades [1]. And there is no shortage of desire for smart pricing in telecommunications. For a survey of recent developments, see Reference 2. Many new technological developments, such as software-defined networking (SDN), are touted as facilitating differentiated services and differentiated pricing. The overwhelming consensus of the industry, as well as of the research community, and of regulators, is that flat rates are irrational. Thus, for example, in 2011, Jon Leibowitz, the then-Chairman of the US Federal Trade Commission could not “quite understand why something like metering hasn't taken off yet.” (See Reference 3 for references to this and similar recent quotes, as well as for a summary of the arguments in favor of flat rates.)
Yet there are reasons for caution in the rush to smart pricing. After all, the modern consensus about its desirability is not new. It goes back centuries, to the days of snail mail. Furthermore, industry has often either stumbled onto flat or almost flat rates, or been forced into them, all against its will, and ended up benefiting. Thus, for example, US wireless service providers have been boasting of the low per-minute voice call revenues that reign in United States, much lower than in most of the world. What they universally neglect to mention is that these low prices are the result of the success of the block-pricing plan introduced by AT&T Wireless in 1998, which also eliminated roaming and long-distance charges. This plan, the result not of a careful study of historical precedents or the economics of communications but rather the fruit of a desperate carrier looking for a way to gain customers, was widely derided but proved unexpectedly popular. It forced the rest of the industry to follow suit with similar plans and led to large increases in voice usage (see, e.g., the chart in Reference 4). The end result is that the United States has the world's highest per-subscriber voice usage, yielding those low average per-minute prices that the industry boasts of. Probably not coincidentally, US wireless service providers are among the world's most profitable. This story, and others similar to it, should make one cautious about rushing to follow the industry consensus. This is true even when such a consensus is fortified by scholarly studies, because those tend to be even more biased towardfine-grained pricing. The telecom industry and telecom researchers have historically been notorious for not understanding what is in the industry's own interests.
The traditional preoccupation with smart pricing is likely to be reinforced by the economics of telecom. Contrary to common opinion, it is not all that capital intensive. As is demonstrated in Section 1.8, telecom is simply not in the same category as such large and important industries as electricity or roads when it comes to the ratio of capital investment to revenues. Telecom is primarily about service, customer inertia, and territorial strategic plays (where the territories may be physical or virtual).
Although the telecom industry is not very capital intensive, communications is extremely valuable and any society is willing to pay astonishing amounts for it. As an example, by some measures, the United States spends almost 50% more on telecom services than it does for electricity. (See Section 1.5 for more data and references.) Furthermore, in spite of all the complaints from the industry about its supposedly impoverished state, there appears to be very large profits in many parts of it. As this passage is being written in the summer of 2013, Verizon is in the process of buying out Vodafone's 45% stake in the Verizon Wireless unit for $130 billion. This means that the whole of Verizon Wireless is being valued at almost $300 billion. As will be shown in Section 1.9, that is about four times the cost of replacing all the tangible assets of that enterprise. It is also almost enough to replace the entire US telecom infrastructure, both wireless and wired, with the latter redone in fiber. This is anomalous by traditional standards, but then, as will be discussed in Section 1.9, the entire economy is behaving anomalously, with very high corporate profits, low interest rates, and low capital investment. Whether this is a temporary aberration, or whether we are in a new economic era, remains to be seen. However, telecom is very much in the mainstream of this historically unusual behavior, and so many traditional yardsticks of financial performance may not apply.
While the telecom industry has often been blind to profitable opportunities, it has always been aware that high profits are possible. However, it has usually faced difficulties in using their favorite methods for profit extraction because of various combinations of legal and regulatory constraints and the peculiar nature of demand for communication services. Table 1.1 shows an approximation of current prices paid by users for varying amounts of data from various services.
Table 1.1 Price per Megabyte
SMS
$1000.00
Cellular voice
1.00
Wireline voice
0.10
Residential Internet
0.01
Backbone Internet
0.0001
This table demonstrates the main problem faced by telecom. The most valuable information can often be conveyed in just a few bits. Thus, for example, in the early days of postal services, when receivers paid on delivery, information would often be transmitted in the form of small modifications in the address. The addressee would then scan the envelope, figure out what the message was, and refuse to accept (and pay for) the letter.
Practices from two centuries ago may seem irrelevant, but in fact they are very instructive, as the basic economic issues have always been the same, even as technology has changed drastically, cf. [5]. Thus, for example, today, we have the telecom industry investing heavily in deep packet inspection. In the past, post offices had employees hold letters up against burning candles to make sure that there were no enclosures that were subject to extra fees. The basic incentive is to extract as much value as possible, and that usually requires fine-grained pricing to achieve successful price discrimination. But usually, in communication as well as in transportation, limits are placed on what service providers are allowed to do. The net neutrality debate is just another instance of the ancient conflict between economic efficiency and fairness in markets [6]. Giving unfettered control of any critical service to any provider, or an oligopoly of providers, either de jure or de facto (by allowing natural monopoly mechanisms to operate), is equivalent to abolishing property rights with the usual negative impacts on innovation and efficiency. Hence, we have almost always had constraints, such as those of common carriage. The real question is about the appropriate level of constraints.
Public talk of capacity limits is often just a public relations measure, designed to overcome opposition to service provider strategies. Thus, for example, in early 2013, Michael Powell, the President of the US cable industry association [and former Chairman of the Federal Communications Commission (FCC)] admitted, contradicting many earlier declarations by a variety of executives and experts, that “cable's interest in usage-based pricing was not principally about network congestion, but instead about pricing fairness” [7]. Whenever business leaders talk of “fairness,” it is generally safe to assume that they are really after extracting more revenues through differential pricing. This is neither a novel nor is it nefarious. In fact, differential pricing was and is at the core of regulatory economics, as it can be used to promote social welfare, and has been frequently mandated by governments. However, historically, the degree of price discrimination that was allowed varied depending on economics, with more discrimination being allowed when the costs of providing those services have been large [8]. The question for the near future is whether modern telecom should be allowed more power to discriminate. Further, even if it is given that power, one should consider whether it would be wise to use it. The right answer depends on the balance between growth in demand and improvements in technology.
The main problem, past, present, and future, that is faced by telecom is that the most valuable information usually requires just a few bits to convey. Thesecond main problem is that because of technological progress, transmission capacity is growing. Thus the industry is faced with the challenge of persuading users to pay for big pipes when the additional value that enlarging those pipes provides is not all that high. (There are arguments that the value of transmission capacity, as well as that of computing power and storage, should be measured on a logarithmic scale, so that going from what is now a slow 1 Mbps link to a 1 Gbps one corresponds only to an increase in value from 6 to 9, cf. [9].) At the moment, that additional capacity is consumed largely by video. But the value is still dominated by the low bandwidth voice and texting.
The general conclusion of this work, based on the study of trends in demand and supply, is that in wireline communication, the critical issue faced by the telecom industry is not handling overpowering exafloods of traffic, as has often been claimed, cf. [10–12]], but stimulating demand to fill the growing capacity of transmission systems [13]. The most effective way to do that is to offer flat rates and open access to encourage innovation. To the extent that any market segmentation is needed, it is best handled by offering flat rate services with different peak speeds. Pricing by volume of traffic (whether using caps or other schemes) may be attractive at the moment to service providers preoccupied with trying to protect their traditional subscription video service revenues. However, it is an ineffective instrument that does not address any of the issues well and, in the long run, is likely to damage not only the economy as a whole but also the profits of service providers. Any truly “smart pricing” measures, such as congestion charges, are likely to be detrimental to the industry.
These general conclusions for wired communications apply directly mainly to the richer and more industrialized countries. Even in those, there is likely to be exceptional situations where the cost structure forces some “smart pricing” approaches. For poor countries, the best choices along the frontier of feasible technological and business models is likely to lean further toward “smart pricing.” This would be consistent with the general observation, cf. [5], that at the consumer level, sophisticated pricing is most appropriate for large and relatively infrequent transactions, and simple pricing for small and frequent ones. This is also what we observe in the market today, with the greatest proliferation of “smart pricing” in less-developed countries, where the relative burden of telecommunications charges is higher.
In wireless communication, the optimal choice even in rich countries appears to be different than that for wireline, because of a different balance between feasible supply and potential demand. There have been widespread projections that wireless data traffic would continue to double each year, as it had done for several years. Those are now being disproved, as growth rates are declining (see Section 1.13). Still, those rates are high, and there is far more traffic that are likely to use the radio path if that were feasible, as wireless data traffic is under 5% of wireline. Coupled with the low value of most of this data, and the resulting low likelihood of service providers being able to extract large new revenues, it appears probable that the incentives for theindustry will be to constrain usage and to implement differentiated quality of service to protect the most valuable low bandwidth applications. So somewhat finer-grained pricing is likely to prevail in this domain than in wireline. Still, the need to limit what Nick Szabo [14] has aptly called the mental transaction costs involved in fine-grained pricing, and related concerns, is likely to restrict the complexity of schemes that succeed. The sophisticated pricing plans so beloved of researchers are likely to be confined to areas such as business-to-business dealings and may be of limited applicability even there.
However, the strong prejudice in favor of “smart pricing” among both industry leaders and academic researchers guarantees that many schemes will be developed, and quite a few will be deployed. Chances are that, as was true of many sophisticated prioritization schemes developed for voice private branch exchanges (PBXs) or early data switches, they will not see much use. But for those cases where they might be used, it appears that most of current research, as well as academic instruction, is missing some important ingredients. As is discussed in Section 1.12, it will likely be important to explore the most effective ways to introduce noise and other impairments into communication systems to provide differential quality of service. (On the other hand, there will likely also be demand for methods to detect such actions.)
The next section reviews briefly some of the main fallacies that invigorate the push for “smart pricing.” This is followed by a section on some missed opportunities in telecommunications, demonstrating how this industry tends to “stumble to success,” pursuing mistaken goals, and prospering by accident. Section 1.4 has a very brief discussion of the reasons telecom has been so poor at innovating in services and is likely to remain poor in the future. Section 1.5 discusses this industry's place in the entire economy. Section 1.6 points out that high profits have not infrequently been obtained in this sector. Section 1.7 sketches the main changes that have taken place in the money flows in telecommunications in recent decades. Section 1.8 demonstrates that, contrary to general opinion, this industry is not all that capital intensive. Section 1.9 discusses some of the puzzles of the modern economy and the degree to which the telecom industry exhibits similar behavior. Section 1.12, cited earlier, discusses some missing ingredients in modern research and education, should “smart pricing” become widespread. Sections 1.10 and 1.11 take a historical look at some earlier work on telecom pricing and the degree to which it reflected the prejudices we observe today. Sections 1.13 and 1.14 then discuss the growth in the demand for data traffic and improvements in transmission technologies and what the contrasts are with those that for optimal pricing strategies. Finally, Section 1.15 provides the conclusions.
1.2 Telecom Mistakes
Many of the basic but general issues that have a bearing on the possible adoption of smart pricing have already been explored in the literature (see, e.g., [5, 15–17]) and so will be touched on very lightly here. However, they do need to be mentioned, because there are many misapprehensions about the nature of telecom and these issues often have an important bearing on the optimal choices of pricing policies. For example, we are constantly told that content is king. (Content is taken here to mean material prepared by professionals for wide distribution and not, as some use it, to denote anything in digital form.) But
Content is not king.
Yes, content, in the sense of material prepared by professionals for wide distribution, is important. But it is simply nowhere near as important as basic connectivity, and the revenues of various services reflect that. This is discussed in detail in References 5, 18. Evidence of this fundamental fact is all around, and some of this will show up later in this paper (e.g., in the observation that US wireless carriers have revenues about three times as large as those that the cable industry derives from subscription video). However, content has historically attracted disproportionate attention and continues to do so today. For example, an article in the Economist [19] stated
A common saying in the industry is that Mexico's phone sector may be about four times more valuable than the television market, but the latter is four times as powerful.
What is especially perplexing about the centuries-old preoccupation with content is that content is not cheap. For telecom service providers to sell content, they generally have to buy it at high prices. (And so, net of what they pay to content producers, US cable networks appear to be getting more revenue out of Internet access and voice services than out of carrying subscription video and all on a far smaller slice of their transport capacity.) Back in 2005, Ed Whitacre, then the CEO of AT&T, caused a flare-up in the net neutrality debate with his threat that he would not let Google use his wires without payment. Strangely enough, it is not clear if anybody raised the question as to whether his basic premise was correct, that is, in the absence of any legal or regulatory constraint, it would be Google paying AT&T. Why should not AT&T have to pay Google? Perhaps Whitacre was right, and Bing might have been an acceptable substitute for Google search for AT&T customers. But perhaps not. Imagine that Whitacre had said he was not going to let ESPN or HBO use AT&T's U-Verse wireswithout payment. Instead of being called evil by small groups of advocates of an open Internet, he surely would have been called insane by almost everybody.
Because content is not king, the vast majority of papers and discussions about net neutrality, industry structure, and related issues are of doubtful relevance. For example, many academic papers start with the assumption that the Internet is a two-sided market. It simply is not. Most of the values that users get from it is not content but simple connectivity, such as being able to tell their friends and business partners they are stuck in traffic. Compared to old communication technologies, the Internet does provide many unique features and, in particular, allows for bridging content and connectivity. (The main search service of Google, which provides the bulk of that company's revenues and profits but very little traffic, is in this intermediate zone, as are most of the facilities of social networks that users care about.) However, the features that matter the most are not the ones that allow content providers to target individual consumers but the ones that allow for group formation and for individuals or groups to become creators and distributors.
Closely allied to the myth that content is king is another extremely widespread and extremely damaging notion, that of streaming video, [20]. However, all the evidence suggests that
True streaming video is, and will remain, a very small fraction of traffic.
Video does dominate current Internet traffic by volume, but it is almost exclusively transmitted as faster-than-real-time progressive downloads. That is the only method that makes sense technologically. (Video conferencing is completely different, but we now have enough experience to be able to predict safely that it will not be contributing giant amounts of traffic.) Furthermore, this was easily predictable and was predicted a long time ago. For example, George Gilder wrote about it two decades ago, and he attributes the idea to Nicholas Negroponte even earlier. Although their prediction has come true, almost everyone thinks that the floods of video they consume are true streaming video. This skews business decisions and public policy discussions, because networks dominated by real-time long-lived data flows of predictable size and with tight latency constraints do indeed lend themselves to many of the pricing and network management techniques that are so beloved by both top managers and telecom researchers, cf. [21].
The myth of real-time streaming video is so pervasive and strong that it also affects networking researchers. For the past decade, this author has been taking polls asking those in the audience to raise their hands if they saw any advantage at all, for anyone, in transmitting video faster than real time. Usually, even among networking researchers, at most, 10% have responded. The highest positiveresponse rates were around 40%, in a couple of groups of audiences packed with researchers working on wireless ad-hoc networks, and who understand that one cannot count on connectivity being maintained, but can use buffers to compensate. (While one can envisage ultra-reliable wired networks, in the wireless arena, this is simply not achievable; there are far too many unpredictable sources of impairments.) This demonstrates that even networking researchers do not know what is happening in today's networks or why it is happening.
The preoccupation with real-time streaming video leads to the constant questioning about the potential demand for high speed access. Who needs gigabit in the home, is the question that is being asked, because the most that most observers can imagine is a few streams that might possibly come to 20 Mbps each in some future high definition (HD) television (TV). This perfectly illustrates the lack of vision not just for the future but on the present that afflicts this industry. After all, why are people buying 300 Mbps home WiFi access points if all they are after is streaming a few movies? Yet such routers are selling, and high speed home access is also selling (when offered at reasonable cost), because they allow for low transaction latency.
The main function of data networks is to cater to human impatience.
This is something that the computer industry, as well as many other competitive industries, whether online search or Internet commerce, understand well. If users do not get their web search results in a second, they go away. On the other hand, the telecom industry has a hard time assimilating this notion. Yet, if you want to download a 8 GB video to your portable device in less than a minute, you absolutely have to have a gigabit link. Hence,
Overprovisioning is not a bug but a feature, as it is indispensable to provide low transaction latency, which is the main function of data networks.
Once you have overengineered your network, it becomes clearer that pricing by volume is not particularly appropriate, as it is the size and availability of the connection that creates most of the value. That is also what the users perceive directly. Generally speaking (and there are obviously exceptions, buffer bloat can lead to contrary experience), increased bandwidth means that things happen faster, the network is more responsive, etc. This is something immediately perceptible to users. It does not require them to engage in any mental transaction costs to figure out where they are with respect to violating some volume caps, for example.
In wireline, the vision of a largely empty network dominated (initially in value, and eventually likely also in volume) by cascades of mostly machine-to-machine transactions driven by human impatience that was easy to predict a long time ago, cf. [21], does appear to be realistic and likely inevitable. As George Gilder has said, “You waste that which is plentiful” and in most wired networks, bandwidth is plentiful. Wireless, though, appears to be different, as will be discussed later.
1.3 Voice and Other Missed Opportunities in Telecom
Correct technological predictions are hard in general, but telecom predictions seem to be worse when compared to other areas. Some of the many mistakes can be excused easily. For example, the popularity of wireless had been consistently underestimated by the industry for several decades. But this was understandable, because the service was novel, and the high value that people had placed on mobility was not easy to predict. (There is a saying that you cannot tell how many people will use a bridge by counting how many swim across a river.) But others are far more surprising and illustrate well how telecom has often “stumbled to success.” As just one example, on an e-mail discussion list as recently as the summer of 2006, one of the top technical officers of a major US cable company insisted that the idea of taking some of the bandwidth away from video services and employing it for Internet access was impractical. He insisted that “[t]he vast majority of folk in this country watch analog tv and don't have electronics to consume them digitally, don't want them or can't afford them.” Yet today, Internet access is already, or is about to become, the main business of the cable networks.
The most perplexing of the many mistakes that telecom has made is in neglect of voice. Even today, voice services provide the bulk of worldwide telecom revenues, but the industry has not been paying attention. When 3G was being prepared for deployment around the turn of the millennium, industry was touting it as an enabler of all sorts of fancy digital “content” services. But it was obvious that voice offered the greatest profit opportunities [22], and voice has indeed been the main revenue generator for 3G. However, while the industry did benefit from this easy-to-anticipate but unanticipated windfall, it has neglected other opportunities in voice [22]. Those opportunities include voice messaging, and, perhaps most important, high quality voice. Current wireless voice quality is poor, far poorer than the “toll quality” voice standard of wired services. (And that “toll quality” is also poor, given what is possible with modern codecs.) From this, and from the rapid expansion of wireless revenues, the industry appears to have concluded that the public does not care about voice quality. It is far more probable that the public accepted low quality wireless voice in order to gain mobility. But this does not mean that quality could not be sold as an added value feature. It might have provided large additional revenues and profits in the 3G world.There capacity was constrained, and therefore, it would have been possible to charge extra for higher quality. As it is, HD voice, which is part of the plan for long-term evolution (LTE), is likely to just become a standard service, as its resource requirements are low compared to the capacity of the new system.
Table 1.2 Voice to Text Substitution (US)
Year
Voice Minutes billions
Texts billions
2005
1495
81
2006
1798
159
2007
2119
363
2008
2203
1005
2009
2275
1563
2010
2241
2052
2011
2296
2304
2012
2300
2190
It is impossible to prove that high quality voice, if deployed and marketed properly, would have been a great success. Soon we may obtain some indication from the public's reaction to HD voice in LTE. But even before that, there were a variety of reasons for believing that voice was promising, including the success of Integrated Digital Enhanced Network (iDEN) with its simple push-to-talk feature. Human culture is primarily an oral one, and we have the astonishing success of the telephone to look back to, which surprised many observers by attracting far more usage and spending than postal services and the telegraph.
Those who denigrate voice can point to data such as that of Table 1.2. It shows steady level of voice traffic on US wireless networks (based on the data from Reference 23), which represents a decline in voice usage on a per-user basis, because the number of subscriptions has been growing during the period covered by this table. It has been surmised that this decline was due to usage migrating from voice to texting. That may very well be true, but it does not necessarily mean voice is unimportant. Texting has major advantages (in particular, being asynchronous, and thus less intrusive than voice), and the phenomenon shown in this table may be an indicator of a substantial opportunity in voice messaging, one that possibly could have generated good revenues in the restricted 3G environment.
Moving forward, the opportunity to gain additional revenues with HD voice appears to be gone, but voice should not be neglected, as it is right now, in a variety of services. Furthermore, it appears that in the development of video services, the industry is neglecting social communication in the traditional preoccupation with content.
1.4 The Telecom Industry and Innovation
The telecom industry has repeatedly shown that it can perform well in increasing transmission capacity.
