modeling the economics of network technology adoption & infrastructure deployment soumya sen 24...

Modeling the Economics of Network Technology Adoption & Infrastructure Deployment

Soumya Sen

24th September, 2010. Princeton University

Research Motivation• 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)

• Many technologies have failed– e.g., IPv6 migration, QoS solutions

• How to assess (design) new network technologies (architectures) for technical and economic viability?– Need for analytical frameworks– Need for a multi-disciplinary approach


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 kind of network architecture should the new technology (service) be deployed on?

• Understanding Trade-offs between Shared and Dedicated networks

• Topic 3:– Network Functionality Richness

• How much functionality should the new network architecture have?


Research Contributions (1)

• Network Technology Adoption• Dependencies across users from network based interactions

(externality)• Incumbent’s have advantage of installed base• Technology gateways impact network externality, and hence

adoption

– Explored the dynamics of adoption as a function of user decisions

– Characterized the convergence trajectories and equilibrium outcomes

– Analyzed the role of gateways in technology migration


Research Contributions (2)

• Shared vs. Dedicated Networks• Many services on a common (shared) network vs.• Many services over separate (dedicated) networks

– Network choice depends on benefits of compatibility among offered services and demand uncertainty of new services

– Identified trade-offs and guidelines for network design


Network Technology Adoption

Topic 1:


Talk Outline:

1. Problem Formulation2. Model & Solution Methodology3. Key Findings & Examples4. Conclusions

Prior Work• Models that do not consider individual user utility:

• Fourt & Woodlock (1960) – constant hazard rate model• Bass (1969) - extension to include “word-of-mouth” effect• Norton & Bass (1987) - successive generation of technology

adoption

• Models that consider user utility function:• Cabral (1990) – only single technology adoption• Farrell & Saloner (1992) - homogeneous users• Choi (1996) - extended F&S to include converters• Joseph et al. (2007) – homogeneous users, doesn’t model

system dynamics


Problem Formulation

• Two competing and incompatible network technologies (e.g., IPv4 IPv6)– Different qualities and price– Different installed base

• Users individually (dis)adopt whichever technology 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)


A Basic User Model

• Users evaluate relative benefits of each technology

– Intrinsic value of the technology• Tech. 2 better than Tech.1 • denotes user valuation (captures heterogeneity)

– Externalities: linear in no. of users - Metcalfe’s Law• Possibly different across technologies (captured through β)• captures gateway’s performance

– Cost (recurrent) for each technology


IPv4 (Tech.1) IPv6 (Tech. 2)

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

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

– Cost (recurrent) of each technology (pi)

– Linear Externalities (Metcalfe’s law)• Normalized to 1 for Tech. 1• Scaled by β for Tech. 2 (possibly different from Tech. 1)

• αi, 0αi 1, i = 1,2, captures gateways’ performance

– Intrinsic technology quality (qi)

• Tech. 2 better than tech. 1 (q2 >q1)

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


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

• Low-def & High def video-conferencing service – Low-def has a lower price , but lower quality– 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– High/Low-def user can decode Low/High-def video signal– Simplex, asymmetric, unconstrained


User Adoption Process

• 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 choose– None if U1< 0, U2< 0– Technology 1 if U1> 0, U1> U2

– Technology 2 if U2> 0, U1< U2

• Decisions change as x evolves over time

12

x1 x2


Diffusion Model

13

• 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

2,1 ,)(

itxtxHdt

tdxii

i


H1( x(t)) H2( x(t))

Solution Methodology• 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

P

Q

R1

R2

R3

R4

R5

R6

R7

R8R9

02

01

001

101

102

002

112

x1=1

x2=1

0

012


Computing Equilibria & Trajectories


Trajectories:

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

Key Questions

• 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?


Results (1): A Typical Outcome

• 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 (2): Gateways may help Incumbents

• 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 (3): More Harmful Gateway Behaviors• 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 (4): More Harmful Gateway Behaviors

• 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


Technology 1 Technology 2

Full-circle!

Limit Cycles: An Intuitive Explanationα1β>1

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


Conclusions• 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


Network Infrastructure Choice:Shared Versus Dedicated Networks

Topic 2:


Talk Outline:

1. Problem Formulation2. Model & Solution Methodology3. Key Findings & Examples4. Conclusions

Motivation• 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

– 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


Problem Formulation• 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


Model Formulation

• 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

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Reprovisioning Stage

Capacity Allocation Stage

Infrastructure Choice Stage

Solve backwards


Model Variables• 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 separate

Service 2 separate

Common

Fixed Costs cs1 cs2 cc

Contribution Margin

(grows with each unit of realized demand)

ps1 ps2 pc1, pc2

Variable Costs

(incurred irrespective of realized demand)

as1 as2 ac1, ac2

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

Return on capacity= pi /ai

Solution (1): Reprovisioning Stage• Service 2 revenue: (i={s2, d2} for Shared and Dedicated respectively)

– when D2<Ki:

– when D2>Ki:– Reprovisioning Ability:

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

User Contribution

Capacity cost


• A word about reprovisioning ability,– Independent of the magnitude of excess demand– Captures feasibility of and latency in securing additional resources

– So what do and mean?

Solution (2): Capacity Allocation Stage

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

• Optimal Provisioning Capacity (for demand distribution ~U[0, D2

max]):


Solution (3): Infrastructure Choice Stage• 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 2Profit from Service 1


Choice of Infrastructure

• 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

Independent of provisioning decision

Depends on provisioning decision

32

Diff. in optimal capacity cost


h(α)=

Function of pi, ai, α, i={s2,d2}

Analyzing the effect of α on h(α)• 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)


Gross Profit MarginReturn on Capacity

Results: Impact of Reprovisioning


Some Design Guidelines

• Identify cost components • use the model to investigate the net economies/

diseconomies they create– Single threshold for switching choices for most cost parameters

• Check the impact of reprovisioning– 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 transitions


Conclusions

• Developed a generic model 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


Ongoing Work & Future Extensions

• Strategic selection of gateways in network technology adoption

• Dynamics of adoption in two sided markets

• Understanding trade-offs between minimalist and functionality-rich network architectures


Bibliography(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-LiZhang. Dynamics of competition between incumbent and emerging network technologies. In Proc. Of ACM NetEcon'08, pp.49-54, Seattle, 2008.

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

(5) S. Sen, K. Yamauchi, R. Guerin and K. Hosanagar. The Impact of Reprovisioning on the Choice of Shared versus Dedicated Networks. Submitted to WEB, December 2010.

(6) R. Guerin, K. Hosanagar, S. Sen and K. Yamauchi. Shared versus Dedicated Networks: The Impact of Reprovisioning on Network Choice. Under preparation for INFORMS journal on Information Systems Research.

Acknowledgements: Roch Guerin (ESE, Penn), Kartik Hosanagar (Wharton, Penn), Y. Jin (ESE, Penn), Kristin Yamauchi (ESE, Penn), Andrew Odlyzko (Math, UMinn), Zhi-Li Zhang (ECE, UMinn)


Thank You!

modeling the economics of network technology adoption & infrastructure deployment soumya sen 24...

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