Modeling Economics of Network Technology Adoption & Infrastructure Deployment

1. Modeling the Economics of Network Technology Adoption & Infrastructure Deployment Soumya Sen 24th September, 2010. Princeton University

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) Many technologies have failed e.g., IPv6 migration, QoS solutions How to assess (design) new network technologies (architectures) for technicaland economicviability? Need for analytical frameworks Need for a multi-disciplinary approach 2 24th September, 2010. Princeton University

3. 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? 3 24th September, 2010. Princeton University

4. 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 4 24th September, 2010. Princeton University

5. 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 5 24th September, 2010. Princeton University

6. Network Technology Adoption Topic 1: Talk Outline:1. Problem Formulation2. Model & Solution Methodology3. Key Findings & Examples4. Conclusions 6 24th September, 2010. Princeton University

7. 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 7 24th September, 2010. Princeton University

8. 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) 8 24th September, 2010. Princeton University

9. 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 9 24th September, 2010. Princeton University

10. IPv4 (Tech.1) IPv6 (Tech. 2) 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 βfor Tech. 2 (possibly different from Tech. 1) • αi, 0αi 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 10 24th September, 2010. Princeton University

11. 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 11 24th September, 2010. Princeton University

12. 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) > 0if≥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 x1 x2 12 24th September, 2010. Princeton University

13. 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} • DetermineHi(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)) 13 24th September, 2010. Princeton University

14. 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 S0 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 0 R8 x1=1 R9 14 24th September, 2010. Princeton University

15. Computing Equilibria & Trajectories • Solve Hi(x*) = xi*, i {1,2} in each region Trajectories: 15 24th September, 2010. Princeton University

16. 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? 16 24th September, 2010. Princeton University

17. 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 17 24th September, 2010. Princeton University

18. 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 18 24th September, 2010. Princeton University

19. 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. 19 24th September, 2010. Princeton University

20. 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 20 24th September, 2010. Princeton University

21. Limit Cycles: An Intuitive Explanation Technology 1 Technology 2 α1β>1 Full-circle! Technology 1: U1(,x1,x2) =  q1+(x1+α1β x2) – p1 Technology 2: U2(,x1,x2) =  q2+(βx2+α2x1) – p2 21 24th September, 2010. Princeton University

22. 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 22 24th September, 2010. Princeton University

23. Network Infrastructure Choice:Shared Versus Dedicated Networks Topic 2: Talk Outline:1. Problem Formulation2. Model & Solution Methodology3. Key Findings & Examples4. Conclusions 23 24th September, 2010. Princeton University

24. 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 24 24th September, 2010. Princeton University

25. 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 • beforedemand 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 26 24th September, 2010. Princeton University

26. 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 Infrastructure Choice Stage Capacity Allocation Stage Solve backwards Reprovisioning Stage 27 24th September, 2010. Princeton University

27. 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 28 24th September, 2010. Princeton University

28. 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? 29 24th September, 2010. Princeton University

29. 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]): 30 24th September, 2010. Princeton University

30. 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 31 24th September, 2010. Princeton University

31. 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 provisioningdecision Independent of provisioning decision h(α)= Diff. in optimal capacity cost Function of pi, ai, α, i={s2,d2} 32 24th September, 2010. Princeton University

32. Analyzing the effect of αon 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 33 24th September, 2010. Princeton University

33. Results: Impact of Reprovisioning 34 24th September, 2010. Princeton University

34. 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 35 24th September, 2010. Princeton University

35. 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 36 24th September, 2010. Princeton University

36. 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 37 24th September, 2010. Princeton University

37. Bibliography 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, 2009. Available at http://repository.upenn.edu/ese papers/496/. 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. 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. 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! 38 24th September, 2010. Princeton University