Cloud Utility Price Models
Sururah A. Bello
and Christoph Reich
Obafemi Awolowo University, Ile-Ife, Nigeria
University of Applied Science Furtwangen (HFU), Furtwangen im Schwarzwald, Germany
Cloud Price Models, Utility Price Models, IaaS, PaaS, SaaS, CloudIA.
Cloud Computing’s service models have a number of proprietary pricing models as the services has been
commoditized to some extent. The SaaS especially so far has been known with a flat price within the usage
time. Pricing models need to be more flexible to prevent customers from thinking that paying same price for
a service over a period is no more cost effective, in spite of the level of utilization of the service. For Cloud
Computing this has to be taken into account. Africans have a business model with peculiarity. In order to
expand the acceptability of Cloud Computing to the African market, this peculiarity must be accommodated.
This study proposes cloud utility price models to give the Cloud customer the luxury of different usage style
and determine a customer specific, individual, most suitable price model. It gives the customer the opportunity
to choose a price model for the predicted usage and work within the budget.
Cloud Computing has a pay-per-use business model
that is remarkably different from previous computing
paradigms. There is need for a means to transform the
computing services into monetary entities, thus the
need for an appropriate Business Model. Typically in
Infrastructure as a Service (IaaS) the commodities are
CPU power, memory, etc. for each virtual machine
(VM). For the Platform as a Service (PaaS) in gen-
eral the provider is commoditizing the availability per
month, used storage per month and used bandwidth.
PaaS is considered to be value added virtual instances
and is offered bundled together with the infrastruc-
ture. For the Software as a Service (SaaS) usage time
per month is mostly quantified. This commoditiza-
tion technique does not really portray the true utility
model of SaaS. In the existing models, once a VM is
started, the billing starts irrespective of the actual real
usage of a Cloud service. It should be distinguished
between a heavily used VM and an idle VM. This
study proposes an enhanced billing system that incor-
porates some minute entities for the SaaS. Differential
pricing resulted from quantifying this minute entities
will portray cloud further as utility service. The paper
is organized as follows: After the related work (Sec-
tion 2) various price models are analysed (Section 3).
Section 4 presented the taxonomy of usage items for
monitoring in Cloud services. Section 5 presents re-
warding models and personalized models. Section 6
shows the technical integration and Section 7 the con-
Models are tools for expressing business logic and de-
scribing customers values (Osterwalder et al., 1998).
It is difficult to apply the traditional system centric
allocation policy in a highly dynamic and distributed
environment. Hence the traditional business model
cannot be applied in the Cloud System. Transition
from the traditional Software Packaged delivery to
Software Delivery as a service in Cloud Computing
demands a new Business Model, as illustrated with
a Gaming Software firm in (Ojala and Tyrvainen,
2011). In (Chang et al., 2010), Cloud Computing
Business Models were classified into eight types and
related to a Hexagon Model, which illustrates the
sustainability of an organization through the adop-
tion of the right business model. Our study focuses
on the pricing models. The commercial success of
Cloud computing strongly rests on a cordial busi-
ness relationship between the provider and the con-
sumer through a good pricing system. In (Teng and
Magoules, 2010), an Auctioning Resource Pricing
policy allows users to predict the future resource pric-
A. Bello S. and Reich C..
Cloud Utility Price Models.
DOI: 10.5220/0004350503170320
In Proceedings of the 3rd International Conference on Cloud Computing and Services Science (CLOSER-2013), pages 317-320
ISBN: 978-989-8565-52-5
2013 SCITEPRESS (Science and Technology Publications, Lda.)
ing as well as satisfy budget and deadline constraints
without knowing offers from other users to solve the
price prediction problem. The study favors the cloud
provider. In (Ibrahim et al., 2011) it was established
that the prices charged for computation on IaaS is per-
sonally and socially unfair. The cost does not ac-
count for actual usage and charging is not uniform
the users. Hence proposed a new charging scheme for
IaaS, where users pay for actual consumption.
According to (Rappa, 2004) water, electricity and
public transportation employed the metered usage
of service business models. While radio, TV, tele-
phone and Internet access uses mainly the subscrip-
tion model with some metered services for special
services. Cloud Computing is often seen as a part
of larger development towards long-dreamed vision
of society where computing is delivered as a utility
(Zhang et al., 2010). The cost of compute power
can be calculated by amortizing the capital cost over
the lifetime of the system as done in costing elec-
tricity systems. The total cost of a cloud service
will be a function of capital funds, depreciation, in-
terest rates and system lifetime. In (Jtmaa, 2010)
seven categories of cost in a Cloud System are iden-
tified, they are: server hardware, software, mainte-
nance, network, facilities, cooling and real estate cost.
These cost will be translated to the consumer in two
basic categories fixed cost and the variable metered
cost. Fixed cost include, startup cost and availability
cost, this is synonymous with the access cost. The
variable cost is the operational cost, to be determined
by the consumption style. According to (Strømmen-
Bakhtiar and Razavi, 2011) the metered usage model
is applicable to products whose standardized qual-
ity is to some extent regulated. Therefore proposed
the metering model for IaaS (processing power, stor-
age devices, servers, I/O, other hardware etc). The
IaaS costing to include legal cost (e.g court or lit-
igation cost), data restoration and disaster recovery
cost (though a limit can be set) and regulatory re-
quirements (location dependent regulations, regular
backups). The metered model of the IaaS can have
variants like metering plus subscription or fixed ba-
sic plus metered price model to allow for flexibility.
PaaS is usually bundled with IaaS, so the PaaS cost is
incorporated as added value in IaaS. In costing PaaS,
customization can be allowed like a free start, billing
above free limit and metering price model. The sub-
scription and pay-as-you go model is proposed for the
SaaS layer. The subscription can also have consider-
ations for booking in advance and discounts for pre-
payment. Next is an overview of possible price mod-
Static Price Models. Static Pricing is fixed for a pe-
riod of time, and has a number of flavors.
Flat fee: Customers pay a flat fee for a service for
a fixed price.
Menu Price: The customer pays a price that is al-
ready found on the catalog.
Variable by Market Value Price Models. On the
other extreme of the static pricing is a pricing mecha-
nism dictated purely by the market value. This pricing
mechanism has a number of variants.
Haggling or Bargaining: The buyer and the seller
dispute the price to finalize on a productive agree-
Yield Management: A pricing policy that allows
for optimizing profit by anticipating, influencing
and forecasting customer behavior (e.g. hotel
Auctioning: This offer services for competitive
and open bid can either be Forward or Reverse
Dynamic Market: This flavor results from contin-
ual change in both supply and demand of a prod-
uct or service. Only allows both buyers and sellers
to collectively change price.
Variable by Customer Characteristics Price Mod-
els. In another category of pricing mechanism, pric-
ing can be determined by the characteristics of the
customers, like the volume consumed and other cus-
tomer preferences but will not be affected by the mar-
ket value. This is also implemented in a number of
Service Bundles: Prices can be set according to
the bundling of services features.
Customer CV: The history of customers patronage
can also influence the price for a set of customers.
Purchased volumes can as well be used to differ-
entiate prices for different customers.
The Customers valuation can also determine the final
price of a product or service.
Clouds have a pay-per use model, which implies the
usage monitoring of Cloud resources in clouds essen-
tial. Based on a survey of the monitoring items of
e.g. Amazon, GO Grid, RightScale, and Salesforce, a
taxonomy of Cloud Use Items has been developed at
the level of IaaS, PaaS, and SaaS. From the provider
point of view, everything that causes cost is of inter-
est. From the customer point of view business related
values are more important.
Use Items in IaaS. Use items in IaaS are catego-
rized in Compute, Storage, Network resources with
their attributes (see Table 1). A long way to go is the
integration of QoS into the Cloud infrastructure, but
has to come, if Clouds want to be successful in busi-
ness areas.
Table 1: Use Items in IaaS.
Compute Resource
Attributes Explanation
No of Instances number of on demand
Type CPU performance (e.g. MIPS),
Memory size, etc.
Time of usage Typically per hour
OS Licensing issue
QoS Snapshot, backup, etc.
Storage Resource
Attributes Explanation
Storage Vol. Volume per month
QoS Redundancy, backup, etc.
Network Resource
Attributes Explanation
Msg transfer Upload, download, etc.
Components Need for firewall, routers
Use Items in PaaS. Table II lists the attributes in
the service model PaaS, which are not so wide spread
as the IaaS ones. Of course all the use items of the
IaaS can be taken into account at the PaaS level, but
there are additional ones.
Table 2: Use Items in PaaS.
Attributes Explanation
Deployment Data size, time of the deployment
Database Volume size, performance issues
Scalability Min/max limits, speed, etc.
Application Support for development,
development debugging, library support, etc.
Programming Type of programming language
language and possible licensing issues
QoS Availability., API supp., security
Use Items in SaaS. At present for the SaaS, only
the time of usage is being monitored. This study in-
tends to extend the monitored entities to items de-
scribed in Table 3.
Table 3: Use Items in PaaS
Attributes Explanation
No of user access More users cause more load
No of transactions Completed transactions
Usage history rewarding if experienced
Usage period Daytime or night usage
QoS Request Response Time, etc.
Exchanging ownership requires methods of setting
worth, which is pricing. There is the need to com-
moditize to a standard level in order to price effec-
tively. Next rewarding models are discussed: Re-
warding Loyal Customers: Customer usage history
could also be used to initiate a price differential. To
retain existing customers, long usage history may be
rewarded. This may entice a customer to stay with
an old client. Economically, it is cheaper, since e.g.
an experienced customer requires less help and most
likely has fewer problems to deal with. Rewarding
Off Peak Usage: Usage outside off peak period could
initiate price differential. Instead of zero utilization
of SaaS services in some period, a personalized price
for such period might entice some SME users. E.g.
discount if the service is used between 10pm and 4am
in the morning. Rewarding Completed Transactions:
Service provider could give discount for a service that
generates more income (e.g completed transactions in
an online shop). Rewarding Increased Users: Moni-
toring the number of users accessing a service implies
service specific inspection. It might be used to adjust
the charge for the service. E.g. a very active service
may have it charge slightly raised to increase revenue
for the provider.
5.1 Personalised Model
Each of the above pricing introduces better oppor-
tunity for negotiation, thereby introducing flexibility
which results in personalize pricing to the Cloud con-
sumer. The user is expected to adhere to the earlier
specifications, during the usage the actual usage style
is computed by the system to choose the best pric-
ing model. Business models that vary with customer
characteristics are known to be thriving in Africa. A
typical African is very comfortable with discrimina-
tory pricing. The customer has the feeling of cap-
turing surplus while the seller is also satisfied that a
desperate buyer pays more. Customers characteristics
are a good means of haggling. Africans are not used
to static pricing model. In order to expand the accept-
ability of Cloud Computing to the African market the
variable pricing system need be incorporated deeply
into Cloud Computing. African buyers enjoy a feel of
personalize pricing.
CloudIA (Sulistio et al., 2009) is a private cloud es-
tablished by HFU to harness the potentials of Cloud
Computing for its internal usage and also for Small
and Medium Enterprises (SMEs). The CloudIA ar-
chitecture is based on OpenNebula with extra mod-
ules for monitoring, QoS, etc., and special security
issues. It has layers: the Business, the System and the
Resource layers. The proposed Utility Price Model
is incorporated into the Business Layer of CloudIA.
As shown in Fig. 1 a customer can access the Utility
Price Module through the GUI Interface to specify the
price model individually. The boundaries (min/max
values) are stored in the Business Templates which
have to be maintained by the provider for groups of
customers (e.g. well known customers, first-use cus-
tomers, etc.) The Accounting module, which collects
from the Cloud API (e.g. no of running instances)
Open Nebula Database (e.g. history data), and from
the Cloud Resources itself (e.g. no. of SaaS users).
Figure 1: Architecture of the Cloud Utility Model.
Negotiation in form of give-and-take compromise is
an essential feature of the African Business; this made
static pricing unattractive to a typical African. Cus-
tomizing Cloud offerings for African involves embel-
lishment of the pricing scheme with negotiation that
allows the buyer to personalize pricing. This study
proposed price models that accommodates variable
pricing, hence attractive to the African markets. It
gives the customers the flexibility of choosing a us-
age style accompanied by a flexible pricing as well.
The customer can get better prices if the usage of the
Cloud services are predicted and therefore allows the
Cloud provider to improve its planning and therefore
improve the utilization of the Cloud infrastructure.
Effort is ongoing to develop mathematical models for
the price models and afterwards employs appropriate
simulations to validate the models.
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