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

Thesis Proposal

On Economic Viability and Choices of Networked Systems & Architectures

Soumya Sen

31st March 2010, Thesis Proposal

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?


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?


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



• 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



• 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


Network Technology Migration

Topic 1:


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)


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


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 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) 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

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


x1 x2

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

2,1 ,)(

itxtxHdt

tdxii

i

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


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 S 0 x1+x21 in a variety of ways

This is in part what makes the analysis complex/tedious

P

Q

R1

R2

R3

R4

R5

R6

R7

R8R9

02

01

001

101

102

002

112

x1=1

x2=1

0

012


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?


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


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


Common Versus SeparateNetwork Architectures

Topic 2:


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


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


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)


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


Reprovisioning Stage

Capacity Allocation Stage

Infrastructure Choice Stage

Solve backwards

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

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


Solution (2): Capacity Allocation Stage

• Expected Revenue, E(Rs2|Ks2), for a given provisioned level Ks2:

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

max]):


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

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


Diff. in optimal capacity cost

Results: Impact of Reprovisioning

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

common-separate-common separate-common-separate


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


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.


Minimalist versus Functionality-rich Network Architectures

Topic 3:


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


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


Two-sided Market

Network Infrastructure

Service Providers


Users

Network Providers

F

b p

nSP x

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

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


K(F) is a decreasing function in the no. of functionalities, F

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,

nSP and x gets realized.

• Optimization results: p*, b*, F*


C(F) is an increasing cost function in F

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?


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


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!


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

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