On the Adoption and Deployment of New Network Technologies: An Economic Perspective

Soumya Sen

Dept. of Electrical & Systems Engineering

University of Pennsylvaniassoumya@seas.upenn.edu

www.seas.upenn.edu/~ssoumya

Joint Work with:

R. Guerin, K. Hosanagar, Y. Jin

18th November, 2010. Penn Seminar on Communications & Networking

On the Adoption and Deployment of New Network Technologies: An Economic Perspective

• Networked Systems have a ubiquitous presence – e.g., Internet, Power grid, Facilities Management networks, Distributed databases

• Success of new network technologies depends on:– Technical advantage– Economic factors (e.g. price, costs, demand)

• Shortcomings:– Many technologies have failed to get adopted

• e.g., IPv6 migration, QoS solutions– Ad-hoc decisions for deployment

• e.g. Cloud Coimputing, U-Verse versus. FiOS

• How to assess (design) new network technologies (architectures) for technical and economic viability?

– Analytical frameworks– Multi-disciplinary approach

Research Motivation

2

On Developing Analytical Models:

– What are the key economic aspects?– What factors are specific to network technologies? (e.g. externality, gateways)– What are the ‘qualitative’ insights from the model?– Are the results robust to changes in the underlying model?

Some Dimensions for Assessing Network Technologies:

• Topic 1: – Network Technology Adoption/ Migration

• How can a provider help its technology (service) to succeed?

• Topic 2: – Network Infrastructure Choice

• What infrastructure should the new technology (service) be deployed on?• Understanding Trade-offs between Shared and Dedicated networks

• Topic 3: – Trade-offs between Functionality-rich versus Minimalist Designs

Research Approach

3

• Network Technology Adoption

• Dependencies across users (externality)• Incumbent’s advantage of installed base• Gateway’s impact on adoption

– Explored the dynamics of adoption among heterogeneous users– Characterized the trajectories and equilibrium outcomes– Analyzed the role of gateways in migration

• Shared vs. Dedicated Networks

• Many services on a common (shared) network vs.

• Many services over separate (dedicated) networks

– Choice depends on compatibility among services, demand uncertainty

– Identified trade-offs and guideline for network design

Research Contributions

4

Network Technology Adoption

1. Problem Formulation

2. Model & Solution Methodology

3. Key Findings & Examples

4. Conclusions

A Two-part Talk: Outline

5

• Two competing and incompatible network technologies (e.g., IPv4 IPv6)

– Different qualities and price– Different installed base

• Users individually (dis)adopt the technology that gives them the highest positive utility

– Depends on technology’s intrinsic value and price– Depends on number of other users reachable (externality)

• Gateways offer a migration path

– Overcome chicken-and-egg problem of first users• Independently developed by each technology

– Effectiveness depends on gateways (converters) characteristics/ performance• Duplex vs. Simplex (independent in each direction or coupled)• Asymmetric vs. Symmetric (performance/ functionality wise)• Constrained vs. Unconstrained (performance/functionality wise)

Problem Formulation

7

A Basic User Model

8

• Users evaluate relative benefits of each technology

– Intrinsic value of the technology ( q1)• Tech. 2 better than Tech.1 (q2>q1) denotes user valuation (captures heterogeneity), Є [0,1].

– Externalities: linear in no. of users (0≤x1+x2≤1) - Metcalfe’s Law• Possibly different across technologies (captured through β)• αi, 0αi 1, i = 1,2, captures gateway’s performance

– Cost (recurrent) for each technology (pi)

Technology 1: U1(,x1,x2 ) = q1+(x1+α1β x2) – p1

Technology 2: U2(,x1,x2) = q2+(βx2+α2x1) – p2

Low-def. video (Tech.1) High-def video (Tech. 2)

10

Technology 1: U1(,x1,x2 ) = q1+(x1+α1β x2) – p1 Technology 2: U2(,x1,x2) = q2+(βx2+α2x1) – p2

• User sensitivity to technology quality ( ) Private information for each user, but known distribution

• Low-def & High def video-conferencing service

– Low-def has a lower price (p1< p2) but lower quality (q1< q2)

– Video is an asymmetric technology• Encoding is hard, decoding is easy

– Low-def subscribers could display high-def signals but not generate them• Externality benefits of High-def are higher than those of Low-def

• Converter characteristics (β>1)

– High/Low-def user can decode Low/High-def video signal – αi, 0αi 1, i = 1,2, captures gateways’ performance

– Simplex, asymmetric, unconstrained (α1β >1)

User Adoption Process

11

Decision threshold associated with indifference points for each technology choice: 1

0(x), 20(x), 2

1(x) ,where x=(x1, x2)

U1(, x) > 0 if ≥ 10(x) - Tech. 1 becomes attractive

U2(, x) > 0 if ≥ 20(x) - Tech. 2 becomes attractive

U2(, x) > U1(, x) if ≥ 21(x) - Tech. 2 over Tech. 1

Users rationally chooseNone if U1< 0, U2< 0Technology 1 if U1> 0, U1> U2

Technology 2 if U2> 0, U1< U2

Decisions change as x evolves over time (myopic)

x1 x2

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Diffusion Model

12

• Assume a given level of technology penetration x(t)=(x1(t),x2(t)) at time t

– Hi(x(t)) is the number of users for whom it is rational to adopt technology i at time t (users can change their mind)

– At equilibrium, Hi(x*) = xi*, i {1,2}

– Determine Hi(x(t)) from user utility function

• Adoption dynamics:– Users differ in learning and reacting to adoption information – Diffusion process with constant rate γ< 1

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Solution Methodology

13

• Delineate each region in the (x1,x2) plane, where Hi(x) has a different expression

– There are 9 such regions, i.e., R1,…, R9

– Regions can intersect the feasibility region S

0 x1+x21 in a variety of ways

• This is in part what makes the analysis complex

– trajectories cross boundaries

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R2

R3

R4

R5

R6

R7

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02

01

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x1=1

x2=1

0

012

Computing Equilibria & Trajectories

14

Solve Hi(x*) = xi*, i {1,2} in each region

Identify “candidate” equilibrium for each Region Rk

• Candidates are valid only if they lie in their region• Equilibria can be stable or unstable

Trajectories:

λ1 and λ2 can be positive, negative, or even complex

*

kRx

)( * SRx kRk

}2,1{,21 icebeax tti

• What are possible adoption outcomes?– Combinations of equilibria– Stable/ Unstable

• Adoption trajectories?– Monotonic vs. chaotic (cyclic)

• What is the role of gateways?– Do they help and how much?

Key Questions

15

• Theorem 1: There can be multiple stable equilibria (at most two)

• Coexistence of technologies is possible – even in absence of gateways

• Final outcome is hard to predict simply from observing the initial adoption trends

Results (1): A Typical Outcome

16

• Theorem 2: Gateways can help a technology alter market equilibrium from a scenario where it has been eliminated to one where it coexists with the other technology, or even succeeds in nearly eliminating it.

• Gateways need not be useful to entrant always!• No gateways: Tech. 2 wipes out Tech.1 • Perfect gateways: Tech. 1 nearly wipes out Tech. 2

Results (2): Gateways may help Incumbents

17

• Theorem 3: Incumbent can hurt its market penetration by introducing a gateway and/or improving its efficiency if entrant offers higher externality benefits (β>1) and users of incumbent are able to access these benefits (α1β>1)

• Theorem 4: Both technologies can hurt overall market penetration through better gateways. Entrant can have such an effect only when (α1β<1). Conversely, Incumbent demonstrates this behavior only when (α1β>1)

Takeaway: Gateways can be harmful at times. They can lower market share for an individual technology or even both.

Results (3): More Harmful Gateway Behaviors

18

• Theorem 5: Gateways can create “boom-and-bust” cycles in adoption process. This arises only when entrant exhibits higher externality benefits (β>1) than incumbent and the users of the incumbent are unconstrained in their ability to access these benefits (α1β>1)

Corollary: This cannot happen without gateways, i.e., in the absence of gateways, technology adoption always converges

Takeaway: Gateways can create perpetual cycles of adoption/ disadoption

P.S: Behavioral Results were tested for robustness across wide range of modeling changes

Results (4): More Harmful Gateway Behaviors

19

Limit Cycles: An Intuitive Explanation

20

Technology 1 Technology 2

Full-circle!

α1β>1

Technology 1: U1(,x1,x2 ) = q1+(x1+α1β x2) – p1 Technology 2: U2(,x1,x2) = q2+(βx2+α2x1) – p2

• Gateways can be useful to:– Promote coexistence & improve market penetration– Help lessen price sensitivity

• But, Gateways can be harmful too:– Hurt an individual technology– Lower Overall Market– Introduce Market Instabilities

• Analytical model is useful in: – Identifying scenarios for policy intervention– developing long-term strategic vision

• Qualitative results are robust to: – switching costs– variation in utility function– non-uniform distr. of user preferences

Conclusions

21

Network Infrastructure Choice:Shared Versus Dedicated Networks

1. Problem Formulation

2. Model & Solution Methodology

3. Key Findings & Examples

4. Conclusions

Part 2: Outline

22

• Emergence of new services require: – Network provider has to decide between:

• Common (shared) Network Infrastructure• Separate (dedicated) Network Infrastructure

• Examples:– Facilities Management services & IT

• e.g. IT & HVAC systems

– Video and Data services• e.g. Internet & IPTV services

– Cloud Computing• e.g. Private (dedicated) cloud Vs Shared cloud

– Broadband over Power lines

• Lack of Framework to evaluate choices:– Ad-hoc decisions (AT&T U-Verse versus Verizon FiOS)– Manufacturing Systems Literature:

• Plant-product allocation, optimal resource allocation

Motivation

23

• Two network services (technologies) – One existing (mature) service – One new service with demand uncertainty

• Costs show economies or diseconomies of scope

• New service has demand uncertainty– Needs capacity provisioning

• before demand gets realized

– Dynamic resource “reprovisioning”• But some penalty will be incurred (portion of excess demand is lost)

– Technology advances allow Reprovisioning (e.g., using virtualization)

• How critical is reprovisioning ability in choosing network design?– Compare networks based on profits

Problem Formulation

25

Model Formulation

26

• Basic Model: A Two-Service Model

• Service 1 (existing service)• Service 2 (new service with

uncertain demand)

• Three-stage sequential decision process

• Compare Infrastructure choices based on expected profits

Reprovisioning Stage

Capacity Allocation Stage

Infrastructure Choice Stage

Solve backwards

Model Variables

27

Provider’s profit depends on:

Costs:

• Fixed costs

• Variable costs - grows with the number of subscribers (e.g. access equipment, billing)

• Capacity costs - incurred irrespective of how many users join (e.g. provisioning, operational)

Cost Component Service 1 Dedicated

Service 2 Dedicated

Shared

Fixed Costs cd1 cd2 cs

Contribution Margin(grows with each unit of realized demand)

pd1 pd2 ps1, ps2

Variable Costs(incurred irrespective of realized demand)

ad1 ad2 as1, as2

Gross Profit Margin = pi - ai , i={s2, d2}

Return on capacity = pi /ai

Solution (1): Reprovisioning Stage

28

• Service 2 revenue: (i={s2, d2} for Shared and Dedicated respectively)

i. when D2 ≤ Ki: Ri (D2 ≤ Ki) = pi D2 – ai Ki

ii. when D2>Ki:

Reprovisioning Ability:

•A fraction “α” of the excess demand can be accommodated

User contribution

Capacity cost

Ri (D2 > Ki) = (pi – ai )(Ki + α(D2 - Ki))

• A word about reprovisioning ability, α

– Independent of the magnitude of excess demand– Captures feasibility of and latency in securing additional resources– So what do α =0 and α =1 mean?

Solution (2): Capacity Allocation Stage

29

• Expected Revenue, E(Ri|Ki), for a given provisioned level Ki:

• Optimal Provisioning Capacity:

• For demand distribution ~U[0, D2max]:

max2

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)()(

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Solution (3): Infrastructure Choice Stage

30

• Dedicated Networks:

– Service 1 revenue:

– Service 2 revenue under optimal provisioning:

– Total profit:

• Shared Network:

• Infrastructure Choice:

– Common if , else separate

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Profit from Service 2

Profit from Service 1

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Choice of Infrastructure

31

• Impact of system parameters:– Varying cost parameters affect the choice of infrastructure

• Shared to Dedicated (or Dedicated to Shared)• Single threshold for switching n/w choice

– Surprisingly, ad-hoc “reprovisioning” ability also impacts in even more interesting ways!

• Common is preferred over separate when ds

Independent of provisioning decision

Depends on provisioning decision

Diff. in optimal capacity cost

h(α)=

Function of pi, ai, α, i={s2,d2}

2)()( *22

*22 ssdd KaKa

Analyzing the effect of α on h(α)

32

• Proposition 1: Increase in α benefits both shared and dedicated networks.

(i) if increases in α benefits shared (dedicated) n/w more than dedicated (shared)

(ii) if increases in α benefits shared (dedicated) more at low α and dedicated (shared) more at high α

• The value of h'(0) and h'(1) fully characterize the shape of h(α)

)0)1(,0)0((,0)1(,0)0( hhhh

)0)1(,0)0((,0)1(,0)0( hhhh

Gross Profit Margin

Return on Capacity

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Results: Impact of Reprovisioning

33

GPMded (pd2-ad2) is sufficiently lower than GPMshr (ps2-as2)GPMded > GPMshr i.e. (pd2-ad2) >(ps2-as2) and ROCded <ROCshr i.e. (pd2/ad2) <(ps2/as2) GPMded > GPMshr i.e. (pd2-ad2) >(ps2-as2) and ROCded >ROCshr i.e. (pd2/ad2) >(ps2/as2)

• Developed a generic model that captures economies and diseconomies of scope between shared and dedicated networks

• Reprovisioning can affect the outcome in non-intuitive ways– Validates the need for models to incorporate this feature– Yields guidelines on how reprovisioning affects choice of architecture

• Identified key operational metrics to consider– Provided decision guideline

• Whether α has an effect depends on – The sign of the derivative h'(α)– Use the two metrics to identify operational region– The magnitude of γ (how far from zero)– Outcomes: zero, one or two switches in network choice

Conclusions

35

Network Technology Adoption:

• Different User Preference (θ)• Non-uniform distribution

– positively & negatively skewed β-distribution)

• Extended to externality benefits (i.e. θβx instead of θq+βx)

• Alternative Externality Models • Non-linear externalities

– Sublinear: xα, 0 < α < 1– Superlinear: xα, α > 1– Logarithmic: log(x+1)

• Pure externalities (no intrinsic technology values, q≈1)

• Presence of Switching costs, Learning costs

Network Infrastructure Choice:

• Different Demand Distribution • Non-uniform distribution

– positively & negatively skewed β-distribution)

• Economies and diseconomies of scale

• Different reprovisioning abilities for shared and dedicated networks (α1, α2 ≠ α)

Closing Remarks: On Robustness

36

Network Technology Adoption:

(1) S. Sen, Y. Jin, R. Guerin and K. Hosanagar. Modeling the Dynamics of Network Technology Adoption and the Role of Converters. IEEE/ACM Transactions on Networking. 2010

(2) S. Sen, Y. Jin, R. Guerin and K. Hosanagar. Technical Report: Modeling the Dynamics of Network Technology Adoption and the Role of Converters. Technical Report. June, 2009. Available at http://repository.upenn.edu/ese papers/496/.

(3) Y. Jin, S. Sen, R. Guerin, K. Hosanagar and Zhi-Li Zhang. Dynamics of competition between incumbent and emerging network technologies. In Proc. Of ACM NetEcon'08, pp.49-54, Seattle, WA, 2008.

Network Infrastructure Choice:

(4) S. Sen, R. Guerin and K. Hosanagar. Shared Versus Separate Networks - The Impact of Reprovisioning. In Proc. ACM ReArch'09, Rome, Italy, December 2009.

(5) S. Sen, K. Yamauchi, R. Guerin and K. Hosanagar. The Impact of Reprovisioning on the Choice of Shared versus Dedicated Networks, Proc. of Ninth Workshop on E-business, WEB, St. Louis, MO, December 2010.

Bibliography

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Thank You!