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Thesis Proposal On Economic Viability and Choices of Networked Systems & Architectures

This thesis proposal explores the assessment and design of network technologies for both technical and economic viability. It examines factors such as network technology adoption, network design trade-offs, and network functionalities.

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Thesis Proposal On Economic Viability and Choices of Networked Systems & Architectures

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  1. Thesis ProposalOn Economic Viability and Choices of Networked Systems & Architectures Soumya Sen 31st March 2010, Thesis Proposal

  2. Research Motivation • Networked Systems & Architectures have a ubiquitous presence today’s world • e.g., Internet, Power grid, Facilities Management networks, Distributed databases • Success of new network technologies depends not only on technical advantage, but also on economic factors (e.g. price, costs, demand) • Many technologies have failed to deploy • e.g., IPv6, QoS solutions • How do we assess (design) new network technologies (architectures) for technical and economic viability? 31st March 2010, Thesis Proposal 2

  3. Assessing Network Technologies • Network Technology Adoption/ Migration • How can a provider help its technology (service) to succeed? • Network Design Trade-offs • What kind of network architecture should the new technology (service) be deployed on? • Network Functionalities • How much functionality should the new network architecture have? 31st March 2010, Thesis Proposal 3

  4. Research Contributions (1) • Network Technology Migration • 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 full dynamics of technology adoption as a function of user decisions • Characterized the convergence trajectories and equilibrium outcomes • Analyzed the role of gateways in technology migration 31st March 2010, Thesis Proposal 4

  5. Research Contributions (2) • Common vs. Separate 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 31st March 2010, Thesis Proposal 5

  6. Research Contributions (3) • Functionality-rich vs. Minimalist Design • Internet is a successful network with a minimalist design • But should future networks follow its suit? • Pros & Cons of adding functionalities: • More functionalities makes it easier for new services to be created • but the network becomes more expensive to build • Developed an analytical framework to evaluate this design trade-off 31st March 2010, Thesis Proposal 6

  7. Network Technology Migration Topic 1: 31st March 2010, Thesis Proposal 7

  8. Problem Formulation • Two competing and incompatible network technologies (e.g., IPv4 IPv6) • Different qualities and price • Different installed base, e.g. one is starting from scratch • 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 can 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) 31st March 2010, Thesis Proposal 8

  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 of technology (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 31st March 2010, Thesis Proposal 9

  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 • A closer look at the parameters • Cost (recurrent) of each technology (pi) • Externalities: linear in the number of adopters – 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) but no constraint on magnitude, i.e., stronger or weaker than externalities (can have q2 >q1 0 ) • User sensitivity to technology quality ( ) • Private information for each user, but known distribution 31st March 2010, Thesis Proposal 10

  11. 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 31st March 2010, Thesis Proposal 12

  12. Diffusion Model • Assume a given level of technology penetration x(t)=(x1(t),x2(t)) at time t • This translates into an hypothetical number of users, Hi(x(t)), for whom it is rational to adopt technology i at time t (users can change their mind) • At equilibrium, penetration levels satisfy Hi(x*) = xi*, i {1,2} • For a given x(t), expressions for Hi(x(t)) can be explicitly determined from the users’ utility function and decision variables • From hypothetical to actual decisions: Adoption dynamics • Not all users learn and react instantly to information about new penetration levels (rate of adoption in target population) • Modeling approach: A diffusion process with constant rate γ< 1 31st March 2010, Thesis Proposal 13

  13. Solving the Model • Many different “regions” with different behaviors, and adoption trajectories that can cross region boundaries • But it is solvable and we can compute/characterize • All combinations of possible stable (and unstable) equilibria • Adoption trajectories in each region • Trajectories can be stitched as they cross region boundaries 31st March 2010, Thesis Proposal 14

  14. Identifying “Regions” • Delineate each region in the (x1,x2) plane, where Hi(x)has a different expression • There are nine such regions, i.e., R1,…, R9 • They can intersect the feasibility region S0 x1+x21 in a variety of ways This is in part what makes the analysis complex/tedious R1 x2=1 R4 P R2 R5 R6 Q R3 R7 0 R8 x1=1 R9 31st March 2010, Thesis Proposal 15

  15. What do we learn from the model? • What are possible outcomes? • Combinations of equilibria • What trajectories to equilibria? • Monotonic vs. chaotic • What is the role of gateways? • Do they help and how much? 31st March 2010, Thesis Proposal 16

  16. Theorem 1: There can be at most two stable equilibria Coexistence of technologies is possible May arise even in absence of gateways Final outcome is hard to predict simply from observing the initial adoption trends Results (1): A Typical Outcome 31st March 2010, Thesis Proposal 17

  17. 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 31st March 2010, Thesis Proposal 18

  18. 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. 31st March 2010, Thesis Proposal 19

  19. 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 31st March 2010, Thesis Proposal 20

  20. Common Versus SeparateNetwork Architectures Topic 2: 31st March 2010, Thesis Proposal 21

  21. Motivation • Two network services (technologies) to be offered • Provider has an existing (mature) service • New service has demand uncertainty Examples: • Facilities Management services & IT • e.g. Internet & HVAC systems • Video and Data services • e.g. Internet & IPTV services • Broadband over Power lines • Network provider has to decide on network infrastructure: • Common (shared) Network Solution • Separate (dedicated) Network Solution 31st March 2010, Thesis Proposal 22

  22. Problem Formulation • Costs in the two options show economies or diseconomies of scope • New service has demand uncertainty • But capacity needs to be provisioned for services beforedemand gets realized • Dynamic resource “reprovisioning” can be done after true demand becomes known • But some penalty will be incurred (portion of excess demand is lost) • Reprovisioning is becoming feasible (e.g., using virtualization) • How critical is reprovisioning ability in choosing network design? • Compare networks based on profits 31st March 2010, Thesis Proposal 23

  23. Model Variables • Provider’s profit depends on: • Costs: • Fixed costs • Variable costs: grows with the actual number of subscribers (access equipment, billing) • Capacity costs: incurred irrespective of how many users join (provisioning, operational) • Contribution Margin = Service Fees – Variable Costs • Realized Demand: • D1 is known (mature service) : Ks1=Kc1=D1 • D2 is uncertain (only fd2 is known) 31st March 2010, Thesis Proposal 24

  24. Model Formulation • Basic Model: A Two-Service Model • Service 1 (mature existing service) • Service 2 (new service with uncertain demand) • Three-stage sequential decision process • Compare Infrastructure choices based on optimal maximum estimated profits Infrastructure Choice Stage Capacity Allocation Stage Solve backwards Reprovisioning Stage 31st March 2010, Thesis Proposal 25

  25. Solution (1): Reprovisioning Stage • Service 2 revenue: (for the case of Separate networks) • when D2<Ks2: • when D2>Ks2: • Reprovisioning Ability: • A fraction “α” of the excess demand can be accommodated Net Contribution Capacity cost 31st March 2010, Thesis Proposal 26

  26. Solution (2): Capacity Allocation Stage • Expected Revenue, E(Rs2|Ks2), for a given provisioned level Ks2: • Optimal Provisioning Capacity (for demand distribution ~U[0, D2max]): 31st March 2010, Thesis Proposal 27

  27. Solution (3): Infrastructure Choice Stage • Separate Networks: • Service 1 revenue: • Service 2 revenue under ‘optimal’ provisioning: • Total profit: • Common Network: • Infrastructure Choice: • Common if , else separate Profit from Service 2 Profit from Service 1 31st March 2010, Thesis Proposal 28

  28. Choice of Infrastructure • Impact of system parameters: • Varying cost parameters affect the choice of infrastructure • Common to Separate (or Separate to Common). • Most parameters have a single threshold for switching 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 Diff. in optimal capacity cost 31st March 2010, Thesis Proposal 29

  29. Results: Impact of Reprovisioning common-separate-common separate-common-separate No reprovisioning possible (all excess demand is lost) No need for prior provisioning pc2-ac2<ps2-as2 No need for prior provisioning pc2-ac2>ps2-as2 30 31st March 2010, Thesis Proposal

  30. Some Design Guidelines • Identify cost components and use the model to investigate the net economies/ diseconomies they create • Single threshold for switching choices for all most cost parameters • Check the impact of reprovisioning • Whether α has an effect depends on • The magnitude of γ (how far from zero) • The sign of the derivative • Outcomes: Zero, one or two intersections • But do all intersections warrant switching between choices? • For one or two intersections: • One can find a single threshold value for α where to switch network choices 31 31st March 2010, Thesis Proposal

  31. Conclusions • Developed a generic model captures economies and diseconomies of scope that differentiate common and separate networks • Most interesting aspect is thatreprovisioning can also affect the outcome • Validates the need for models to incorporate this feature • Yields guidelines on how reprovisioning affects choice of architecture. 31st March 2010, Thesis Proposal 32

  32. Minimalist versus Functionality-rich Network Architectures Topic 3: 31st March 2010, Thesis Proposal 33

  33. Introduction • Network Providers (NP) create an network platform with capabilities for service innovation: • Provides built-in functionalities to Service Providers (SP) for creating new services • Allows Service Providers and end-users to interact • A Network Provider has to decide: • What level of functionalities to incorporate in their network? • How to charge the service providers and users? NP’s Goal: Maximize network profits 31st March 2010, Thesis Proposal 34

  34. Analyzing the Trade-offs • Arguments for more functionalities in networks: • Allows Service Providers to create and offer new value-added services easily • New services generate higher User demands • Arguments for less functionalities in networks: • Less expensive for Network Providers to build and operate their network • Allows services to innovate their own functionalities 31st March 2010, Thesis Proposal 35

  35. Two-sided Market nSP x Users Service Providers Network Providers p b Network Infrastructure F 31st March 2010, Thesis Proposal 36

  36. Model (1): Users • Factors affecting User’s utility: • Intrinsic benefits: • Heterogeneity in normalized connectivity benefits • Number of Service Providers: • More Service Providers are better • Fees: • Lower fees paid to the network provider is better (flat-fee) User heterogeneity No. of Service Providers fees 31st March 2010, Thesis Proposal 37

  37. Model (2): Service Providers • Factors affecting Service Provider’s utility: • Number of Users: • More users generates higher (advertising) revenue • Fees: • Lower fees paid to the network provider is better (flat fee) • Functionalities: • Can be chosen a la carte • More functionalities makes it cheaper to create new services Cost No. of Users Advertising revenue per user Fees paid to Network Provider SP heterogeneity K(F) is a decreasing function in the no. of functionalities, F 31st March 2010, Thesis Proposal 38

  38. Network Provider’s profit function: Decision Sequence: NP chooses the number of functionalities (F) NP then chooses the fees p and b Fraction of SPs and Users who join the network at equilibrium,nSPand x gets realized. Optimization results: p*, b*, F* Model (3): Network Provider C(F) is an increasing cost function in F 31st March 2010, Thesis Proposal 39

  39. Ongoing Research Investigations • What are the optimal fees (p*, b*) and optimal functionality level, F*? • How does F* vary with system parameters and cost functions? • When does a NP prefer functionality-rich over minimalist design and vice-versa? • How does the F* chosen by the NP compare with that of a social planner? 31st March 2010, Thesis Proposal 40

  40. Future Extensions • Impact on Service Innovation • Availability of built-in functionalities discourage SPs from innovating their own versions • i.e., service quality is lower • User utility is adversely impacted with higher built-in functionalities: • Service Provider’s utility, where k’(F)>k(F): Decreasing function in F 31st March 2010, Thesis Proposal 41

  41. Bibliography • Y. Jin, S. Sen, R. Guerin, K. Hosanagar and Zhi-LiZhang. Dynamics of competition between incumbent and emerging network technologies. In Proc. Of NetEcon'08, pp.49-54, Seattle, 2008. (2) S. Sen, Y. Jin, R. Guerin and K. Hosanagar. Modeling the Dynamics of Network Technology Adoption and the Role of Converters. submitted to Transactions on Networking. 2009. (3) S. Sen, Y. Jin, R. Guerin and K. Hosanagar. Modeling the Dynamics of Network Technology Adoption and the Role of Converters. University of Pennsylvania, Technical Report. June, 2009. Available at http://repository.upenn.edu/ese papers/496/. (4) S. Sen, R. Guerin and K. Hosanagar. Shared Versus Separate Networks - The Impact of Reprovisioning. In Proc. ACM ReArch'09, Rome, December 2009. Thank You! 31st March 2010, Thesis Proposal 42

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