the economics of cloud computing 10-7-11

5/26/2018 The Economics of Cloud Computing 10-7-11

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The Economics of Cloud Computing1

Ergin Bayrak

University of Southern California

John P. Conley

Vanderbilt University

and

Simon Wilkie

University of Southern California

Abstract

Cloud computing brings together several existing technologies including service oriented

architecture distributed grid computing virtuali!ation and broadband net"orking to provide

so#t"are in#rastructure and plat#orms as services. $nder the old %& model companies built their

o"n server #arms designed to meet peak demand using bundled hard"are and so#t"are solutions.

&his "as time consuming capital intensive and relatively in#lexible. $nder the cloud computing

model #irms can rent as many virtual machines as they need at any given time and then either

design or use o##'the'shel# solutions to integrate company'"ide data in order to easily distribute

access to users both "ithin and outside o# the company #ire"all. &his converts #ixed capital costs

into variable costs prevents under and over provisioning and allo"s minute by minute #lexibly.

Consumers are also increasingly turning to the cloud #or computing service through such

applications as (mail Pandora and )acebook. &he purpose o# this paper is to discuss this ne" and

trans#ormative technology survey the existing economics literature on the sub*ect and suggest

potential directions #or ne" research.

1&he authors take #ull responsibility #or any errors and may be contacted at ebayrak+usc.edu*.p.conley+vanderbilt.edu ands"ilkie+usc.edu respectively.

,ey"ords- Cloud Computing SaaS PaaS. %aaS Economics. %n#ormation &echnologyJE Categories- /0 1 /2 2

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mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


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1. Introduction

Cloud computing is a ne"ly emerging computing paradigm in "hich computing resources such as

so#t"are processing po"er and data storage are provisioned as on'demand services over

broadband net"orks. Cloud computing enables a shi#t a"ay #rom computing as a bundled hard"are

and so#t"are product that is ac3uired through #ixed capital investments to computing as a location

independent and highly scalable service that is ac3uired on'demand over broadband net"orks #rom

large'scale computing centers or 4clouds5 on a pay'per'use basis "ith little or no #ixed capital

investment. &he cloud approach leads to cost reductions and e##iciency gains through economies o#

scale distribution o# costs over large pools o# users centrali!ation o# in#rastructures in areas "ith

lo"er costs and improved resource utili!ation rates. &hese e##iciency improvements allo" large

savings in operational costs signi#icant reductions in the up#ront capital costs re3uired #or ne" tech'

startups. As a result many observers have characteri!ed cloud computing s as a disruptive general

purpose technology "ith potential #or enormous impacts on the economy as a "hole.

Although relatively ne" cloud computing is already a very signi#icant part o# the technology sector.

A recent report by %& research and advisory #irm (artner #orecasts "orld"ide cloud services

market6s revenue to surpass 789.: billion in ;


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/espite the social and economic signi#icance o# cloud computing very little "as been "ritten in the

economics literature that is directly on topic. >ost papers seem to appear in various parts o# the

computer science and to a lesser extent the #ormal and in#ormal business literature. ur purpose

"ith this paper is three #old. )irst to discuss the basics o# "hat cloud computing is "ith special

attention to its economic implications. Second to survey cloud computing literature as it relates to

economic 3uestions. Since very #e" papers have appeared in economics *ournals "e extend the

survey to include some o# the more relevant CS and management literature as "ell as older

economics papers on in#ormation technology that may shed light on cloud computing. )inally to

explore open research 3uestions and discuss ho" economics might contribute to our understanding

o# this ne" and important technology. %n section t"o "e revie" various de#initions o# cloud

computing. %n section three "e contrast cloud computing "ith prior general'purpose technologies

#rom an economic perspective and identi#y characteristics that give cloud computing a distinct

economic structure. We also identi#y policy issues in the cloud computing ecosystem. Section #our

proposes open research 3uestions in the economics o# cloud computing. Section #ive concludes.

2. hat is Cloud Computing!

/espite the "ide consensus that cloud computing is a ne" and disruptive general purpose

technology there does not seem to be a comparable consensus de#ining "hat exactly cloud

computing is nor a common understanding o# ho" it a##ects economic #undamentals. &his may be

due to the scope o# this ne" technology as "ell as its complex and multi'layered technical and

economic underpinnings. Some common de#initions #ail to capture this complexity such as 4moving

computer applications and programs to the %nternet #rom the desktops5. ther de#initions are "ell

received in the computer science literature but may be overly technical and so less use#ul to

economists and other outsiders. A collection o# t"enty t"o such de#initions are summari!ed in

Fa3uero et al. ?;


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Clouds are a large pool o# easily usable and accessible virtuali!ed resources ?such as

hard"are development plat#orms andGor services@. &hese resources can be

dynamically recon#igured to ad*ust to a variable load ?scale@ allo"ing also #or an

optimum resource utili!ation. &his pool o# resources is typically exploited by a pay'

per'use model in "hich guarantees are o##ered by the %n#rastructure Provider by

means o# customi!ed Service evel Agreements ?SA@.

An evolving de#inition maintained by $nited States Hational %nstitute o# Standards and &echnology

?H%S&@ seems to be the most comprehensive and "idely accepted de#inition o# cloud computing

?>ell and (rance ?;


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use composability as the criterion to de#ine various layers in the cloud architecture and inter'

relations bet"een those layers. &hey classi#y a cloud layer to be higher in the architecture ?or at a

higher level o# abstraction@ i# its services can be composed o# services #rom the underlying layers.

&he three primary cloud computing service models are presented belo".

So#t"are as a Service ?SaaS@ is the service model in "hich the capability provided to the consumer

is the ability to use the cloud provider6s applications running on a cloud in#rastructure. Applications

are accessible #rom various client devices through a thin client inter#ace such as a "eb bro"ser.

&his highest layer in the cloud in#rastructure is called the Cloud Application ayer in Kouse## and

/a Silva6s ?;


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resides in the Cloud So#t"are %n#rastructure ayer in Kouse## and /a Silva6s ?;icroso#t6s Connected Service

)rame"ork ?CS)@ on the other hand accompanies its %aaS services as a CaaS component that can

be rented separately.

We "ould like to suggest that "hile these service models are use#ul in de#ining the technological

di##erences bet"een various layers o# abstraction they are not as use#ul in categori!ing their

economic impacts. %nstead "e "ould like to distinguish bet"een a retailand wholesaleside o# cloud

computing and argue that these are t"o 3ualitatively di##erent uses #or the cloud. %aaS and PaaS

consumers are mostly companies using the cloud to outsource their internal %& #unctions or provide

client #acing applications including SaaS services. SaaS consumers on the other hand are mostly

individuals moving their data ?email social net"orking and backup #or example@ simple

computation needs ?"ord processing and spreadsheets@ and content and entertainment

consumption ?using Pandora ulu and World o# Warcra#t instead o# buying C/Ms /F/Ms or

so#t"are@ to the cloud8 . Although there certainly exist some signi#icant crossovers #or example

"hen individuals use PaaS or %aaS to run "ebsites on virtuali!ed servers or large companies use

SaaS to outsource some #unctions such as CL> the 3ualitative distinction bet"een the retail and

"holesale nature o# SaaS and PaaSG%aaS remains. We "ill discuss this in more detail in subse3uent

sections.

6# course SaaS providers are o#ten consumers o# lo"er level PaaS and %aaS services i# not already vertically

integrated to PaaS and %aaS providers.

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)rom the cloud providers6 perspective these three service models can be set up according to #our

ma*or deployment strategies. A private cloud in "hen the cloud in#rastructure is operated solely #or

a single organi!ation. A community cloud is similar but the cloud in#rastructure is shared by

several organi!ations and supports a speci#ic community that has shared interests. &hese cloud

in#rastructures may be managed by the organi!ations themselves or a third party and may exist on

premise or o## premise. A public cloud involves the cloud in#rastructure being made available to the

general public or a large industry group and is typically deployed by a separate organi!ation selling

cloud services. )inally a hybrid cloud is a composition o# t"o or more deployment models that are

bound together by a technology that enables data and application portability.

". # $iscussion of the %iterature &elated to the Economics of Cloud Computing.

Cloud computing started in the last decade as a result o# the convergence o# several earlier

technologies and %& operating models. )rom a technical point o# vie" cloud computing "as

enabled by a combination o# virtuali!ation cluster computing grid computing broadband

net"orking and large scale data centers centrali!ed at lo" cost locations. &he development o#

service'oriented so#t"are architectures #or creating business processes packaged as services along

"ith service level agreements that contractually speci#y such things as delivery time or per#ormance

re3uirements #urther enabled the provision o# computing as a service. &he recent #inancial crisis

and recession have also contributed to the accelerating adoption o# cloud computing as companies

have been #orced to #ind cost'e##ective %& solutions. &his re#lected in a %/C ?;


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empirical studies on %& adoption surveyed in )ichman ?2==;@ contribute to the conclusion that %&

adoption has di##erent determinants depending on the class o# technology in 3uestion and locus o#

adoption. )ichman ?2==;@ distinguishes type 2 technologies "hich have minor user

interdependencies and relatively small kno"ledge barriers to adoption and type ; technologies in

"hich both o# these #actors are signi#icant. >ost PaaS and %aaS cloud o##ering many business

oriented SaaS cloud o##ering can be characteri!ed as type ; technologies. &he locus o# adoption

on the other hand may be either at the individual or organi!ational level. Except #or simple SaaS

applications that individuals adopt as they move to the cloud "ith most cloud computing o##erings

the locus o# adoption is organi!ational. An illustrative classi#ication cloud computing applications

according to this #rame"ork can be #ound in appendix ;.

Farious empirical studies seem to con#irm that the determinants o# classical innovation di##usion

dynamics carry over to the personal adoption o# type 2 technologies. )irstly a #avorable perception

o# %& innovation positively a##ects the rate and pattern o# adoption ?see /avis et. al. 2=9= and u##

and >unro 2=9=@. Secondly adopters are di##erentially in#luenced both by in#ormation channels

and sources at various stages o# adoption. %n particular early adopters tend to be younger highly

educated involved in mass media and interpersonal communication and more likely to be opinion

leaders ?Brancheau and Wetherbe 2==


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capacity "ith respect to innovations. Adopter attitudes and pre#erences training accessibility o#

consulting and in#luential peers are also sho"n to contribute to individual adoption o# type ;

technologies.

)inally in terms o# the organi!ational adoption o# type ; technologies classical di##usion

determinants are again #ound to be important. &he standard S'shaped cumulative adoption pattern

"as con#irmed #or this type o# adoption by (urbaxani ?2==endelson ?2==


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di##erentiates one #rom one another "hich in turn a##ects their individual economic competitiveness.

&"o hypotheses that stem #rom this vie" are that the application6s ?i@ strategic value and ?ii@

inimitability are both negatively associated "ith SaaS'adoption. /espite the correct prediction o# the

direction o# impact in both hypotheses only the #irst one #inds signi#icant support in the data.

)inally Benlian and Buxmann ?;


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&he #irst similarity bet"een cloud computing and traditional utility models electricity or telephony

#or example is that they all have characteristics o# a disruptive general'purpose technology "hich

make a surge o# associated innovation possible. (iven the rapid development o# cloud computing

and the relatively accelerated adoption cycle "e seem to be experiencing cloud computing is likely

to have an even #aster rate o# economic impact than electri#ication or the gro"th o# telephone

net"orks had historically

A second similarity is the extensive cost savings that cloud approaches permit. Cloud providers6

cost savings result mainly #rom economies o# scale through statistical multiplexing virtuali!ation and

clustering that enable higher utili!ation rates o# centrali!ed computing resources. &his in turn is

possible because cloud provides can place their %& in#rastructure in rural areas "here both real

estate and labor are relatively cheap and close to po"er sources and internet backbone #iber

net"orks. %n addition the labor cost o# maintaining the computing in#rastructure can be distributed

across a greater number o# servers in large computing centers and this also contributes to the cost

savings and economies o# scale2


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So called 4Cloud Heutrality5 is an interesting emerging policy issue addressing this problem that in

many "ays mirrors the net"ork neutrality debate. pen cloud mani#esto22is an attempt to establish

principles and standards to support an open and interoperable cloud system "hich is supported by

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&he problem o# latency adds a layer o# complexity to cloud systems. &he relatively lo"er cost o#

moving photons over #iber compared to the cost o# moving electricity over the grid #avors locating

computing centers a"ay #rom the clients and closer to po"er sources ho"ever the savings in

po"er cost can be out"eighed by the cost o# in#ormation latency due to distance and net"ork

congestion. )or instance the need #or instantaneous computing in the case o# #inancial services and

trading may dictate that computing be local.

/ata security is yet another concern in cloud computing systems. n the one hand consumers o#

cloud services may be apprehensive about trusting outside companies "ith their private mission'

critical data since many o# the measures taken to ensure data security on the part o# cloud providers

are obscure to the consumer. Humerous examples o# security and privacy breaches most #amously

on the )acebook plat#orm or the (mail system create grounds #or consumer concern. n the other

hand cloud providers value success#ul security implementation as one o# the most important assets

#or gaining positive reputation. &hey there#ore have a strong incentive to maintain success#ul

security practices. arge providers can a##ord to hire experts and spread the #ixed cost o#

implementing good security protocols over a larger user base and so cloud providers may actually

have both a technological and cost advantage in this dimension.

o" one should design and implement systems that enable users to combine share and trade

computing resources is a 3uestion that has received signi#icant interest in the computer science

literature. &his is especially true in the (rid Computing sphere "here market mechanisms have

been proposed and implemented to trade computing resources in the #orm o# virtuali!ed server

instances. Altmann et al. ?;


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in a series o# articles collected in Altmann and ,lingert ?;


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existence o# envy at ,S solutions depending on utility pro#iles /L) al"ays results in an envy'#ree

allocation.

)riedman et al. ?;


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outline some o# the economic 3uestions that cloud computing raises and suggest ho" economic

analysis might contribute to our understanding this ne" technology.

n the retail side o# cloud computing "e can begin by looking at the problem #rom the perspective

o# consumer theory. &he development o# an e##ective technology to make micro'payments #or

content and services has the potential to revolutioni!e the "eb. %n practice ho"ever consumers

already do make micro'payments indirectly ?and in a second best "ay@ by giving up personal

in#ormation to providers o# cloud services in exchange #or access. )or example )acebook is an

SaaS that provides storage and net"orking services to its users. $sers have a choice o# ho" much

in#ormation to give to the site such as oneMs email address physical address religion age

relationship status pictures o# oneMs sel# and #riends links to content one likes content that one has

created etc. &he #urther a user reveals in#ormation the more sharing he can do "ith his #riends.

Similarly "hen "e use (oogle our searches are tracked and indexed ulu and Het#lix create

records o# our vie"ing habits Fisa and >asterCard kno" ho" much li3uor "e buy Ebay Expedia

and especially Ama!on kno" a great deal about our consumption habits and many other sites

deposit tracking cookies. Content and service providers moneti!e this data directly by serving ads to

users based on this data or by selling or renting the accumulated in#ormation on a user to third

parties.

&his already active exchange provides a basis #or developing a theory o# the 4economics o#

privacy5. As a currency that can be exchanged #or services privacy has some interesting #eatures.

)irst the property rights are not clearly de#ined. What can be done "ith in#ormation "e post or

give to a site depends both on regulations and the terms o# service. Since many providers o# cloud

services especially social net"orking sites have signi#icant net"ork externalities and lock'in e##ects

consumers are not in a good position to the resist changes in the terms o# service "hich e##ectively

raise the privacy cost o# using the service. &hus there is a case to use regulation to prevent such

abusive monopoly practices. Hote ho"ever that even though )acebook can sell a user6s

in#ormation that user can also choose to give the same in#ormation to another provider. &hus

)acebook does not 4o"n5 our in#ormation in a conventional sense. Privacy is like a ten dollar bill

that "e can use again and again. Economists "ould say that private in#ormation is excludable ' %

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can re#use to give it to site but also non'rivalrous ' % can give it again and again to many sites.

o"ever a user may think that once he has spent 4ten dollars5 "orth o# privacy at one site he

might as "ell spend the same ten dollars "ith anyone "ho o##ers anything use#ul to us. o"ever i#

the privacy price is t"enty dollars ?meaning "e have to give up additional in#ormation@ he "ould

have to think again. nce the in#ormation is released it can never be called back. &he cloud never

#orgets.

A theory o# privacy might include the #ollo"ing #actors. Hot all privacy is e3ually valuable to service

providers. Advertisers "ould pre#er to market to richer people all else e3ual. &his means that the

rich are being underpaid and the poor overpaid #or their private in#ormation. Providers may try to

enrich the 3uality o# the user'base by o##ering services that selectively appeal to valuable

demographics but in general "e "ould expect to see the rich under'participating in the cloud. %# it

"ere possible to certi#y that certain users "ere #rom a valuable demographic providers could o##er

them 4gold memberships5 "ith enhanced services in exchange #or their in#ormation. )or no" this

is a market #ailure. ne might also think about time inconsistent pre#erences. )or example young

people value social net"orking and other cloud services more than older people and also value

their privacy less. Who cares i# an 29 year old posts something stupid or a ;; year old shares an

embarrassing picture Ho harm done. bviously as one gets into oneMs thirties these things have

more serious conse3uences but old postings cannot be called back. &hus one can think o#

)acebook as buying lo" and selling high. Koung people "ho revealed a lot o# data later on are

older and *oin a more valuable demographic #rom "hom is harder to get private in#ormation.

Cloud technologies also open many 3uestions in labor economics. >ainly these stem #rom large

scale data integration "ithin #irms that cloud in#rastructure make possible and the conse3uent

ability to interact "ith this data through clients ranging #rom desktop computers to mobile devices.

>any "orkers can no" "ork at home or on the road. &his reduces the capital needs o# companies

to provide o##ice space. %t is even possible #or companies to #urther reduce capital needs by

leveraging the use o# the employeesM computers cell'phones cars and other e3uipment. >any

*obs customer support #or example donMt need to be done in continuous blocks o# time but

"orkers are needed or can be used at all times o# day. &his capacity creates #lexibility that makes it

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more #easible #or the physically disabled and those "ith #amily responsibilities to participate in the

labor #orce. At least #or some types o# "ork the allocation o# tasks and monitoring o# productivity

can be largely automated by integrated business systems. &here is little need to have an employee

supervised at a central location by a physical person. &hus one might empirically study "hether "e

see greater "ork #orce participation especially by "omen and the disabled in certain industries as

cloud technologies get more ubi3uitous. )rom a theoretical standpoint one could explore "hat

kinds o# *obs lend themselves to this sort o# decentrali!ed system o# labor "hat kind o# incentive

and monitoring schemes "ill be e##icient and "hat types o# "orkers and businesses "ill be

attracted to these ne" models. &hese issues are driven by individual interaction "ith SaaS #or the

most part and thus are part o# the retail e##ects o# cloud computing.

A phenomenon that SaaS in the cloud greatly #acilitates is the collection o# #ree labor that many

people provide to all sorts o# activities through 4cro"d sourcing5. People "rite revie"s o# products

at Ama!on Cho"hound and &ripAdvisior post blogs and comments on blogs volunteer to be

moderators at sites they like add bookmarks to Leddit and Stumbleupon add content to

Wikipedia contribute code to inux and other open source so#t"are pro*ects. &hese contributions

are valuable and in many cases the cloud service providers moneti!e them in various "ays21. Why

and ho" people make these contributions "hether due to altruism a desire #or reputation pleasure

or other re"ard is not "ell explored in the context cloud computing. See Conley and ,ung ?;


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hand can provision servers to run at 9


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&he re"ards to success#ully implementing ES> pro*ects in the cloud are signi#icant though some

are hard to document. o"er operating costs due to better management o# inventory and human

resources better sales through use o# Customer Lelations >anagement ?CL>@ so#t"are and the

uses o# data mining and business intelligence systems and especially the #lexibility and scalability

that such cloud based solutions provide are most o#ten cited.

Clearly undertaking an ES> pro*ect is risky but also has signi#icant re"ards. &he degree o# risk

relates to 3uality o# management the company culture the level o# technical expertise and the

state o# existing data systems. &hus one can think o# this as a 4signaling game5 in "hich a company

undertakes a pro*ect to sho" that it has agile management a high level o# technical expertise or

ne" product lines or marketing ideas that "ill re3uire rapid expansion. Bene#its o# such signaling

might includeD causing less "ell'placed competitors to exit or sell'out at reduced prices inducing

venture capitalists to invest or raise the companyMs value at an %P. Since ES> is scalable it

makes rapid company expansion much easier and cheaper. &hus it is very similar to installing

excess but unused capacity in an oligopoly game. Since legacy systems and employees used to the

current "ay o# doing things are an impediment to success#ul deployment o# ES> ne" companies

"ho can build both systems and company culture #rom the ground up have an inherent advantage

over incumbents. &his suggests that there might be more churning o# companies as the cycle o#

technological advance speeds up or that incumbent companies "ould do "ell to continuously spin

o## ne" product and business lines to independent subsidiaries.

n the other hand "e can imagine that companies might #ollo" the strategy o# secretly undertaking

ES> pro*ects. Since these can take one to t"o years to complete a company can steal the march on

its competitors. %# the pro*ect is success#ul a company has as many as t"o years to en*oy production

"ith lo"er operating costs and cheap and easy expandability. An aggressive company might be able

to achieve a scale that "ould make it hard #or competitors to survive especially in sectors that en*oy

net"ork externalities. &hus "e might see innovation races "here the #irst across the post gets the

net"ork externality and thus monopoly. ne can also imagine companies heading #or bankruptcy

going all in on ES> pro*ects. %# they #ail they are out o# business any"ay i# they succeed the

company might be saved. We might see more ES> pro*ects in recessions than in boom years

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because the opportunity cost o# disruption to business and o# employee time are lo"er ceteris

paribus and because the advantages o# being able to cheaply scale up as the business cycle turns

positive again are greater.

ne big problem "ith ES> is that one si!e does not #it all. Hot only do di##erent industries have

di##erent needs but each company has its o"n management culture legacy systems solvency and

so on. Especially #or companies "ith older cultures and less technological savvy deciding ho" to

move #or"ard is di##icult. A common strategy is to look #or examples o# success#ul implementations

and copy these solutions. &his tends to create a second'mover advantage. &he #irst innovator takes

signi#icant risks but i# he is success#ul his competitors can simply copy him. &hus the advantages

to taking the risk are temporary. &his implies that innovation might be slo"er in sectors "here there

are no net"ork externalities or other #actors that "ould allo" a lo" cost #irm to rapidly expand its

market share. We might also expect that the most success#ul incumbent in a sector "ould have little

incentive to rock the boat and risk teaching his competitors ho" to innovate.

Companies starting big ES> pro*ects can proceed in a variety o# "ays. &he cheapest and #astest

"ay is to put together o##'the'shel# solutions and do as little customi!ation as possible. &his might

mean using SaaS such as Sales#orce.com #or CL> and Work#orce.com #or L #unctions and racle

Web Services #or data integration #or example. Putting these together "ith legacy systems may

re3uire building applications using PaaS so as to #ocus on the direct so#t"are needs rather than

"orrying about the details o# the in#rastructure. &he alternative is to build a customi!ed system #rom

scratch using %aaS "hich is more expensive time consuming and re3uires greater technical

kno"ho".

Aside #rom the obvious advantage that custom systems can be tailored to the exact needs o# the

company there are several reasons #or companies to choose this path. Perhaps the most important is

that 4lock'in5 is a big concern "hen you use SaaS and PaaS. So#t"are service providers each store

data in their o"n proprietary "ay2. Extracting such data and building a ne" so#t"are system

around it is an expensive and di##icult task. %n addition employees get used to the "ork #lo" and

17ock'in is a has been extensively studied in economics. See )arrell and ,lemperer ?;


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inter#aces o# these proprietary systems. &his also makes it costly to s"itch systems. %n a similar "ay

the greater the degree o# abstraction in the PaaS plat#orm T #or example special AP%Ms #or

interactions bet"een components like databases and email proprietary libraries o# code that the

users can build applications "ith the more di##icult it is to move to a ne" provider. &hus building

ES> on more abstracted layers o# IaaS makes users o# cloud services more vulnerable to price

increases. %n addition users are not in a good position to en#orce high service 3uality. >ore

speci#ically as technology and markets change cloud providers may choose not to continue to

support #unctions or #eatures o# their services that are highly valued by a subset o# customers.

$pdates to cloud systems may a##ect the "ay that they interact "ith the rest o# a companyMs ES>

solution and so crashes may result that re3uire time to #ix.

# course the possibilities o# lock'in makes such cobbled together systems less valuable to users

and so less pro#itable to providers. &hus "e might consider a game bet"een cloud providers in

"hich they choose ho" easy to make it #or customers to cleanly move to another provider. While

this "ould decrease their market po"er it "ould increase their value and thus the price they could

charge #or services. Especially i# service providers do not plan to take advantage o# lock'in by

suddenly s"itching #rom a lo" priceGcustomer'base building phase to a high priceGrent extraction

phase it "ould seem to be a dominant strategy to make it easy #or customers to leave. &hese same

considerations give an advantage to rapidly gro"ing companies and very large companies like

(oogle racle and Ama!on. Such companies are less likely to s"itch to a rent extraction phase

since this "ould deter #uture customers. (ro"ing companies get more bene#it #rom maintaining a

good customer reputation "hile large companies su##er more damage i# they try to extract rent #rom

a subset o# customers.

&he in#ormation technology revolution o# the 9


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companies are not saddled "ith legacy systems they can build integrated cloud applications #rom

the ground up. &hese #actors make it easier #or entrants to o##er entertainment and other services

that compete "ith incumbents like big television net"orks olly"ood studios and music

companies. Since scale increases both revenue and costs at the same time pro#itability can be

maintained at variety o# scales. &his in turn allo"s ne" companies to o##er services o# particular

interest to relatively small groups and also to scale up rapidly to take advantage o# net"ork

externalities i# they exist. &hus the value o# the intellectual property o# large incumbents is

reduced. &he democrati!ing e##ect o# the cloud on entrepreneurship means that more options

available that compete #or consumer interest "ith existing content. &he claims that piracy #acilitated

by P;P net"orks built on cloud in#rastructure is the primary cause o# the decline o# these

incumbents must there#ore be taken "ith a grain o# salt.

%ndustry lobbying to protect their position has led to the passage o# such measures as the Sonny

Bono Copyright &erm Extension Act and the /igital >illennium Copyright Act ?/>CA@ "hich

extend copyright terms and increase penalties #or piracy. &he *usti#ication is that artists "ill not

produce art i# it is not protected. %t "ould be interesting to see reliable estimates o# ho" much %P

"ould be lost i# copyright protection "ere reduced. >usicians make most o# their money #rom

per#ormances and touring plenty o# people "rite blogs and post their #iction and poetry Kou&ube

posts millions o# both user and company created video even textbooks and lectures are posted #or

#ree.

ne may argue that a large part o# the reason #or this is that many artists and other content creators

are not primarily motivated to produce by prospect o# monetary re"ard. Content creators ho"ever

are generally not in a position to spend much to distribute their "orks and thus access the non'

monetary bene#its o# reputation praise #ame etc. %n the old days the only "ay to distribute "orks

"as to publish books records tapes movies and so on. &hese "ere expensive both to produce and

to distribute and as such content creators had no alternative but to go through company gate"ays

to get their products to the public. With ne" technologies many cloud based it is cheap both to

produce and distribute content. &hus moneti!ing content might have been necessary to give

incentives to publishers to distribute ne" "orks but not get artists to create them. Ho" that artists

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can distribute their o"n "orks the policy *usti#ication #or extensive copyright protection may be

much "eaker. # course one si!e does not #it all. Big expensive productions like ma*or olly"ood

movies "ould not be made "ithout copyright protection. &his suggests that the solution might be to

make copyright costly ?perhaps 72aschler ?2=91@ and Chun and &homson ?2==;@ #or example. &his "ould re3uire

using either a loss #unction to aggregate the #ailures to meet claims in the three dimensions o# CP$

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band"idth and storage into one dimension or more interestingly to expand the bargaining "ith

claims approach to three dimensions. Similar extensions o# other axiomatic bargaining solutions

"ould #urther in#orm this problem. As "e mention above (hodsi et.al. ?;


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). Conclusion

Cloud computing is the next step in the on'going evolution o# %n#ormation &echnology. )rom a

technical standpoint very little that currently is done on cloud plat#orms could not have been done

"ith previously available technology. o"ever the cost'reductions rapid scalability and #lexibility o#

cloud solutions give them a revolutionary potential in many economic sectors. &hese #actors also

open many signi#icant economic 3uestions in industrial organi!ation labor economics and other

areas on both the theoretical and empirical side. /espite this the economics literature is exceeding

thin. >ost o# "ork is con#ined to #e" papers in cooperative and non'cooperative game theory. We

survey this literature as "ell as the more relevant parts o# the much larger computer science and

business literature on this topic. We argue that the technological categori!ations used in these #ields

?So#t"are Plat#orm and %n#rastructure as a Service@ do not correlate "ell to the economic impacts

cloud technologies. %nstead "e propose that it is more use#ul #or economists to think about

4"holesale5 and 4retail5 cloud applications. Wholesale cloud applications are primarily aimed at

businesses. &hey #acilitate large'scale data integration pro*ects rapid lo"'#ixed cost entry and

expansion o# ne" start'ups and the outsourcing o# many non'core aspects o# a given #irmMs

activities. Letail cloud applications on the other hand are primarily aimed at consumers. &hey

move applications and content o## personal computers to various types o# cloud plat#orms and make

both consumer produced and purchased content available in increasingly device and location

agnostic "ays. %n addition retail applications trans#orm the "ay the consumers sociali!e

communicate and consume entertainment. We conclude by suggesting several directions #or ne"

research.

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#ppendi*

#1. The +IST $efinition of Cloud Computing

Cloud computing is a model #or enabling convenient on'demand net"ork access to a shared pool o#

con#igurable computing resources ?e.g. net"orks servers storage applications and services@ that

can be rapidly provisioned and released "ith minimal management e##ort or service provider

interaction. &his cloud model promotes availability and is composed o# #ive essential characteristics

three service models and #our deployment models.

Essential Characteristics-

On-demand self-service.A consumer can unilaterally provision computing capabilities such

as server time and net"ork storage as needed automatically "ithout re3uiring

human interaction "ith each service6s provider.

Broad network access.Capabilities are available over the net"ork and accessed through

standard mechanisms that promote use by heterogeneous thin or thick client

plat#orms ?e.g.mobile phones laptops and P/As@.

Resource pooling. &he provider6s computing resources are pooled to serve multiple

consumers using a multi'tenant model "ith di##erent physical and virtual resources

dynamically assigned and reassigned according to consumer demand. &here is a

sense o# location independence in that the customer generally has no control or

kno"ledge over the exact location o# the provided resources but may be able to

speci#y location at a higher level o# abstraction ?e.g. country state or datacenter@.

Examples o# resources include storage processing memory net"ork band"idth

and virtual machines.

Rapid elasticity.Capabilities can be rapidly and elastically provisioned in some cases

automatically to 3uickly scale out and rapidly released to 3uickly scale in. &o the

consumer the capabilities available #or provisioning o#ten appear to be unlimited

and can be purchased in any 3uantity at any time.

Measured Service. Cloud systems automatically control and optimi!e resource use by

leveraging a metering capability

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Service >odels-

Cloud Software as a Service (SaaS.&he capability provided to the consumer is to use the

provider6s applications running on a cloud in#rastructure. &he applications are

accessible #rom various client devices through a thin client inter#ace such as a "eb

bro"ser ?e.g. "eb'based email@. &he consumer does not manage or control the

underlying cloud in#rastructure including net"ork servers operating systems

storage or even individual application capabilities "ith the possible exception o#

limited user'speci#ic application con#iguration settings.

Cloud !latform as a Service (!aaS.&he capability provided to the consumer is to deploy

onto the cloud in#rastructure consumer'created or ac3uired applications created

using programming languages and tools supported by the provider. &he consumer

does not manage or control the underlying cloud in#rastructure including net"ork

servers operating systems or storage but has control over the deployed

applications and possibly application hosting environment con#igurations.

Cloud "nfrastructure as a Service ("aaS.&he capability provided to the consumer is to

provision processing storage net"orks and other #undamental computing resources

"here the consumer is able to deploy and run arbitrary so#t"are "hich can include

operating systems and applications. &he consumer does not manage or control the

underlying cloud in#rastructure but has control over operating systems storage

deployed applications and possibly limited control o# select net"orking components

?e.g. host #ire"alls@.

/eployment >odels-

!rivate cloud.&he cloud in#rastructure is operated solely #or an organi!ation. %t may be

managed by the organi!ation or a third party and may exist on premise or o##

premise.

Community cloud.&he cloud in#rastructure is shared by several organi!ations and supports

a speci#ic community that has shared concerns ?e.g. mission security re3uirements

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policy and compliance considerations@. %t may be managed by the organi!ations or

a third party and may exist on premise or o## premise.

!u#lic cloud.&he cloud in#rastructure is made available to the general public or a large

industry group and is o"ned by an organi!ation selling cloud services.

$y#rid cloud.&he cloud in#rastructure is a composition o# t"o or more clouds ?private

community or public@ that remain uni3ue entities but are bound together by

standardi!ed or proprietary technology that enables data and application portability

?e.g. cloud bursting #or load'balancing bet"een clouds@.

#.2.1.Classification of Cloud Computing #pplications via ,ichman-s ,rameor/

ocus o# Adoption

%ndividual rgani!ational

Class o#

&echnology

&ype 2 ?lo" user

interdependencies and

kno"ledge barriers@

Personal adoption o#

simple SaaS

applications such as

email "ord

processing data

management

rgani!ational adoption

o# SaaS applications such

as CL> or enterprise

email.

&ype ; ?high user

interdependencies and

kno"ledge barriers@

Personal adoption o#

PaaS #or "eb

development and

%aaS #or hosting

rgani!ational adoption

o# PaaS #or application

development and %aaS

#or high volume

computing

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#.2.2.$eterminants of #doption via ,ichman-s ,rameor/

ocus o# Adoption

%ndividual rgani!ational

Class o#

&echnology

&ype 2 ?lo" user

interdependencies

and kno"ledge

barriers@

Classical di##usion variables- Classical di##usion variables

Perceived %nnovation Characteristics rgani!ational characteristics

Adopter Characteristics rgani!ational decision processes

%n#ormation Sources and Stage o# implementation

Communication Channels Competitive e##ects ?adopter industry@

Change Agents and pinion eaders Supply side #actors

Economic #actors ?price@

%& group characteristics

&ype ; ?high user

interdependenciesand kno"ledge

barriers@

Classical di##usion variables

>anagerial in#luences

Critical mass

Absorptive capacity Combination o# variables

%mplementation characteristics

%nstitutions lo"ering kno"ledge barriers

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&eferences

Altmann J. and ,linger S. ?;. ?2=91@ 4(ame theoretic analysis o# a bankruptcy problem #rom the

&almud5)ournal of &conomic heory Fol. :8 Ho. ; pp. 2=1';2:

Archak H and Sundarara*an A Nptimal /esign o# Cro"dsourcing ContestsN ?;


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Conley J. and ,ung ).'C. ?;odel5 )ournal of !u#lic &conomic heory Fol. 2; pp.

881T89=

Creeger >. ?;


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(atignon . and Lobertson &. S. ?2=9=@ 4&echnology /i##usion- An Empirical &est o#

Competitive E##ects.5)ournal of MarketingFol. 1: pp. :1'0=.

(hodsi A. aharia >. indman B. ,on"inski A. Shenker S. Stoica %. ?;endelson . ?2==odel o# %n#ormation Systems Spending

(ro"th.5 "nformation Systems Research Fol. 2 pp. ;:'0.

amilton J. ?;


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,ha*eh'osseini A. (reen"ood /. and Sommerville %. 5Cloud >igration- A Case Study o#

>igrating an Enterprise %& System to %aaS5 Cloud Computing "&&& 7rd"nternational Conference on

Cloud Computing pp. 01 ?;. Bretschneider $. and ,rcmar . ?;anagerial in#luence in the implementation o# ne"

technology.5 Management Science Fol. :0 pp. 2;1;'2;81.

in ( )u /. hu J. and /asmalchi (. ?;


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the economics of cloud computing 10-7-11

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