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Price Elasticity of the Enterprise ComputingResource Market

Qingye Jiang, Senior Member, IEEE, Young Choon Lee, and Albert Y. Zomaya, Fellow, IEEE

Abstract—Pricing is an important property of a product in that it significantly affects customer’s purchasing behavior. In thispaper, we study the supply and demand relationship of the global enterprise computing resource market, including the serversales business, server rental business, and public clouds. We find out that consumers in different market segments are not equallysensitive to prices changes, leading to significantly different purchasing behavior. The price elasticity is extremely inelastic forserver sales and server rental business, and is modestly elastic for public clouds. In the server sales business, we reveal that (a)the three biggest vendors - IBM, HP and DELL - dominate and have the market power over pricing in their respective fine-grainedmarket segment, and (b) price reduction is not an effective way to win market share when the price elasticity is inelastic.

Index Terms—cloud computing, enterprise computing resource market, microeconomics, price elasticity of demand

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1 INTRODUCTION

Pricing strategies are crucial business decisions in thelife cycle of a product, especially when there are multi-ple competitors in the market. In a perfect competitionmarket, there are multiple suppliers offering similarproducts, while no participant is powerful enoughto control the price of the product. To compete formarket share, suppliers often use price reduction inpromotional campaigns. In order to maintain competi-tiveness in the market, competitors are forced to lowertheir prices to match, resulting in a series of pricingwars. Many of such “matches” in price reduction arethe outcome of business intuition rather than rigorousreasoning, and often at the cost of losing revenue andprofit. It is not uncommon to see enterprises go outof business as the result of pricing wars.

The enterprise computing resource market seems tobe a perfect competition market with multiple players.Traditionally, enterprise can buy servers from hard-ware vendors, or rent servers from hosting serviceproviders. In recent years, Infrastructure-as-a-Service(IaaS) is gradually making its way into the enterprisecomputing resource market. Amazon Web Services(AWS) are widely recognized as examples of publiccloud services. Inspired by the dramatic growth ofAWS, there have quickly emerged many followersin the market, including Microsoft Windows Azureand Google Compute Engine. New service providersoften employ price reduction as a strategy to win cus-tomers from competitors. Existing service providersare forced to carry out further price reductions tomaintain competitiveness in the market. In this study,we are interested in the following three questions:

• Is the enterprise computing resource market aperfect competition market?

• In the enterprise computing resource market,how sensitive consumers are to price changes?

• Is price reduction an effective way to win in theenterprise computing resource market?

To answer these questions, we study the sup-ply and demand relationship of the global enter-prise computing resource market between 2006 and2013. The data source includes Gartner’s quarterlyreports on worldwide server shipments, as wellas the quarterly / annual reports from Amazonand Rackspace. (The references to all of the actualdata sources being used in this paper are listed inhttp://www.qyjohn.net/?p=3507.) We quantitativelymeasure the consumer’s sensitivity to price changesusing the concept of price elasticity of demand [1].We analyze the impact of different pricing strategieson the performance of the business, using the serversales business of IBM, HP and DELL as case studies.This research provides new insights into the microe-conomics characteristics of the enterprise computingresource market, and can help participants in variousmarket segments design better pricing strategies.

2 BACKGROUND AND RELATED WORK

2.1 The Enterprise Computing Resource MarketEnterprises have three major options to acquire com-puting resources. They can either buy new serversfrom hardware vendors, lease servers from hostingservice providers, or use public clouds. The differentways to acquire computing resources represent dif-ferent segments of the enterprise computing resourcemarket, namely server sales business, server rentalbusiness, and public clouds (Table 1).

Server Sales Business – The major suppliers in theserver sales business are hardware vendors, includingIBM, HP, and DELL. Consumers can order serversdirectly from the vendors, or through a third-partydistributor. The cost to acquire new servers is denotedas capital expense (CapEx), and is usually paid to the

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TABLE 1: Different Segments in the Enterprise Computing Resource Market

Market segment Server Sales Business Server Rental Business Public CloudsMajor Vendors IBM, HP, DELL, Fujitsu, Oracle Rackspace, OVH, SoftLayer Amazon Web Services, Google

Compute Engine, MicrosoftWindows Azure

Acquisition Time weeks days minutesExpense Type CapEx OpEx OpExUnit Cost 2,000–50,000 USD

one-time payment100–2000 USDmonthly charge

0.02–6.82 USDhourly charge

Ownership consumer supplier supplierOperations consumer supplier supplierLife Cycle 3 to 5 years 1 to 36 months hourlyContracts optional service contract with

service level agreementlong term contract with servicelevel agreement

service level agreement

Market Size (#servers) 10 million annually or 40 mil-lion in total

2 million in total 1 million in total

Market Share 93% 5% 2%

supplier in a single payment before or after hardwaredelivery. The acquisition time usually falls withinweeks for low-end servers up to months for high-end servers. When the servers are delivered to theconsumer, their ownership is also transferred to theconsumer. The life cycle of servers is between threeand five years.

Server Rental Business – The major suppliersin the server rental business are hosting serviceproviders, including Rackspace, OVH, and SoftLayer.Consumers usually establish a server rental contractdirectly with the service provider. According to thecontract, the consumer pays a recurring monthly feeto maintain access to designated computing resourcethrough network. Such cost is denoted as operationsexpense (OpEx). The time needed to acquire access tocomputing resource after signing the contract usuallyfalls within days. The server rental contract does notchange the ownership of computing resource, butsome service providers choose to give the ownershipto the consumer after the successful execution of along contract (e.g., 3 years).

Public Clouds – Major cloud providers are AmazonWeb Services, Google Compute Engine, and MicrosoftWindows Azure. Consumers can directly request for,and gain instant access (within minutes) to, com-puting resource through web portals or applicationprogramming interfaces (API). In a manner similar topublic utilities such as gas and electricity, consumersonly pay for the amount of computing resource theyuse for the duration of their usage. The ownershipof the physical computing resource always belongsto the service providers. Consumers’ responsibility isessentially confined to the pay-as-you-go expense.

2.2 Related Work

There exists an abundance of literature on the pricingstrategy in the computing resource market. Most ofthem focus on maximizing revenue / profit for a par-ticular vendor or service provider within its own cus-tomer base. These researches usually assume a certainresource consumption pattern from the customers,

without considering the impact from its competitorsand the overall business environment. Only very fewresearchers studied the supply-demand relationshipin the computing resource market, as presented below.

Chow [2] studies the technological change and thedemand for computers with the price and quantityof computers in the United States between 1955 and1965. Tam et al. [3] extend Chow’s work with an-nual computer hardware spending data in the UnitedStates between 1955 and 1984. Their findings indicatethat the price elasticity of demand for computer hard-ware is inelastic, but the degree of elasticity changesover time. The data sources being used in [2] and[3] are extremely old, while technology advancementshave dramatically changed the horizon of the com-puting resource market in general. On the technologyside, expensive mainframes are being replaced bycommodity x86 64 hardware. On the business side,server rental and public clouds are gaining increasinglevel of popularity among enterprises. Such changesin the demand for computing resource, as well as therelationship between supply and demand, are largelyunder-explored.

Stavins [4] studies the price elasticity of demand forthe personal computer market with annual personalcomputer sales data in the United States between 1976and 1988. Goolsbee [5] compares the price sensitivityon computer purchases from online stores versusin retail stores. Both [4] and [5] focus on personalcomputers instead of enterprise computing resources.

Rogers et al. [6] use Game Theory to analyze the ef-fect of price reduction on the public cloud market. Theauthors concluded that there is no Nash equilibriumin this game, resulting in a constant stalemate. Cloudservice providers practice price reductions to gain me-dia attention and up-sell value-added services, at thecost of reduced margin from commoditized servicessuch as storage and compute. The problem with thisapproach is that unless the profit from the value-added services exceeds the lost from down pricing,lowering price always results in reduced profit andcan threaten the survival of a business.

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hipm

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illion  Units)

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(a) The volume of server shipments and revenues on a globalscale, based on the data in Gartner’s quarterly reports on world-wide server shipments.

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y  =  -­‐0.001x  +  14.363  R²  =  0.34181  

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5000   5400   5800   6200   6600  

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illion  un

its)

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(b) The demand curve of the global server sales business, calcu-lated from the data in Gartner’s quarterly reports on worldwideserver shipments.

Fig. 1: Server Sales Business

3 PRICE ELASTICITY OF DEMAND

In this section, we first introduce the microeconomicsconcept of price elasticity of demand; describe its ap-plication to the enterprise computing resource market.Then, we present our analysis on price elasticity of thethree market segments.

Price elasticity of demand is defined by the percent-age change in demand over the percentage change inprice. More formally,

E =

(dQQ

)(dPP

) , (1)

where E represents the price elasticity of demand,P represents the price and Q represents the quan-tity sold, while dP and dQ represent the percentagechanges (in absolute values) in price and demand.When E is greater than 1, the percentage changein demand is greater than the percentage change inprice, which is classified as elastic demand. When Eis smaller than 1, the percentage change in demandis smaller than the percentage change in price, whichis classified as inelastic demand. When E equals to1, the percentage change in demand equals to thepercentage change in price, which is classified asunitary demand. When demand is inelastic, a rise inprice leads to a rise in revenue. When demand iselastic, a fall in price leads to a rise in revenue.

In general, there are two factors contributing to theprice elasticity of demand of a product. The first factoris the availability of close substitutes. If a buyer hasgreater choice among close substitutes in consump-tion, the price elasticity of demand is greater. Thesecond factor is the proportion of a buyer’s budgetthat is devoted to a product. The larger the propor-tion of budget, the more responsive is the quantitydemanded to price changes, and the price elasticityof demand is greater.

To identify the price elasticity of demand in thisstudy, we carry out linear regression with history

price and demand data. With such a technique, we canexpress the relationship between price and demand as

Q = b0 + b1P. (2)

The slope of a linear curve b1 can be expressed as

b1 =dQ

dP. (3)

ThereforeE = −b1

P

Q. (4)

In the enterprise computing resource market, ven-dors and service providers offer computation units(such as servers and EC2 instances) with differentcapacities. In this study, we carry out price elasticityanalysis from a statistics point of view. That is, weignore the difference in the actual computation units,but focus on the total amount and the average priceof the computation units.

3.1 Price Elasticity of Server Sales BusinessFigure 1 shows the volume of server shipments andrevenues, as well as the demand curve for the serversales business. The significant drop in both servershipments and revenue in 2009 was the result of theglobal economic recession. Using Equation 4, the priceelasticity of demand for the worldwide server marketis calculated as 0.51 in 2013, i.e., extremely inelastic.

For the majority of enterprise consumers, whenthey need computing resource the first option thatcomes to mind is purchasing some server hardware.Other forms of computing resource are not close sub-stitutes for various reasons—renting server hardwarefrom hosting service providers might pose securityproblems, while public clouds have not been widelyadopted. From a budget perspective, server hardwarepurchases, as fixed-asset investments, are usually de-cided at the organization level, and are only a smallproportion of an organization’s annual budget. Thelack of close substitutes, and the small proportion of

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f  Servers

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illions)

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Total  Revenue  Dedicated  Hos;ng  Revenue  Public  Cloud  Revenue  Total  Servers  

(a) Total servers and revenues, based on thedata in Rackspace’s annual reports to SEC.

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r  Server  (USD

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ber  o

f  Servers

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Dedicated  Hos5ng  Servers  Dedicated  Hos5ng  Price  

(b) Annual server growth and averageserver price, calculated from the data inRackspace’s annual reports to SEC.

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y  =  -­‐0.4625x  +  13665  R²  =  0.20149  

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ts

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(c) The demand curve for Rackspace’sserver rental business, calculated from thedata in Rackspace’s annual reports to SEC.

Fig. 2: Server Rental Business: Rackspace

budget, combined contribute to the inelastic demandin server hardware. In other words, organizationsinvest on server hardware based on their businessplans, regardless of price changes in the market.

A fundamental characteristic of inelastic demand isthat the percentage change in demand is less thanthe percentage change in price. In this case, pricereduction might produce more demand, but will alsoresult in revenue lost. In a perfectly monopoly indus-try, the vendor would always do the opposite, i.e.,raising the price and harvesting more revenue. Theproblem is, the competition in the server hardwaremarket is extremely intensive. Leading server vendorsare cutting prices to win market share. Regardless ofthe steady growth in worldwide server shipments,the overall revenue from server sales seems to bedecreasing, as shown in Figure 1a. This results indecreasing margin from server sales, and makes itdifficult for server vendors to survive.

3.2 Price Elasticity of Server Rental BusinessFigure 2a shows Rackspace’s number of servers andrevenue, as well as the revenue break down for dedi-cated hosting (server rental business) and public cloudbetween 2006 and 2013. Figure 2b shows the estimatednumber of servers and the estimated average revenueper server for Rackspace’s dedicated hosting business.It should be noted that the number of worldwideserver shipments discussed in Section 3.1 representsthe newly added computing resource every year. Fur-thermore, the life cycle of server hardware is usuallymore than one year, and in most cases falls within therange of three to five years. The number of servers forRackspace’s dedicated hosting business represents theaccumulated computing resource over the past years.To facilitate a fair comparison, we calculate the annualserver increments for Rackspace’s dedicated hostingbusiness, and use that number to compare with theserver sales business. Figure 2c is a supply and de-mand plot with the average revenue per server onthe horizontal axis and the annual server incrementson the vertical axis. The calculated price elasticity ofdemand for Rackspace’s dedicated hosting business is0.70 in 2013, which is modestly inelastic.

Enterprise consumers choose server rental overbuying server hardware for various reasons. For smalland medium businesses, the cost and effort to buildand maintain a small data center with the appro-priate networking capacity is overwhelming. Thereare many server hosting service providers available,including international and local ones. An enterpriseusually needs to go through a comparison and selec-tion process before deciding which one to work with.Once a decision has been made, a long-term contractwith the selected service provider will be setup. Thelength of such contract is usually measured by monthsor even years, depending on the consumer’s businessplan. Such a long-term contract effectively eliminatesthe possibility of migrating to alternative optionswhen there is a price change in the market. Thus,enterprise consumers invest on server rental basedon business plans, which are usually not sensitiveto price changes. This is why the price elasticity ofdemand for the server rental business is inelastic.

3.3 Price Elasticity of Public CloudsFor the public cloud business, we use the estimatednumber of servers for AWS EC2 derived by a numberof cloud computing professions in the industry as thefoundation to carry out our analysis. AWS is currentlythe largest public cloud service provider. It is com-monly recognized that the size of AWS is far largerthan all the competitors combined. Figure 3a showsthe estimated number of servers for AWS EC2 on aglobal scale. Figure 3b is a supply and demand plotshowing the estimated amount of annual EC2 serverincrements and the price of m1.small instance in US-East-1 region between 2008 and 2013, with a linearregression trend line. The calculated price elasticityof demand is 1.20 in 2013, which is modestly elastic.

Many consumers consider EC2 as an alternative totraditional server rental services and virtual privateserver (VPS) services, while ignoring those advancedfeatures such as CloudWatch, AutoScaling, and ElasticLoad Balancing. Furthermore, spending on EC2, as anoperations expense, is usually decided at the projectlevel, and is usually a big portion of a project’sbudget. The availability of close substitutes, and the

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2006   2007   2008   2009   2010   2011   2012   2013  

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ber  o

f  Servers  

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(a) The estimated number of servers for the AWS EC2 service,estimated and adjusted based on Amazon’s quarterly earningreport in the second quarter of 2015.

2007  2008  

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y  =  -­‐6411.8x  +  625  R²  =  0.81884  

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al  EC2

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ts  (tho

usan

ds)

m1.small  instance  price  in  US-­‐East-­‐1  region

(b) The demand curve for AWS EC2 in the us-east-1 region,calculated based on EC2 pricing history and the estimatednumber of EC2 servers.

Fig. 3: Public Clouds - AWS EC2

big proportion of budget, combined contribute to theelastic demand for EC2. In other words, enterpriseconsumers spend on EC2 based on actual businessneeds rather than business plan, and are sensitive toprice changes because of budget limitations.

In general, inelastic demand is closely related toplanned buying, while elastic demand is closelyrelated to unplanned buying. With the fixed-assetmodel, when enterprise consumers need computingresource, the budget for the needed computing re-source must first be secured before making an order.Then it takes weeks, if not months, for the neededcomputing resource to be delivered and deployed.With the utility model, consumers have instant accessto computing resource with no capital expense, zerolead-time, at affordable prices. In other words, theutility model makes it possible for customers to reactto price changes in a timely manner.

4 CASE STUDIES

In this section, we analyze the impact of differentpricing strategies on the performance of the busi-ness, using IBM, HP and DELL as case studies; theyrepresent over 75% of the market share. Other ven-dors including Sun (later acquired by Oracle), Fujitsu,Cisco, Lenovo, etc. combined only represent 25% ofthe market share.

Figure 4a shows the annual server shipments andthe average prices for IBM, HP and DELL. The sig-nificant gaps in the average prices indicate that thesevendors are not competing in the same fine-grainedmarket segment. IBM dominates the high-end servermarket, HP occupies the mid-range server market,while DELL dominates the low-end server market.

For IBM, the average price does not change muchbetween 2006 and 2010. The slight decrease in itsserver shipments is an indicator that the market pref-erence is gradually shifting from high-end servers tomid-range and low-end servers. Between 2010 and2012, there is an 11% increase in IBM’s average price,

with a corresponding 8% decrease in server ship-ments. The price elasticity of demand for IBM serversis 0.73, which is modestly inelastic. For HP, there isan 18% percent decrease in average price between2006 and 2013, with a corresponding 13% increase inserver shipments. The price elasticity of demand forHP servers is 0.72, which is very similar to IBM. ForDELL, there is a 28% increase in average price, witha corresponding 15% increase in server shipments.The simultaneous increase in price and shipmentsindicates that the price elasticity of demand for DELLservers is extremely inelastic - the demand increasesregardless of price increases.

The in-elasticity in demand, plus the significantgaps in the average prices, suggest that enterpriseconsumers do not perceive IBM, HP and DELL serversas close substitutes for each other. These vendorsdominate in their own fine-grained market segment,and have the market power over pricing. IBM re-sponded to the inelastic demand with up-pricing.However, the market preference was shifting fromhigh-end servers to mid-range and low-end servers,resulting in the decrease in revenue. HP responded tothe inelastic demand with down-pricing, resulting inincrease in shipments but decrease in revenue. DELLresponded to the inelastic demand with up-pricing.Coupled with the shift in market preference, DELLachieves a simultaneous increase in server shipmentsand revenue. The situation for both HP and DELLcan be directly derived from Equation (1) - when thedemand is elastic, a fall in price leads to a fall inrevenue, a rise in price leads to a rise in revenue.Therefore, the HP and DELL cases can be consideredas classical examples of how the principle of priceelasticity works in microeconomics.

Figure 4c shows the market share for IBM, HPand DELL, as defined by the percentage of servershipments. As shown in this figure, price reductionis not an effective way to win market share.

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(a) Server Shipments and Average Prices

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HP  IBM  DELL  

(c) Market Share

Fig. 4: Case Studies - HP, IBM and DELL. All the data presented were calculated from the data in Gartner’squarterly reports on worldwide server shipments.

5 CONCLUSIONS

In this paper, we study the price elasticity of de-mand of the global enterprise computing resourcemarket, including the server sales business, serverrental business, and public clouds. We reveal thatfrom a microeconomics point of view public cloudsare fundamentally different from traditional serversales business and server rental business. The differ-ence in business model leads a different purchasingbehavior, which is reflected by the difference in priceelasticity of demand. The price elasticity of demandis extremely inelastic for both server sales businessand server rental business. Since consumers are notsensitive to price changes, a rise in price leads to arise in revenue, while a fall in price results in a fallin revenue. The price elasticity of demand is modestlyelastic for public clouds. Since consumers are sensitiveto price changes, a fall in price results in a rise inrevenue. As such, it is vital for vendors and serviceproviders to carefully evaluate the price elasticity ofthe product or service they offer when developingpricing strategies.

In our case studies with IBM, HP and DELL, thesignificant gaps in their average unit prices indicatethat the hardware vendors are not competing in thesame fine-grained market segment. IBM dominatesthe high-end server market; HP occupies the mid-range server market; while DELL dominates the low-end server market. These vendors have the marketpower over pricing in their respective fine-grain mar-ket segment. As shown in the historical market sharedata, price reduction is not an effective way to winmarket share.

REFERENCES

[1] A. Marshall, Principles of Economics, vol. III, ch. IV. Digireads.com Publishing, 2004.

[2] G. C. Chow, “Technological change and the demand for com-puters,” The American Economic Review, pp. 1117–1130, 1967.

[3] K. Y. Tam and K. L. Hui, “Price elasticity and the growth ofcomputer spending,” IEEE Transactions on Engineering Manage-ment, vol. 46, no. 2, pp. 190–200, 1999.

[4] J. Stavins, “Estimating demand elasticities in a differentiatedproduct industry: the personal computer market,” Journal ofEconomics and Business, vol. 49, no. 4, pp. 347–367, 1997.

[5] A. Goolsbee, “Competition in the computer industry: Onlineversus retail,” The Journal of Industrial Economics, vol. 49, no. 4,pp. 487–499, 2001.

[6] O. Rogers and A. Sadowski, “Prices: to cut or not to cut? thatis the question facing cloud providers.” http://tinyurl.com/ozjeozh, 2014.

Qingye Jiang received the B. Eng. Degreein civil engineering from Tsinghua Univer-sity, China, in 1999 and the M.S. degreein civil engineering from University of Illinoisat Urbana-Champaign, USA, in 2001. He iscurrently pursuing the Master of Philosophydegree in the School of Information Tech-nologies at The University of Sydney. He isa senior member of IEEE.

Young Choon Lee is currently an AustralianResearch Council DECRA Fellow and a lec-turer in the Department of Computing, Mac-quarie University. His current research inter-ests are in the areas of distributed systemsand high performance computing.

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Albert Y. Zomaya is the Chair Professor ofHigh Performance Computing & Networkingin the School of Information Technologies,The University of Sydney. He is also the Di-rector of the Centre for Distributed and HighPerformance Computing which was estab-lished in late 2009. Professor Zomaya pub-lished more than 500 scientific papers andarticles and is author, co-author or editor ofmore than 20 books. He served as the Editorin Chief of the IEEE Transactions on Com-

puters (2011-2014). Also, Professor Zomaya serves as an associateeditor for 22 leading journals.

Professor Zomaya is the recipient of the IEEE Technical Commit-tee on Parallel Processing Outstanding Service Award (2011), theIEEE Technical Committee on Scalable Computing Medal for Excel-lence in Scalable Computing (2011), and the IEEE Computer SocietyTechnical Achievement Award (2014). He is a Chartered Engineer,a Fellow of AAAS, IEEE, IET (UK). Professor Zomayas researchinterests are in the areas of parallel and distributed computing andcomplex systems.