1. Thesis Proposal On 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? 2 31st March 2010, Thesis Proposal

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? 3 31st March 2010, Thesis Proposal

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 4 31st March 2010, Thesis Proposal

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 5 31st March 2010, Thesis Proposal

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 6 31st March 2010, Thesis Proposal

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

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) 8 31st March 2010, Thesis Proposal

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 9 31st March 2010, Thesis Proposal

10. IPv4 (Tech.1) IPv6 (Tech. 2) 10 31st March 2010, Thesis Proposal Technology 1: U 1 ( ,x 1,x 2 ) =  q 1 +(x 1 +α 1 β x 2 ) – p 1 Technology 2: U 2 ( ,x 1,x 2 ) =  q 2 +(βx 2 +α 2 x 1 ) – p 2 A closer look at the parameters –Cost (recurrent) of each technology ( p i ) –Externalities: linear in the number of adopters – Metcalfe’s law Normalized to 1 for tech. 1 Scaled by β for tech. 2 (possibly different from tech. 1) α i, 0  α i  1, i = 1,2, captures gateways’ performance –Intrinsic technology quality ( q i ) Tech. 2 better than tech. 1 ( q 2 >q 1 ) but no constraint on magnitude, i.e., stronger or weaker than externalities (can have q 2 >q 1  0 ) –User sensitivity to technology quality (  ) Private information for each user, but known distribution

11. Low-def. video (Tech.1) High-def video (Tech. 2) Two video-conf service offerings: Low-def & High def –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 Users choose technology as a function of –Price vs. quality trade-off –The level of externality benefits they can enjoy 11 31st March 2010, Thesis Proposal

12. User Adoption Process Decision threshold associated with indifference points for each technology choice:  1 0 (x),  2 0 (x),  2 1 (x),where x=(x 1, x 2 ) –U 1 ( , x) > 0 if  ≥  1 0 (x) - Tech. 1 becomes attractive –U 2 ( , x) > 0 if  ≥  2 0 (x) - Tech. 2 becomes attractive –U 2 ( , x) > U 1 ( , x) if  ≥  2 1 (x) - Tech. 2 over Tech. 1 Users rationally choose –None if U 1 < 0, U 2 < 0 –Technology 1 if U 1 > 0, U 1 > U 2 –Technology 2 if U 2 > 0, U 1 < U 2 Decisions change as x evolves over time 12 31st March 2010, Thesis Proposal x1x1 x2x2

13. Diffusion Model 13 31st March 2010, Thesis Proposal Assume a given level of technology penetration x(t)= ( x 1 (t),x 2 (t) ) at time t –This translates into an hypothetical number of users, H i ( x(t) ), for whom it is rational to adopt technology i at time t (users can change their mind) –At equilibrium, penetration levels satisfy H i (x*) = x i *, i  {1,2} –For a given x(t), expressions for H i ( 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

14. 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 14 31st March 2010, Thesis Proposal

15. Identifying “Regions” Delineate each region in the (x 1,x 2 ) plane, where H i (x) has a different expression –There are nine such regions, i.e., R 1,…, R 9 –They can intersect the feasibility region S 0  x 1 +x 2  1 in a variety of ways This is in part what makes the analysis complex/tedious P Q R1R1 R2R2 R3R3 R4R4 R5R5 R6R6 R7R7 R8R8 R9R9 x 1 =1 x 2 =1 0 15 31st March 2010, Thesis Proposal

16. 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? 16 31st March 2010, Thesis Proposal

17. Results (1): A Typical Outcome 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 17 31st March 2010, Thesis Proposal

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 31st March 2010, Thesis Proposal

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 (α 1 β>1). Theorem 4: Both technologies can hurt overall market penetration through better gateways. Entrant can have such an effect only when (α 1 β 1). Takeaway: Gateways can be harmful at times. They can lower market share for an individual technology or even both. 19 31st March 2010, Thesis Proposal

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 (α 1 β>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 31st March 2010, Thesis Proposal

21. Common Versus Separate Network Architectures Topic 2: 21 31st March 2010, Thesis Proposal

22. 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 22 31st March 2010, Thesis Proposal

23. 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 before demand 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 23 31st March 2010, Thesis Proposal

24. 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: D 1 is known (mature service) : K s1 =K c1 =D 1 D 2 is uncertain (only f d2 is known) 24 31st March 2010, Thesis Proposal

25. 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 25 31st March 2010, Thesis Proposal Reprovisioning Stage Capacity Allocation Stage Infrastructure Choice Stage Solve backwards

26. Solution (1): Reprovisioning Stage Service 2 revenue: (for the case of Separate networks) –when D 2 <K s2 : –when D 2 >K s2 : –Reprovisioning Ability: A fraction “α” of the excess demand can be accommodated Net Contribution Capacity cost Cost ComponentService 1 separate Service 2 separate Common Fixed Costsc s1 c s2c Contribution Margin (grows with each unit of realized demand) p s1 p s2 p c1, p c2 Variable Costs (incurred irrespective of realized demand) a s1 a s2 a c1, a c2 26 31st March 2010, Thesis Proposal

27. Solution (2): Capacity Allocation Stage Expected Revenue, E(R s2 |K s2 ), for a given provisioned level K s2 : Optimal Provisioning Capacity (for demand distribution ~U[0, D 2 max ]): 27 31st March 2010, Thesis Proposal

28. 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 28 31st March 2010, Thesis Proposal Profit from Service 2 Profit from Service 1

29. 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 Independent of provisioning decision Depends on provisioning decision 29 31st March 2010, Thesis Proposal Diff. in optimal capacity cost

30. Results: Impact of Reprovisioning No reprovisioning possible (all excess demand is lost) No need for prior provisioning p c2 -a c2 >p s2 -a s2 No need for prior provisioning p c2 -a c2 <p s2 -a s2 common-separate-commonseparate-common-separate 30 31st March 2010, Thesis Proposal

31. 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

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

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

34. 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 34 31st March 2010, Thesis Proposal

35. 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 35 31st March 2010, Thesis Proposal

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

37. 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) 37 31st March 2010, Thesis Proposal User heterogeneity No. of Service Providers fees

38. 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 No. of Users Advertising revenue per user Fees paid to Network Provider Cost SP heterogeneity 38 31st March 2010, Thesis Proposal K(F) is a decreasing function in the no. of functionalities, F

39. Model (3): Network Provider 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, n SP and x gets realized. Optimization results: p*, b*, F* 39 31st March 2010, Thesis Proposal C(F) is an increasing cost function in F

40. 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? 40 31st March 2010, Thesis Proposal

41. 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 41 31st March 2010, Thesis Proposal

42. Bibliography (1)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! 42 31st March 2010, Thesis Proposal