1. On the Adoption and Deployment of New Network Technologies: An Economic Perspective
Soumya Sen
Dept. of Electrical & Systems Engineering
University of Pennsylvania
Joint Work with:
R. Guerin, K. Hosanagar, Y. Jin
18th November, Penn Seminar on Communications & Networking 1

2. 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)
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
S. Sen On the Adoption and Deployment of New Network Technologies: An Economic Perspective 2 2

3. Research Approach 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
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4. Research Contributions
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
S. Sen On the Adoption and Deployment of New Network Technologies: An Economic Perspective 4 4

5. A Two-part Talk: Outline
Network Technology Adoption
1. Problem Formulation
2. Model & Solution Methodology
3. Key Findings & Examples
4. Conclusions
S. Sen On the Adoption and Deployment of New Network Technologies: An Economic Perspective 5 5

6. Problem Formulation
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)
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7. A Basic User Model Technology 1: U1(,x1,x2 ) =  q1+(x1+α1β x2) – p1
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, 0αi 1, i = 1,2, captures gateway’s performance
Cost (recurrent) for each technology (pi)
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8. Low-def. video (Tech.1) High-def video (Tech. 2)
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, 0αi 1, i = 1,2, captures gateways’ performance
Simplex, asymmetric, unconstrained (α1β >1)
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9. User Adoption Process
Decision threshold associated with indifference points for each technology choice: 10(x), 20(x), 21(x) ,where x=(x1, x2)
U1(, x) > if  ≥ 10(x) - Tech. 1 becomes attractive
U2(, x) > 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 if U1> 0, U1> U2
Technology if U2> 0, U1< U2
Decisions change as x evolves over time (myopic) x1 x2
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10. Diffusion Model
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
H1( x(t))
H2( x(t))
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11. Solution Methodology R1 R4 P R2 R5 R6 Q R3 R7 R8 R9
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+x21 in a variety of ways
This is in part what makes the analysis complex
trajectories cross boundaries R1
x2=1 R4 P R2 R5 R6 Q R3 R7 R8
x1=1 R9
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12. Computing Equilibria & Trajectories
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
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13. 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?
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14. 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
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15. 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
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16. 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.
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17. 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
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18. Limit Cycles: An Intuitive Explanation
α1β>1
Technology 1 Technology 2
Full-circle!
Technology 1: U1(,x1,x2 ) =  q1+(x1+α1β x2) – p1
Technology 2: U2(,x1,x2) =  q2+(βx2+α2x1) – p2
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19. 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
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20. Part 2: Outline
Network Infrastructure Choice: Shared Versus Dedicated Networks
1. Problem Formulation
2. Model & Solution Methodology
3. Key Findings & Examples
4. Conclusions
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21. Motivation Emergence of new services require: Examples:
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
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22. 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
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23. Model Formulation Solve backwards 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
Infrastructure Choice Stage
Capacity Allocation Stage
Solve backwards
Reprovisioning Stage
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24. 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)
Gross Profit Margin = pi - ai , i={s2, d2}
Return on capacity = pi /ai
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
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25. Solution (1): Reprovisioning Stage
Service 2 revenue: (i={s2, d2} for Shared and Dedicated respectively)
when D2 ≤ Ki: Ri (D2 ≤ Ki) = pi D2 – ai Ki
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?
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26. Solution (2): Capacity Allocation Stage
Expected Revenue, E(Ri|Ki), for a given provisioned level Ki:
Optimal Provisioning Capacity:
For demand distribution ~U[0, D2max]:
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27. 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
Profit from Service 2
Profit from Service 1
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28. 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
Depends on provisioning decision
Independent of provisioning decision
h(α)=
Diff. in optimal capacity cost
Function of pi, ai, α, i={s2,d2}
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29. 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)
Return on Capacity
Gross Profit Margin
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30. Results: Impact of Reprovisioning
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)
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31. Conclusions
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
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32. Closing Remarks: On Robustness
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
Economies and diseconomies of scale
Different reprovisioning abilities for shared and dedicated networks (α1, α2 ≠ α)
S. Sen On the Adoption and Deployment of New Network Technologies: An Economic Perspective 36 36

33. Bibliography Thank You! Network Technology Adoption:
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
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, Available at papers/496/.
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.
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.
Thank You!
S. Sen On the Adoption and Deployment of New Network Technologies: An Economic Perspective 38 38