A Hedonic Price Index for Cloud Computing Services
Persefoni Mitropoulou, Evangelia Filiopoulou, Stavroula Tsaroucha,
Christos Michalakelis and
Mara Nikolaidou
Department of Informatics and Telematics, Harokopio University of Athens, 9 Omirou str, Tavros, Athens, Greece
Keywords: Cloud Computing, Infrastructure-as-a-Service, Pricing Models, Hedonic Price Indices.
Abstract: Cloud computing is an innovative business model, being developed at a fast pace during the last years, offering
many operational and economic benefits to both the demand and the supply side of the ICT market.
Infrastructure as a Service (IaaS), which includes control of fundamental computing resources, is expected to
be the fastest growing model of public cloud computing. Due to the existence of several IaaS cloud providers,
there is increased competition among cloud companies, which develop different pricing models in order to
meet the market demand. As a consequence, prices for cloud services are a result of a multidimensional
function, shaped by the service’s characteristics. The development of a suitable pricing method, based on an
appropriate price index able to capture the market dynamics, is an obvious necessity. The aim of this paper is
the construction of such a price index, for the IaaS model, using data from a wide range of cloud providers
and a large number of price bundles. The hedonic pricing method is used to decompose cloud computing
services into their constituent characteristics, obtaining estimates of the contributory value of each
resource. According to the results, RAM size, CPU power and subscription turned out to be the most
influential factors that shape IaaS pricing.
1 INTRODUCTION
During the recent years, cloud computing has gained
enormous popularity across the business world as
there is an increased demand for a new business
model that can help companies respond faster and
cheaper to their constituents’ needs, not only in
Europe but also worldwide. Its systems and services
are being improved and developed at a fast pace,
offering operational benefits to both the providers and
the consumers of the technology, contributing
substantially at the same time to the creation of a
competitive environment in the global market (Etro
2009).
Therefore, cloud computing is considered to be a
really powerful technological tool and an innovative
business model, composed of three service models:
Infrastructure as a Service (IaaS), which includes
control of fundamental computing resources, such as
memory, computing power and storage capacity;
Platform as a Service (PaaS) that provides control
over the deployed applications and possibly
configuration settings for developer platforms and
Software as a Service (SaaS), which includes the use
of software services accessed through a web browser
or a program interface (Mell and Grance 2011). In
addition to these, cloud computing has also four
deployment models: Private cloud, provisioned for
exclusive use by a single organization; Community
cloud, used exclusively by a specific community of
consumers from organizations that have shared
concerns; Public cloud, open for use by the general
public and Hybrid cloud, which is a composition of
two or more distinct cloud infrastructures (Mell and
Grance 2011).
Concerning the above models, public cloud
computing receives more attention and the IaaS
model gains increased adoption across the business
world (Anderson et al. 2013). More specifically, IaaS,
which is a foundational cloud delivery service and the
most straightforward of the cloud models, provides
flexibility and can be a very good solution for
companies needing computing resources in the form
of virtualized operating systems, workload
management software, hardware, networking, and
storage services (Hurwitz et al. 2012). Computational
power and operating systems are delivered to the
customers in an “on-demand” approach. An
enterprise that migrates its IT system to IaaS may hire
the required resources as needed, instead of buying
499
Mitropoulou P., FIliopoulou E., Tsaroucha S., Michalakelis C. and Nikolaidou M..
A Hedonic Price Index for Cloud Computing Services.
DOI: 10.5220/0005410604990505
In Proceedings of the 5th International Conference on Cloud Computing and Services Science (CLOSER-2015), pages 499-505
ISBN: 978-989-758-104-5
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
them (Mell and Grance 2011). According to Gartner’s
latest report about the public cloud (Anderson et al.
2013), it is expected that IaaS will be the fastest
growing area of public cloud computing achieving a
compound annual growth rate (CAGR) of 41.3%
through 2016, as Figure 1 illustrates.
Figure 1: Public Cloud Services - five year (2011-2016)
CAGRs (%), by model. Source: Gartner (February 2013).
Currently, there are several public cloud providers in
the cloud computing market that provide similar
services to customers. A client's choice of which
cloud company would host his infrastructure-related
services in the long-term depends jointly on the price
it has, the Quality-of-Service (QoS) guarantees it
offers to its customers and the satisfaction of the
advertised guarantees (Vinu Prasad et al. , Siham et
al. 2012). Cloud computing works, in general, on a
“pay-as-you-go” basis, giving the option to the user
to pay for what they use; meaning that the customer
is charged for each computing resource (e.g. RAM,
CPU, storage) separately, usually per unit-hour
(Martens et al. 2012). On the other hand, it is true that
the battle for a dominant market share grows the
competition among cloud companies and leads to the
development of new pricing schemes in order to meet
the market demand. As a result, several packages of
different resources are offered in attractive tariffs and
they are continually fitted to the changing preferences
and increasing needs of customers. Furthermore,
there is an option somewhere in the middle, as there
are some cloud providers who offer predefined sets of
some resources, usually memory and CPU are
bundled, whereas users can select simultaneously on
their own some other computing characteristics, such
as storage size (Martens et al. 2012, Andra 2013).
Finding the right combination of the available
resources is critical for a business to achieve the best
value when creating its own cloud bundle of services.
As any bundle consists of various characteristics,
valued differently by each consumer, there are a
number of questions that arise in the cloud computing
context, such as:
How customers’ choices and preferences for
IaaS affect the pricing of the corresponding
resources.
Which characteristics are truly independent
from one another, while at the same time
being the most important for shaping the
pricing of IaaS.
Towards this direction, multiple cloud providers
have already developed cost estimator tools, which
are used to help customers evaluate IaaS services,
deciding the most suitable for their needs. In addition,
another similar approach is considered to be a broker
model that acts as an intermediary between
consumers and providers. Both of these methods are
mainly based on asking users some questions about
the amount of computational power, memory, storage
requirements, data transfer and they subsequently
offer a monthly estimate for price, for the selected
bundle (Hurwitz et al. 2012) or the most cost-efficient
tariff option among many different providers in the
case of a broker (Jörg et al. 2014). However, none of
these tools is capable of identifying the cheapest
cloud hosting provider due to the fact that this is a
choice that depends exclusively on the clients’
computing needs and, furthermore, price is
considered to be a multidimensional function, where
many factors should be taken into account (Siham et
al. 2012).
Into that context, this paper tries to describe the
current mechanism of pricing and reveal the
dynamics which drive the pricing of cloud computing
services. For this, it provides an empirical analysis of
IaaS pricing, by constructing a price index based on a
hedonic pricing method.
The remainder of the paper is structured as
follows: Section 2 presents a brief literature review of
the previous work regarding the pricing methods of
cloud computing, while in Section 3 there is a
theoretical approach of hedonic functions analysis
and price indices. The empirical study and the
evaluation of hedonic regression model together with
discussion are described in Section 4, and finally,
Section 5 concludes, providing directions for future
research.
2 PRICING MODELS OF CLOUD
COMPUTING
In a cloud computing environment, an Infrastructure-
as-a-service demand is considered as an access to the
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system resources, such as CPU, memory and disk.
Resource allocation is a really challenging task
because of the different pricing models the providers
use. In general, cloud hosting providers must offer a
good pricing model that does not bring any loss to
neither the provider nor the consumer and maximizes
the social surplus of the corresponding market. It is
not usually easy to reach a point where both sides
agree with the price set. A user is always willing to
pay a lower price for the resources requested, whereas
a provider should not want to go beyond the lowest
price that gives him no profit (Andra 2013).
Currently, there are many cloud vendors that
follow a fixed pricing strategy, meaning that
customers pay for what they use but not for what the
cloud services value (Vinu Prasad et al.). One of the
most common pricing schemes of this category is the
“pay-as-you-go” model, which charges the users for
just the services they need, paying only for the
required computing instances and just for the time
they use them. In the case they need more resources,
they simply request them from the provider
(Grossman 2009). Another fixed pricing model is
based on subscription, setting a standard price for the
required resources according to a longer period of
subscription (Al-Roomi et al. 2013). However, these
static approaches of pricing have some limitations,
due to the fact that they reserve computational
resources in advance and it is often hard to satisfy
both the cloud vendors’ and cloud users’
requirements.
Hence, dynamic pricing is usually the key solution
to the above problems. It is a method in which the
price for each bundle of resources is based on a
number of factors, such as availability, time, the
service features and according to the forces of the
demand and supply of a real-time market (Andra
2013, Al-Roomi et al. 2013). Mihailescu and Teo
(2010) proposed such an auction-based pricing
strategy for federated clouds, in which resources are
shared among many cloud service providers.
Rohitratana and Altmann (2012) used an agent-based
simulation of four different pricing models that
indicated that the Demand-Driven (DD) pricing
scheme was the best approach in ideal cases. Li et al
(2011) introduced a real-time pricing algorithm for
cloud computing resources. It analyzed some history
utilization data and it found the final price that was
mostly beneficial for the provider because it reduced
its costs, allowing at the same time resources to be
used more effectively. Moreover, there are some
pricing methods that are mostly driven by
competitors’ prices (Rohitratana and Altmann 2010)
and some others based on the amount of money users
are ready to pay (Ruiz-Agundez et al. 2011). All of
the above pricing methods are fair enough for the
customers’ side.
Both of the above categories of pricing models,
especially the dynamic one, take into consideration
some of the service’s most important characteristics.
The construction of price indices is generally used for
this purpose, seeking to estimate the extent to which
each characteristic affects the total price of a service
bundle. Among the most common and widely used
approaches is the hedonic pricing method (Triplett
2004). It was primarily developed seeking to capture
the effect of environmental and housing attributes in
the context of the housing market (Goodman 1978)
and to adjust for quality change for automobiles
(Griliches 1961). In (Chanel et al. 1996) a price index
for paintings, based on regressions using the full set
of sales, was constructed and the idea that goods are
valued for their utility-bearing characteristics can be
found in (Rosen 1974). As far as information and
communication technologies are concerned, hedonic
price indices have been widely used for personal
computers (Pakes 2002, Berndt et al. 1995) taking
quality changes into account and for microcomputers
and printers using evidence from France (Moreau
1996). A hedonic pricing approach has been also
proposed in (Jörg et al. 2014) to estimate price
evolution of telecommunication services based on
data across Europe and in (Siham et al. 2012) the
hedonic pricing method was applied to make cloud
pricing plans more transparent.
3 HEDONIC PRICE INDICES
Hedonic methods refer to regression models in which
a product’s (or a service’s) prices are related to
product characteristics and the observed price of a
product (service) is considered as a function of these
characteristics. The main assumption hedonic
methods are based on, is that a service is a bundle of
characteristics and that consumers just buy bundles of
product characteristics instead of the product itself. A
hedonic method decomposes the item being
researched into its constituent characteristics, and
obtains estimates of the contributory value of each
characteristic, provided that the composite good can
be reduced to its constituent parts and that the market
values those constituent parts.
According to the definition of (Triplett 2004): “A
hedonic price index is any price index that makes use
of a hedonic function. A hedonic function is a relation
between the prices of different varieties of a product,
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501
such as the various models of personal computers,
and the quantities of characteristics in them”.
These methods can be used to construct a quality-
adjusted price index of a service. An informative
overview of the hedonic methods and how they are
constructed can be found in (Berndt 1991, Triplett
2004). Moreover, as shown in (Rosen 1974)
consumer chooses from a large number of product
varieties without having the ability to influence
prices. As a consequence, consumers maximize
utility and producers maximize profits. In hedonic
studies it is possible to adjust the price of a service for
its quality not quantity. All of them are based on some
estimated coefficients that are inflicted on the
characteristics of the products in two periods; m and
m + 1. It is possible to estimate the coefficients
separately, for each evaluate period of time, or
consider the observations of two or all periods
together and estimate a common set of coefficients,
seeking to reveal the general trend.
The advantage of this method is that the necessary
calculations are easy to implement. Hedonic methods
are also very fast to apply but the disadvantage is that
index price can change even if no new products exist,
or if all prices remain the same. Among the strengths
of a hedonic pricing method are that it can be used to
estimate values based on actual choices and its
versatility, since it can be adapted to consider several
possible interactions between market goods and
environmental quality.
The hedonic price indices are commonly used as
approximations to find how much money a consumer
would need in period m+1 relatively to the amount of
money required in period m, keeping the same level
of utility. The solution to this problem is to determine
the consumer’s profile and his reaction to a varied and
fast-changing supply of products. The main problem
towards this direction is that each consumer has
potentially different needs and requirements No
matter what profile is decided, it will be a hypothesis
and an assumption that will correspond to a specific
model. In addition to this, a consumer’s desire is not
stable, something quite reasonable since there is a
great offer as technology becomes cheaper and more
attractive.
A hedonic function

f
X
, which relates a
number of the product’s characteristics with the
corresponding price as:

ii
P
fX
(1)
where P
i is the price of a variety (or a model) i of the
considered product and X
i is a vector of
characteristics associated with the specific variety.
The hedonic function is then used, for a number of
different characteristics among the varieties of the
product and the price index is calculated. As soon as
the characteristics to be considered are determined
then, for N varieties of the product (or service) the
following equations must be evaluated:
011 22
··,
1, . . . ,
iiii
P
bbX bX e
iN

(2)
where
b
i
are the regression coefficients that have to
be estimated and e
i
is the regression residual of the
assumed functional form. The regression coefficients
value the characteristics and they are often called
implicit prices, because they indicate the prices
charged and paid for an increment of one unit of the
corresponding characteristic. Implicit prices are much
like other prices, they are influenced by demand and
by supply. In some cases the natural logarithm (ln) of
the price is considered, instead of the actual value.
Furthermore, the functional form of the index can be
nonlinear.
In the case that the prices span between two (or
more) periods of time m and m + 1, the equations to
be evaluated are
011 22
1011 22 1
·· ,
1, . . . ,
·· ,
1, . . . ,
im i i im
im i i im
PbbXbXe
iN
PbbXbXe
iN


(3)
In the context of this work, the vector of
characteristics Xi, corresponds to the configuration of
the IaaS cloud services that affects the price,
including characteristics such as RAM size, number
of CPUs, memory size, bandwidth etc. The
description of these parameters is given in the next
section.
The importance of a price index is that it can be
used to determine suggested prices for combinations
of the characteristics that were not included, or they
were not available, when the index was constructed.
4 PRICE INDEX
CONSTRUCTION
This section describes the empirical study design,
contains the evaluation of the hedonic price index
methodology for cloud computing services, the
construction of a corresponding index and discussion
of the results.
The price index is constructed for the IaaS cloud
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computing, the most straightforward cloud service.
Data collection was based on Cloudorado
(http://www.cloudorado.com), a price comparison
service of cloud computing providers. Cloudorado is
also a price calculator for multiple cloud hosting
providers, since the comparison is performed by
calculating price for individually set server needs. It
currently focuses on providing pricing bundles for
IaaS providers. The number of the collected price
bundles is 2354, by 25 providers, shown in Table 1.
Table 1: Cloud IaaS providers.
Amazon
atlantic.net
Bitrefinery
CloudSigma
Dimensiondata
eApps
ecloud24
Elastichosts
Exoscale
GIGENET
GOGRID
HYVE
JoyentCloud
Lunacloud
M5
Ninefold
OPENHOSTING
Rackspace
SERVERMULE
Storm
StratoGen
Terremark
VPSNET
Windows Azure
Zettagrid
Google is not included among the providers since it
does not offer price bundles but it rather charges for
each CPU and each GB of storage and memory
capacity. The price bundles are specified by the
resources presented in Table 2, together with the
considered values. These characteristics participate as
variables in the hedonic pricing model.
Data correspond to the IaaS services offered by
cloud providers who use different pricing models.
The study started by selecting specific computing
requirements (e.g. 2xCPU, 1GB RAM, 50GB
Storage, 5GB Tranfer-Out, Linux) but due to the fact
Table 2: IaaS characteristics.
Characteristic Description Values
CPU CPU power 2x, 4x, 6x / 3x,
5x, 7x
RAM RAM size in
Gigabytes (GB)
1, 4, 16, 32
Storage Measured in GB 100, 1000
Transfer_Out Number of bytes sent
by server to Internet
per month. (GB)
5, 10000
OS Operating System of
the server
Linux,
Windows
Subscription Indicates if there
should be a
subscription
No, Yes
(corresponds
to 1 year
subscription)
that many of the providers (e.g. Amazon, Rackspace,
GoGrid) use price bundling, the best package of
resources, which was most close to each customer’s
needs, was chosen every time. Prices range between
$31 and $3,318 per month and there are observable
differentiations depending on the existence of a
subscription, while the duration of the subscription
does not affect the price substantially. The operating
system parameter (OS) and the subscription
characteristics participate as dummy variables. The
values for the OS are 0 for Windows and 1 for Linux
and, regarding the subscription, corresponding values
are 0 for no subscription and 1 for a subscription.
Not surprisingly, the most popular geographical
continent for providers is North America, with 19 out
of the 24 to have datacenters located there, followed
by Europe, with 13 providers. Australia and Asia
follow with 8 and 6 providers, respectively, and
Africa comes last with just 1 provider.
Among the limitations of the collected dataset is
that there are a few more characteristics participating
in the construction of the price bundling, which were
not considered into this study. These characteristics
are the Transfer In (the number of bytes received by
server from the internet per month), the Time On
(proportion of the day the server is available) and the
option that the CPUs, the RAM and the storage can
be distributed among more than one physical server.
The value of the Transfer In characteristic does not
contribute at a substantial level to the shaping of the
pricing bundles, because many cloud providers such
as Amazon and ecloud24 charge customers only for
the outgoing traffic and the others include it as a small
amount in the total price of services. Therefore, with
no loss of generality, the Transfer In attribute was
considered to be at 1GB per month. As far as Time
On is concerned it was set at a level of 100%
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503
availability per day. The default offered value of non-
distributed resources was also considered.
Moreover, some non-functional factors, such as
availability and reliability of resources or the lack of
fulfilling the agreements between consumers and
providers, were not considered in the price model, as
variables. Inclusion of these characteristics is an
interesting extension of the model, since it may reveal
their potential to affect the price of a cloud offering.
This needs to be empirically proven through the
execution of many different scenarios.
The results of the hedonic pricing method are
summarized in Table 3.
Table 3: Results of hedonic method.
Coefficients Value
Constant
130,499***
(35,42)
CPU
14,532**
(6.41)
Storage
0,249***
(0.02)
RAM
20,434***
(0.86)
OS
-16,91
(2.29)
Transfer_OUT
0,076***
(0.002)
Subscription
-85,82
(20.28)
*** p<.01, **p<.05, *p<.1, n.s. not significant
The calculated R
2
value is 57.91%, indicating that
although a great portion of the uncertainty is
described by the model, the linear form of the model
may not be the most appropriate to describe the
pricing index and alternative formulations could also
be considered.
As observed, all parameters are significant and
they contribute to the shaping of the price.
Subscription is the parameter contributing more to the
price index, followed by the RAM size and the CPU.
The high value of the constant, which represents a
fixed monthly fee, supports the finding that the
subscription is a crucial parameter. Storage does not
seem to affect the price very much. The choice of the
operating system affects pricing at a high level, since
Linux reduces the price of the bundles by a factor of
16.91.
5 CONCLUSIONS
The hedonic pricing method was used in this work, in
order to develop a price index for the Infrastructure as
a Service cloud computing services. The evaluation
of the method was based on the linear hedonic model
and the data were collected for a number of 22
providers, corresponding to more than 2300 price
bundles.
The results indicate that, apart from the constant
parameter which indicates the importance of the
subscription, a finding that is also supported by the
high value of the subscription parameter, the RAM
size and the CPU are also of substantial importance
and significance. On the contrary, the storage and the
transfer out parameters seem to affect the pricing
procedure less.
As in most cases, there are some certain
limitations in this work, which in turn constitute its
further extension and indicate directions for future
research. Among them is the use of nonlinear
functional forms in the hedonic formulation, seeking
to improve the accuracy of the pricing index. The
value of R
2
achieved indicate that it would be worth
testing. Apart from the general price index
considering price bundles across all providers, the
construction of an index for each provider would be
of particular interest, mainly for comparison reasons.
The construction of price indices for the other
cloud computing models, namely the software as a
service (SaaS) and the Platform as a Service (PaaS),
where literature has little to present, would be another
important, as well as interesting research direction.
In any case, the existence of a price index for the
cloud services can provide very useful information,
not only regarding the pricing schemes but also
regarding the market of cloud itself and could suggest
optimal pricing approaches of the cloud services.
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