A Real Options Framework to Value Network, Protocol,and Service Architecture

Mark GaynorBoston University

mgaynor@bu.edu

Scott BradnerHarvard University

sob@harvard.edu

ABSTRACTThis paper proposes a real options framework for evaluatingarchitectural choices and the economic value of thesealternative choices of networks, protocols, and services. Usingproven financial techniques of real options, our modelexplores the value of distributed architecture compared to thebenefits of centralized control. Voice and email case studiesthat agree with our theory and model are presented. We applyour model to illustrate the value of end-to-end structure, whySIP-based VoIP is winning, and the value of open gardenservice business models allowing third parties to providenetwork services/applications. This work illustrates thepotential of real options to help quantify the economic valueof network, protocol, and service architectures.

Categories and Subject DescriptorsC.2.1 [Network Architecture and Design]: Distributednetworks, Centralized networks.

General TermsManagement, Design, Economics, Experimentation,Standardization.

KeywordsNetwork architecture, real options, network services, end-to-end.

1. INTRODUCTIONThe contribution of this paper is a real options basedframework linking the value of flexibility (i.e., the ability toinnovate) to market uncertainty. Our model illustrates thetradeoff between: (1) who has the ability to experiment, (2) thedegree of difficulty of the experimentation, (3) marketuncertainty, (4) the business and technical advantages of amore efficient centralized structure, and (5) how architectureevolves over time. This model is useful in evaluating networkarchitecture principles such as the end-to-end argument [1],comparing network infrastructure variations (such as circuitVs packet switching), protocols within a particular category(such as SIP Vs Megaco/H.248 for voice over IP), and servicearchitectures built with similar protocols (such as centralizedVs distributed music distribution systems). This frameworkshows that when market uncertainty is high, network, protocol,and service architectures that foster experimentation at thenetwork’s edge creates a potential for a greater value for aservice provider than does central administration of networkactivities.

Our framework is useful to designers and managers makingdecisions about network, protocol, and service architectures.Having imperfect information about what users prefer and howmuch services are worth to these users, designers must makemany decisions about distributed compared to centralizedarchitecture, converged networks, and open Vs walled gardenservice architecture. One application of this theory illustratesthe importance of the end-to-end argument by explaining howhigh market uncertainty increases the value of open end-to-end structure to service and software providers. Our modelillustrates why some current network architecture, such asNetwork Address Translation (NAT) and firewalls, that breakend-to-end ideas at a fundamental level will have devastatingconsequences to the opportunities for service providers toprofit from tomorrow's Internet. The fact of data, voice, andvideo convergence is clear, but the architecture of protocols inthe converged network (for example, Voice Over IP (VoIP)protocols) is still uncertain. Our framework helps evaluateopen standards such as the IETF's Megaco (a.k.a. the ITU-T'sH.248, which is an example of an architecture that assumes acentrally managed structure), compared to the IETF's SIP,(which is an example of an architecture that allows bothcentralized and end-to-end management structures), comparedto proprietary protocols such as Cisco's Skinny. BroadbandInternet connectivity is coming, but the business models thatcarriers will use is still not clear: How will services beprovided over broadband Internet connections? Willcentralized service providers control what content and servicesusers are able to access in the name of increasing the serviceprovider's income? Or will a more open garden businessmodel, where users have many choices for content andservices, prevail?

Decisions made today will affect the nature of innovation intomorrow’s network infrastructure; the choices that will createthe most overall value will encourage network infrastructurethat promotes innovation, yet also allow for efficient scalableservices. Our model is useful for evaluating architecturalchoices and the economic value of these alternative choicesthat designers have.

2. DISTRIBUTED VS CENTRALIZEDThe management structure of a network, protocol, orservice/application not only affects the cost and complexity ofexperimentation, but also the range of who can participate incarrying out experiments, which is discussed in detail in[2][3][4][5]. A distributed structure promotes innovation byencouraging cost-effective experimentation with new ways toprovide services that anybody (including end users) canperform. This is in contrast to systems with centralizedstructure that wind up discouraging experiments because of

cost and complexity of experimentation, or because of thereluctance of the network or service manager to allow it.

An example of why experimentation is promoted in adistributed architecture and inhibited in a centralizedarchitecture is depicted in two possible different structures fora music service (See Figure 1). In the distributed architecture,Bob and Alice can use completely incompatible systemsbecause each system only has to deal with its own group ofusers. Furthermore, in a distributed architecture, each user i sable to develop his or her own new devices or protocols. Thisis very different than the centralized model, which utilizes acentralized music server. This architecture may be veryefficient to operate, but it is not as flexible since thecentralized service provider must coordinate any protocolchanges and new devices with all of its users.

3. VALUE OF EXPERIMENTATIONWhen a new type of product or service is first introduced,equipment vendors or service providers may not understandwhat features potential users will want since neither the usersor the providers have any experience with the concept. It i spossible that the first company to introduce the product orservice will get it right, but it's far more likely that they willnot and the field will be open for other vendors to make theirown attempts to discover just what the customers want. Wewill call these attempts to find what the customer wants"experiments."

Vendors experiment with different applications, as well as withsimilar applications that have different feature sets. Eachexperiment is seen by the user as a product and is an attempt tomeet an uncertain market. The economic value ofexperimentation links to market uncertainty by definition:uncertainty is the inability of the experimenter to predict thevalue of the experiment (i.e., predict what the customer wants).When uncertainty is zero, the outcome of each attempt to meetthe market is known with perfect accuracy: the market match i sperfect every time because user preferences are completelyunderstood, and this makes the products/services acommodity where no one makes much money. In low marketuncertainty, it’s hard for any vendor to win big because

competition becomes price based, but when uncertainty i shigh, successful products are likely to generate huge financialsuccess because they can capture a large part of the market witha unique product/service.

3.1 What is an ExperimentExperiments are vendors, service providers, entrepreneurs, andusers trying to meet a perceived customer need with a newarchitecture, protocol structure, service/application, or aparticular feature set for a service/application. Experimentshappen at many levels from network architectures to particularfeature sets built on accepted standards. At one time, the OSIset of protocols and the Internet Protocols were twoexperiments at the network architecture layer. InternetProtocol standards became the dominant design, which openedup a new layer of experimentation: protocol structure withinthe Internet framework, for example Tim Bernners Lee’s webexperiment. A current example of experimentation is withprotocols used to provide VoIP such as SIP, Megaco, Skinny,and H.323. Within each protocol, such as Megaco or SIP, thereis experimentation on features within each protocol. There i salso experimentation at the service/application layer. If SIP i sbeing used, what should you do with it? Chat to friends overyour Internet connection, or maybe integration of your voicemail and email, or … What features should these applicationshave, and how should they work? The point is nobody reallyknows, but many service providers/vendors/users are tryingmany ideas–each one of these attempts is an experiment.

There are many other his toric examples ofexperimentation−some successful, some not. The PBX and itsmany standard features emerged from experimentation withthousands of features from vendors when the PBX designbecame computer based [5]. A standard feature set emerged,and successful PBX features such as caller-ID, speed dialing,and voicemail have been retrofitted into Centrex (a carrier-based PBX service). Not all experiments are successful: forexample, when the PBX vendors in the 80’s experimented withoffering a switched data service using their TDM framework, i tfailed in the marketplace. There are also examples of failedInternet standards: Privacy Enhanced Mail (PEM) and SimpleNetwork Management Protocol v2 were both unsuccessfulexperiments. PEM failed because required international publickey infrastructure never materialized, and customers felt localsecurity infrastructures did not offer enough benefits forcustomer use. SNMPv2 failed because customers felt that itwas too complicated. When market uncertainty is high, weexpect a few experiments to be highly successful and expectsome others to be spectacular failures.

In the lists of experiments above, there is an important thread:in each case, market selection by users determines the value ofthese experiments by choosing what matched his or her ownneeds best, at a price they are willing to pay. It is thisexperimentation, along with market selection of the mostsuccessful experiments that creates the options value ofallowing users to have choice.

3.2 Best of Many ExperimentsIf the potential value of each experiment falls within a normaldistribution, then Figure 2 shows what we expect to happenwhen attempting several parallel experiments. It illustrates theprobability of experiments being a particular distance from themean. V = E(X) denotes the expected value (mean) of aparticular experiment, U(n) denotes the value of the best of n

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Figure 1 Distributed Vs Centralized Music Server

parallel experiments. Looking at the percentages in Figure 2,we expect that 34 percent of the experiments will fall betweenthe mean and +1 standard deviation from it, and so on. Thisillustrates that when the standard deviation (s.d) is high andyour experiment is the most successful at meeting the market,then it is likely you will win big by capturing most of themarket because your product better meets users wants in amarket where products are differentiated by features. Thischance to win big will induce many vendors to experiment,which increases the probability of a superior match and bigwin for the lucky (or smart) vendor or service provider.

Figure 2 illustrates U(10) and U(100), the expected maximumfrom 10/100 experiments. This maximum is composed of twodifferent components: the distribution mean (V) and the offsetfrom the mean. This offset from the mean is composed of twoparts: the effect of the standard deviation and the effect of theparallel experimentation. Thus, the best of many experiments(U(n)) can be broken into these parts: U(n) = V + Q(n)*s.d.Q(n) [6] measures how many standard deviations from themean the best of n experiments is, and the market uncertaintyis the scaling factor. The probability of the best experimentgreatly exceeding the mean increases as the number ofexperiments grows or the standard deviation increases.

The best of these experiments has a value that grows furtherfrom the mean, but at a decreasing rate. As market uncertaintyincreases, so does the gain from experimentation and thus, thepotential for profit. It demonstrates that with low marketuncertainty, even a large number of experiments have lowvalue because the scaling factor overrides the benefit fromexperimentation.

In other words, if everyone knows exactly what the customers(market) want, all vendors will make essentially the sameproduct and it will be a mostly undifferentiated commodity,which implies that no vendor will make much money becausethey will split the business and compete on price. On the otherhand, if no one has a clue of what the customer actually wants,the vendor that guesses correctly will capture the market (andcustomers) all to themselves and will be able to chargewhatever the market will bear. The chance to win big inducesinvestment, which increases experimentation–a winingsituation for users (and the winning entrepreneur).

4. A REAL OPTIONS FRAMEWORKThe above statistical argument links MU to networkarchitecture and gives a good basis for some assumptions, a

theory about the architecture of networks, protocols, and theservices built with this infrastructure, as well as a supportingmodel, which we present below.

4.1 Model AssumptionsBefore presenting an intuitive model based on themathematics of real options, we lay out some simpleassumption and a few basic theories about how marketuncertainty links to network infrastructure architecture.

• There is uncertainty about users preferences; we call thismarket uncertainty. This means that network owners,protocol designers, and service providers cannotaccurately predict the value to their users of what they arebuilding/providing. This market uncertainty is denotedas MU.

• Experimentation with network infrastructure, protocolstructure, and services is possible. Different types ofarchitecture allow different types of experimentation.Market selection picks the best from the many trials. Thisis how the experimentation is evaluated to determine whatmatches the market best as well as its value.

The value of the maximum of n simultaneous experiments isgreater than the expected value for any particular experiment.From above, we know that as the number of experiments or thestandard deviation (i.e. market uncertainty) increases, so doesthe difference between this maximum of n experimentscompared to average value or a single experiment. With highmarket uncertainty and many experiments, the possibility of atruly outstanding market match grows.

• The less disruptive and less expensive it is to experiment,the more experiments there will be. Furthermore, moreexperimenters and the greatest breadth of knowledgeincreases the value of this experimentation. This is whyarchitecture allowing user experimentation withoutaltering the infrastructure of the network (such as end-to-end ideas) creates great value.

• Our theory is limited to situations where there areBusiness and Technical Advantages (BTA) that pushdesigners and managers to favor central management andcontrol. Examples are email (discussed in Section 6.2),centralized account management, which has advantages interms of efficiency, tractability, and oversight, andcentralized music distribution architectures that contentowners prefer because of the better enforcement ofcopyright control.

This is an important assumption because the interesting caseis when there are advantages to centralized structure, yetdistributed architecture creates the most value. When the bestof many experiments is greater than the business and technicaladvantages of central control and management, then flexibledistributed architecture makes sense. This means marketuncertainty is one important variable determining the value ofnetwork, protocol, and service/application infrastructure basedon whether it allows distributed applications/services.

Below we present a model that quantifies this theory usingtechniques from extreme order statistics [7] and real options,which is based on work by Baldwin and Clark [6] about thevalue of modular design.

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Figure 2 How Uncertainty Changes the Value of Experimentation

4.2 ModelOur mathematical model [2,5] validates the above theory froman analytical viewpoint allowing visualization of the tradeoffbetween market uncertainty ( M U ) , the value ofexperimentation from distributed structure, the advantages ofcentralized control and management, and how these networks,protocols, and services/applications evolve over time.

4.2.1 Value of Distributed StructureOur model is based on the cost and value differential betweenmore efficient centralized management and more flexibledistributed structure. We assume a distributed networkinfrastructure enables end users (or organizations) toexperiment with distributed services and applications, andthat centralized management and control implies that endusers are prohibited from any type of experimentation in thecontext of offering new services.

In this model, the Business and Technical Advantage (BTA) ofa centrally managed architecture, relative to a more distributedmanagement style, is represented as a cost difference. Ourmodel applies when it costs less to provide services withcentralized management and control than with distributedarchitecture; thus, the BTA is positive.

The expected value of a particular management structure is thetotal revenue minus the total cost of the networkinfrastructure, protocol structure, or service/applicationarchitecture. With a central management structure that i srestrictive to end user experimentation, we assume only oneexperiment instance (because it is so hard to experiment).Thus, the value is the expected total revenue (i.e. V thedistribution mean) from a single experiment, minus the totalcost.

With distributed structure that promotes end userexperimentation, there are n separate attempts to meet themarket with a market selection process by users to determinethe best outcome along with its market value. From above, weknow Q(n) denotes the value of parallel experimentation, withmarket uncertainty (i.e. the standard deviation) the scalingfactor; therefore, the value of the best choice from nexperiments with the benefit of parallel experimentation inuncertain markets factored in is the expected total revenuefrom the best of n individual attempts to meet an uncertainmarket minus the total cost of the winner.

The main difference between the value of centralized comparedto distributed structure is the additional term representing themarginal value of picking the best of the n experiments (i.e.MU*Q(n)) along with the difference in cost between thearchitectures (i.e. BTA).

Figure 3 illustrates this result: it is the surface representingthe value of users having many choices. It shows the value ofpicking the best experimentation (z-axis) along with itsrelationship to both market uncertainty (x-axis) and thenumber of experimental attempts (y-axis). When MU is low, alot of experimentation is not useful, because the bestexperiment is only slightly better then the average (i.e., the lefthand side of Figure 3). However, with high MU, the best of ntrials is likely to be significantly better than the average (theright side of the figure).

Experimentation by users is worthwhile if the expected valuefrom the experimentation enabled by distributed architectureexceeds the expected value from centralized structure, which i sequivalent to the benefit of experimentation with market

selection being greater than the business and technicaladvantages to centralized control (i.e. MU*Q(n) > BTA).

The above argument illustrates a real options framework tocompare the value of distributed structure allowing a diversegroup of innovators to a more restrictive centralizedarchitecture. It explains how distributed architecture createsmore expected value even when it has business and technicaldisadvantages when compared to centralized control andmanagement.

4.2.2 LearningThe above model provides a framework to help understand andvisualize the relationship between market uncertainties, manyparallel experiments, and the advantages of centralmanagement. By modeling architectures that evolve fromgeneration to generation, this section expands our basicmodel. This is accomplished by introducing learning−that is,experience from previous generations of experiments aboutthe preferences in the market.

The effect of learning is to flatten the distribution curve bydecreasing the standard deviation (i.e. market uncertainty).Learning reduces the benefit of many experiments becauseeach experiment falls within an increasingly narrowing rangecentered around the mean (See Figure 2); thus, over time, manyexperiments help less and less. To incorporate learning, weintroduce a model based on difference equations, whichutilizes a learning function [2] to decrease market uncertaintyat each generation. This multi-stage model captures theevolution of infrastructure over time.

One important question is whether it is better to have fewergenerations with more experimentation per generation, or moregenerations with less experimentation per generation. Withconstant MU (i.e. no learning between generations), thedecreasing rate of increase of Q(n ) implies that moregenerations with less experimentation might be best. However,if MU does decrease, it limits the gain from experimentation,thereby making the answer dependent upon the rate ofdecrease.

4.3 Believability of ResultsThese results display a smooth, somewhat linear surface, whichis attributed to good behavior of the normal distribution. Thisdistribution is well suited to visualize the tradeoffs betweenefficiency, flexibility, evolution, and market uncertainty. It i s

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unlikely that experiments in real life will follow a normaldistribution and will be un-correlated as we assume. However,our model mostly depends on the best of many experimentsalways exceeding the expected value of a single experiment;we believe that for most realistic distributions this is true. Wesuspect a distribution with a heavy tail on both ends, whichimplies that with enough experimentation, there will betremendous failures and stunning successes. The link betweenthe ability to experiment and market uncertainty does notdepend on using the normal distribution that the above resultsare based on.

5. REAL NUMBERS FOR BTA/MUBelow we discuss deriving real numbers for the business andtechnical advantages (BTA) of centralized control and themarket uncertainty.

5.1 BTAWith careful cost accounting, the advantages of centralizedmanagement are not hard to ascertain. BTA is the totaladvantage achieved with central management. It includes bothmanagement and technical components. BTA is very general,as it must capture all the advantages of centralizedarchitecture. The tangible advantages (such as less people tomanage) are directly convertible into hard numbers. Lesstangible advantages (such as the ability to monitor networkuse) can be converted into a cost advantage. Computing BTAis mostly a finance and accounting exercise.

5.2 Market UncertaintyThere are several techniques to determine the level of marketuncertainty. We also propose several methods to put hardnumbers to market uncertainty.

5.2.1 Course Grain Estimate of MUPrevious work has examined different metrics to gauge thelevel of market uncertainty: ability to forecast the market [8],emergence of a dominant design [9], agreement amongindustry experts [5], feature convergence [5], commoditynature of a product [5], and changes in standards activity [5].

5.2.2 Fine Grained Estimates of MUHow might real numbers be assigned to market uncertainty?Market uncertainty is the standard deviation of thedistribution describing the value of services or applicationsfor a particular purpose. There are two ideas we are working onin order to better quantify what market uncertainty means. Onemethod is valuing services provided with open gardenbusiness models, such as iMode. Open garden structurepromotes experimentation by outside (and independent)service providers and vendors. Another possibility is to lookat the market capitalization of organizations that provideInternet services. Can this type of information help determinea number for market uncertainty? We think so!

6. EVIDENCE OF CORRECTNESSIf our theory/model is correct, we expect user preferences tochange in relationship to the market uncertainty. When marketuncertainty is high, or is changing from low to high, we expectusers to prefer distributed solutions; when market uncertaintyis low, or changing from high to low, we expect centralizedarchitecture to become more popular. We illustrate this withtwo case studies [5]: voice services and email.

6.1 Voice Services There are two ways for business to provide voice services: adistributed architecture using a PBX located on site, or acentralized structure using the Centrex service offered bytraditional phone companies. Figure 4 is the number ofinstalled Centrex lines from two different sources [5]. Itillustrates the unpredicted resurgence of Centrex in the mid-80’s. Gaynor’s thesis argues that a decrease in marketuncertainty was one important factor influencing the shiftback to Centrex services at that time. History shows thatsuccessful PBX features such as caller-ID, voice-mail, andautomatic call distribution have migrated from PBX toCentrex. About this time a general agreement had developedabout what phone service consisted of, and whatever themethod of delivering the service, the service itself was mostlythe same. This example illustrates how successful ideas fromdistributed environments can migrate into more centralizedstructure, and in this case, more efficient structure when marketuncertainty is reduced.

6.2 Web Based EmailEmail has become one of the Internet’s killer applications overthe last ten years. Initially, email based on Internet standardshad a distributed flavor with traditional POP basedarchitecture; there has, however, been a recent shift in userpreferences. As Figure 5 illustrates, centralized web-basedemail architecture boasts explosive growth. Gaynor’s thesisargues that this shift to centralized web-based email is linkedto a reduction of market uncertainty in the email area, asevidenced by the fact that email user agents began to offer thesame set of features, the emergence of a dominant design (i.e.the Internet set of email standards) and stability in the basestandards. Centralized email became popular after theuncertainty with email standards decreased.

Both the voice and email case studies support our theory:market uncertainty is a critical factor in deciding betweencentralized or distributed management structures.

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7. APPLYING THIS FRAMEWORKThere are several ways to use this real options framework: as avisualization tool to help management understand that thereare strategic advantages to a more costly, but more flexible,network infrastructure, as a quantitative guide for technicaldesign choices1, and as a tool for VCs to evaluate investmentoptions. Venture Capitalists, Marketing and other non-technical managers need evidence that building moreexpensive architecture can translate into more revenue, sinceusers are willing to pay more for services that meet their needsbetter. In the examples below, we apply our model as a lever toconvince management that choices such as end-to-endstructure, SIP as a VoIP protocol, and open gardens createtremendous value when markets are uncertain. Our frameworkis not yet ready as a quantitative guide to help technicaldesigners, however, once we better understand how to estimatemarket uncertainty (See Section 6), this framework will bemore useful in this context. In its current state, our modelprovides an intuitive justification of the value of distributedstructure; in the future, this framework could become avaluable quantitative method to allow technical networkengineers to evaluate different proposals for buildinginfrastructure and applications.

The Internet has changed, which has caused a re-thinkingabout end-to-end ideas. VoIP, Broadband, and other newtechnologies are posing many important questions that needanswers. Data and voice are converging, but the dominantstandards have not emerged to achieve this merging of datatypes. Network owners have choices about supporting open orwalled garden business models. The decisions of today affectthe value of innovation tomorrow. Below is a brief look atthese new technologies. We have discovered a common thread:network and application infrastructures that allow distributedend-to-end experimentation, as well as efficient centralizedcontrol of the most popular service/application, are doing wellin the current market.

7.1 Today's InternetThe end-to-end agreement has been fundamental to the successof the Internet. Because of the historical end-to-end structureof the Internet, end users have been able to experiment withnew ideas, unlike in the centrally controlled PSTN. Userexperimentation with end-to-end services/applicationstranslates into more choices for all users. Our real optionsargument explains the value of allowing users to experiment,

1 Thanks to an anonymous reviewer for this comment.

as well as how this value is linked to market uncertainty–highmarket uncertainty implies greater value of allowing end-to-end services/applications.

Today's Internet is different from its early days because of thescope of its use and those who use it. The end-to-end argumentthat fostered innovation in the early Internet must adapt to itscurrent reality as Clark's and Blumenthal's [10] recent paperdiscusses. They explain the impact of new network devicessuch as Network Address Translators (NAT) [11] and firewallsthat affect the end-to-end nature of Internet applications. NATsand firewalls, by limiting what users can do, adversely affectinnovation within the current Internet. Other protocols, suchas a service authenticating a user before allowing them toperform a task, are not end-to-end in the pure sense, yet can beseen as a combination of end-to-end transactions. Even moreimportant, this type of verification from a "trusted authority"makes intuitive sense in today's Internet, and does not appearto limit experimentation. We find that some alterations frompure end-to-end services still retain the general ideas and stillpromote experimentation, but other alterations, such as NAT’s,are not justified and break the end-to-end paradigm inunacceptable ways because they stifle innovation.

7.2 NATs and FirewallsNAT architecture breaks unique global IP addressing. Whatmight have seemed a clever idea was not, because NAT’s breaksthe end-to-end model when changing the network addresswithin the IP header of a data packet. Using a NAT prevents awhole range of existing and future services/applications fromworking. Any service that requires knowing the IP address ofone endpoint (as seen by another endpoint) does not workwith a NAT2. For example, most Voice-over-IP protocols,including SIP and H.323, break with NAT technology becausethe IP address of each endpoint for these voice applications towork must be known by the other endpoint. NATs break end-to-end Internet security protocols such as IPSec, because partof the underpinning of IPSec is the uniqueness and security ofIP addresses, and NATs change this. While not prohibitinginnovation, NATs do make it more difficult.

In the context of innovation, however, firewalls have a greaternegative impact than NATs. First, while NAT is not required ina firewall product, most have this NATing ability. Moreimportant, the basic function of a firewall is to filter outpotentially bad traffic3, which is often defined as anything to atransport level port address not on the list of allowed services.This is far worse than the NAT function, because instead ofbreaking a particular class of applications (like NATs do), i tblocks all new applications not on the approved list.Innovating new applications with firewalls between users i snot possible unless the firewall is configured to allowexperimentation of this new service. The filtering function offirewalls is bad, but when combined with NAT, innovationbecomes even more constrained.

While they may be attractive and are sometimes required forother reasons, firewalls and NATs make network

2 The IETF has just approved a protocol called STUN [15],

which allows a device behind a NAT to know it’s external IPaddress.

3 Firewall filtering is not as secure as some believe. Thetechnique of HTTP tunneling described in the Aprils fools’day RFC 3093 explains why.

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Figure 5 Growth of web-based Email (in 1000’s) of lines

experimentation harder, and thus, according to our real-options framework, reduce the overall value of the networksince users will have fewer choices.

Figure 6 illustrates one aspect of the architectural evolution ofthe Internet: the degree of centralized control and management.It illustrates the traditional Internet and its current direction.The traditional (end-to-end) Internet had a very distributedinfrastructure, which implies it does best when marketuncertainty is high. Current architecture changes to theInternet such as MPLS, NATing, firewalls, Megaco, RSVP QoSservices, and security services are pushing the current Internetinfrastructure toward the right because of the imposedcentralized control of these architectural changes.

7.3 VoIP – Why SIP is WinningIt now appears that SIP will become the dominant standard forend device control in VoIP at least for the near future. Our realoptions based framework illustrates why architectures such asSIP, which allow the flexibility to build infrastructure witheither end-to-end or centralized structure, have more valuethan standards such as Megaco/(H.248) that force centralizedcontrol by prohibiting end-to-end services [12], or proprietaryprotocols such as Cisco’s Skinny where a single vendorcontrols the standard. SIP promotes more innovation byenabling end users to experiment, through intelligent enddevices, which provide services. This creates more choices forusers, and according to our real options framework, createsmore value at least until such a time when a commonunderstanding will have developed on the nature and featuresof Internet-enabled voice services.

Figure 7 illustrates real options predictions of the value ofdifferent architectures for voice infrastructure. With traditionalvoice services, the market uncertainty is low. Centrex, acentralized architecture, allows very little experimentation;thus its value falls in the front left corner, the region of lowestvalue. With VoIP, the market uncertainty is high, whichincreases the value (moving to the right side). Proprietaryprotocols make innovation hard because experimentation islimited to a single vendor, which places its value in the rightfront. SIP is in the high value region because it promotesexperimentation, and the high market uncertainty makes theexperimentation worthwhile. Our frameworks prediction thatSIP creates the most value seems to be correct as SIP i swinning the standards battle for controlling end devices thatprovide VoIP services.

7.4 Open Vs Walled Garden This real options framework is useful to evaluate policydecisions. Observers such as Larry Lessig [13] are applyingend-to-end type thinking towards policies related to owners ofcable networks and independent service providers that want toprovide network services using the basic infrastructure of thecable network. Should cable network owners be able to controlwhat services (and hence control, among other things, whatcontent) a user can choose from? Our model implies that this i snot a good idea even for the cable network owner. As wediscussed earlier, greater value emerges when users are able tochoose the services and the providers of these services withoutinterference from cable network owners. Any other policystifles innovation, causing a reduction in value. ProfessorLessig agues how important it is to keep neutrality in theInternet. He notes that many companies such as Disney andMicrosoft support this open garden view of the Internet.Today's telecom policy regarding open cable network accesswill sculpt tomorrow’s landscape; it is critical that policymakers choose the right policies. As the Internet has shown,“right” means allowing end users to choose the services andcontent they want.

Cable network owners argue that because they paid forbuilding the network infrastructure they should have completecontrol over user access to all content and services. However,the real options framework illustrates how when users’ choicesare limited by the network owner or operator, everybody loses.Achieving maximum value in the context of meeting userneeds requires service providers unaffiliated with the corenetwork to be allowed to offer content andservices/applications

One measure of a networks value is the satisfaction of itsexisting users. Another measure is the network’s ability toattract new customers. Our real options framework illustrateswhy open gardens have greater value (measured by eithermetric) than walled gardens. More users with greatersatisfaction will translate into increased revenue. Users likeopen gardens because there are more choices for them than in awalled garden that limits their choices. Users living in opengardens are less likely to switch networks to find service orcontent more to their liking than users in walled gardensbecause those in open gardens already have these choices. Inthe context of value to current users, ability to keep users, andattractiveness to new users, our model illustrates why opengardens win every time (unless you assume that there will beno further innovation of network-based services in the future).

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Figure 7 Mapping VoIP architecture into a real options framework

7.5 Common ThreadThe common thread in all these examples is the success ofprotocols and infrastructure that allow flexibility. Theflexibility of distributed services promotes innovation, aswell as centralized control, so that successful applications canbe efficient, scalable, and safe. At many levels of the protocolstack, protocols and applications being adopted are flexible inregard to the architecture of applications/services they allow.At the application protocol layer, SIP and Internet emailprotocols demonstrate management flexibility. SIP allows trueend-to-end services as well as a more centralized architecturevia proxy servers and calling agents [12]. Internet email allowsboth distributed and centralized management structure asdiscussed in [5]. Finally, at the highest level are Web-baseduser applications, which have the property of flexiblemanagement structure. Protocols that are flexible in regard tomanagement structure are successful at many layers, whichhighlights the value of flexibility in allowingservices/applications to have either distributed structure orcentralized control.

8. RELATED WORK ON OPTIONSThe framework used to illustrate why high market uncertaintyenhances the value of allowing distributed services is basedon the theory of real options, which extends the theory offinancial options to value options on real (non-financial)assets [14]. Real options provide a structure linking strategicplanning and financial strategy. Similar to financial options,real options limit the downside risk of a design (orinvestment) decision without capping the upside potential.This theory has proven useful in examining a plethora ofsituations in the real world, such as staged investment in ITinfrastructure [14], oil field expansion, developing a drug[14], showing the value of modularity in designing computersystems [6], explaining the value of modularity in standards[3,4], and network based services [5]. This paper demonstrateshow network infrastructure that allows distributed end-to-endservices/applications is needed to maximize value in thecontext of meeting users needs in uncertain markets. Previouswork on real options by Baldwin and Clark [6] discussing thevalue of modularity in a computer system provides thetheoretical framework for this model, which is expanded uponby Gaynor and Bradner [4][5].

9. CONCLUSIONThis paper presented a real options framework useful tocompare network, protocol, and service/application structure.Our model explains why high market uncertainty implies thatservice providers and users will profit from a distributedarchitecture because of the value of experimentation, as well asthe desire of users to have choices. It also illustrates how whenmarket uncertainty is low, an efficient, safe, and controllablecentralized management structure is more desirable to serviceproviders and users because users don’t value many choiceswhen all the choices meet their needs well. We present a theory

and model, which is supported by empirical evidence. Finally,we illustrated the value of this framework by applying it toseveral diverse areas such as the value of end-to-end ideas,analyzing the choices for VoIP architecture, and explaining thevalue of open compared to walled garden service/applicationbusiness models that promote users choices incontent/services they have access to. Our model explains whyusers prefer infrastructure that allows flexibility in usage.

10. REFERENCES[1] Saltzer, J, and Reed D, and Clark, D., “End-To-End

Arguments in System Design.,” ACM Transactions inComputer Systems 2, 4 , Nov: 1984 p 277–-288.

[2] Gaynor, M, The Effect of Market Uncertainty on theManagement Structure of Network-based Services. Ph.D.Thesis, Harvard University 2003.

[3] Gaynor, M., Bradner, S. Iansiti, M, and Kung, HT., TheReal Options Approach to Standards for BuildingNetwork-based Services., Proc. 2nd IEEE conf onStandardization and Innovation, Boulder, CO,. Oct, 2001.

[4] Gaynor, M. and Bradner, S. Using Real Options to ValueModularity in Standards, Knowledge Technology andPolicy, Special on IT Standardization, 14:2.*, 2001

[5] Gaynor, M, Network Service Investment Guide:Maximizing ROI in Uncertainty Markets, Wiley, 2003.

[6] Baldwin, C. and Clark, K. (1999). Design Rules: The Powerof Modularity. Cambridge, MA: MIT Press.

[7] Lindgren, B.W, Statistical Theory, NY: Macmillan, 1968

[8] Tushman, M, and Anderson, P, “TechnologicalDiscontinuities and Organizational Environments,”Administrative Science Quarterly, 31 (1986) pp 439-465.

[9] MacCormack, Alan, “Towards a Contingent Model of theNew Product Development Process: A ComparativeEmpirical Study.” Working Paper 00-77, 2000, HBS.

[10] Clark, D, and Blumenthal, M, “Rethinking the design ofthe Internet: The end-to-end arguments vs. the brave newworld,”. TRPC August 10, 2000.

[11] K. Egevang and P. Francis, RFC1631 – The IP NetworkAddress Translator (NAT). May 1994

[12] Gaynor, M, “Linking Market Uncertainty to VoIP ServiceArchitecture”, IEEE, Internet Computing, July/Augest2003

[13] Lawrence Lessig. 2002., The Future of Ideas: The Fate ofthe Commons in a Connected World, Vintage Books.,2002

[14] Amram, M. and Kulatilaka, N., Real Options, ManagingStrategic Investment in an Uncertain World. Boston, MA:HBS press ,1999.

[15] Rosenberg, J, and Weinberger, H, .. RFC 3489, STUN, IETF,March 2003.