Cloud Description Ontology for Service Discovery and Selection
Molka Rekik
, Khouloud Boukadi
and Hanˆene Ben-Abdallah
Mir@cl Laboratory, Sfax University, Sfax, Tunisia
King Abdulaziz University, Jeddah, K.S.A.
Ontology, Cloud, Services, Functional Properties, Non-functional Properties, Discovery, Selection.
Cloud computing is a model delivery of infrastructure, platform and software services over the net. The lack
of standardization of heterogeneous Cloud service descriptions makes service discovery and selection very
complex tasks for Cloud users. To ease this complexity, it is crucial to describe the various Cloud service
pertinent information in a homogeneous model. To provide for such model, this paper offers to contributions.
First, it proposes a Cloud description ontology that integrates service descriptions obtained from heteroge-
neous sources. The ontology model accounts for the functional and non-functional properties, attributes and
relations of infrastructure, platform and software services in order to facilitate the discovery and selection of
suitable Cloud services. Second, this paper presents a qualitative and quantitative evaluation of the proposed
ontology. For the qualitative evaluation, we use a reasoner to identify defects in the ontology, whilst for the
quantitative evaluation we compare our search results with those obtained through a web search engine.
Cloud computing technology can be viewed as a set
of software services (SaaS), platform services (PaaS)
and infrastructure services (IaaS) offered to users over
the net. It has the advantage of offering users scal-
able and elastic service capabilities, at a reduced cost,
speeding up the deployment of their businesses. In-
deed, in the SaaS model, a user gets the provider ap-
plications that are hosted and run in cloud infrastruc-
ture; in the PaaS model, the user gets the platform that
is running in the cloud infrastructure for creating or
deploying the applications; and in the IaaS model, the
user gets the virtual resources in which an execution
environment can be deployed to run an application.
A Cloud federation is a new paradigm where a
set of Cloud providers are grouped in order to offer
a wider range of resources/services and, hence, en-
large their market shares (Rochwerger et al., 2009),
(Buyya et al., 2010). However, Cloud federations are
hindered by the lack of a standard language for de-
scribing providers’ offers making it difficult for end-
users to discover and select an appropriate provider
within a federation. In fact, service providers de-
scribe similar services with different manners (differ-
ent pricing policies, different quality of services, etc.);
such heterogeneous descriptions complicates service
comparison and selection, and may create interop-
erability problems among multiple providers (espe-
cially when migrating from a provider to another).
To overcome the problems stemming from heteroge-
neous service descriptions within a Cloud federation,
we propose to define a common ontology for Cloud
service description. This ontology is especially use-
ful when users want to search for domain specific in-
formation (Ruthven and Lalmas, 2003), (Hoang and
Tjoa, 2006), (Bhogal et al., 2007). It is a means to
share common understanding about the structure of
Cloud service descriptions while covering all Cloud
service layers (IaaS, PaaS and SaaS), and to represent
semantically and structurally Cloud providers’ infor-
It is worth noting that, there exists some indus-
trial standards such as (NIST, 2013), (Forum, 2010),
(Behrendt et al., 2011) and research projects such
as (Beom et al., 2011), (Grozev and Buyya, 2012),
(Moscato et al., 2011), (Youseff et al., 2008), (Zhang
et al., 2012) that treat the aforementioned problems;
unfortunately, current propositions focus only on the
IaaS layer: enabling unified access to existing IaaS
services to deal with the heterogeneity of the Clouds.
That is, they do not offer solutions to the PaaS and
the SaaS layers. In fact, our work is the first to treat
the three layers together. It relies on existing stan-
dards, Cloud providers’ catalogs and some research
projects to define an ontology that accounts for a set of
functional and non-functional configuration proper-
ties. The ontology provides for service selection that:
Rekik M., Boukadi K. and Ben-abdallah H..
Cloud Description Ontology for Service Discovery and Selection.
DOI: 10.5220/0005556400260036
In Proceedings of the 10th International Conference on Software Engineering and Applications (ICSOFT-EA-2015), pages 26-36
ISBN: 978-989-758-114-4
2015 SCITEPRESS (Science and Technology Publications, Lda.)
in the IaaS layer, uses the characteristics of the virtual
machine (VM) (e.g., the CPU type, the memory size,
the operating system, etc.); in the PaaS layer, refers
to the characteristics of the platform (e.g., program-
ming language, libraries, databases, etc.); and in the
SaaS, deals with the application characteristics (e.g.,
usability, scalability, multi-tenancy, etc.).
The rest of the paper is organized as follows. Sec-
tion 2 describes works pertinent to ontology for Cloud
computing. In Section 3, we present our ontology to
describe Cloud services, which we evaluate quantita-
tively and qualitatively in Section 4. Finally, in Sec-
tion 5, we summarize the status of the presented work
and outline its extensions.
In order to standardize the representation of Cloud
computing services, various works use an ontology.
In (Youseff et al., 2008), the authors establish the
knowledge domain of the area of Cloud computing
and its relevant components. They discuss each layer
strengths, limitations as well as dependence on other
layers. In addition, they define a general taxonomy
that captures the inter-relations between the different
Cloud components. This work facilitates the under-
standing of the Cloud environment and its key con-
cepts. The identified concepts cover the three Cloud
layers and they are defined and generated from stan-
dards. However, the informal description of the con-
cepts in this work does not provide for its direct ap-
plication in a selection method. This motivated sev-
eral propositions to formalize the Cloud concepts in
an ontology that facilitates service discovery.
As Gruber (Gruber, 1995), the ontology provides
an explicit specification of a conceptualization. So, an
ontology is a language used for representing formally
the available information of a specific domain provid-
ing a hierarchy of concepts which can be manipulated
by the users.
In (Parhi et al., 2014), the authors propose
a semantic-based service description and discovery
framework using the multi-agent approach. The
framework mainly assists users to discover suitable
service on-demand based on a Cloud service descrip-
tion ontology. Adopting a similar approach, the au-
thors in (Ghoneim and Tolba, 2014) propose a frame-
work composed of two main components: controller
and cloud ontology blackboard environment. Both
components use an ontology to discover the best
suited set of responses for the consumer requests. In a
similar way, in (Bernstein and Vij, 2010), the authors
define an ontology based catalogs that describes fea-
tures and capabilities of resources offered by Cloud
providers, such as CPU, storage, security, and com-
pliance capabilities. The main aim of this work is
to provide a federated Cloud environment. Also, the
authors in (Beom et al., 2011) propose an ontology
based resource management for Cloud Computing.
They select the best Cloud resources based on the ob-
jective of the Service Level Agreement (SLA). Over-
all, the aforementioned mentioned ontologies sup-
port only a few number of concepts in the Cloud
IaaS level. In addition, they do not define the non-
functional elements which are important information
during the selection process to satisfy user require-
In (Zhang et al., 2012), the authors propose the
CoCoOn ontology for describing Cloud infrastruc-
ture services while detailing the functional and non-
functional properties. Using CoCoOn, they imple-
ment a service discovery system to search for avail-
able infrastructure services. The search method relies
on the typical QoS criteria by adopting a Web service
approach. In addition, because CoCoOn covers only
the IaaS level concepts, the search considers only the
virtual machines as Cloud resources.
An interesting initiative in this area is proposed
by the mOSAIC project (Moscato et al., 2011). The
project analyzes existing standards and proposes an
ontology for semantic retrieval and composition of
Cloud services. It provides a platform and proposes
a set of APIs for solving multiple Clouds interoper-
ability problems. In addition, it treats the functional
and non-functional properties. However, the ontology
proposed in mOSAIC considers the IaaS Cloud layer
and neglects the other Cloud service layers (SaaS and
It is worth mentioning that, in all of the above
mentioned published works, the authors do not show
an evaluation to improve the use of their proposed on-
In the herein presented work, we build upon the
above mentioned works to propose a comprehensive
Cloud service description ontology that serves as a ba-
sis for the discovery and selection of Cloud providers
and services in the context of a Cloud federation.
More specifically, we propose an ontology that covers
both functional and non-functional aspects of Cloud
services, and at the three layers of Cloud (IaaS, PaaS
and SaaS). In addition, we evaluate our proposed
work, first, by using a reasoner to examine its logical
or conceptual coherence and, secondly, by comparing
the search results it offers with those of Web search
The work presented in this paper is part of an on-
going project where a Cloud service description on-
tology is the building block for the scheduling of
business processes in a Cloud federation environment
(i.e., the task-resource allocation). In our previous
work (Rekik et al., 2014), we presented a context-
based categorization in order to trigger the on-demand
resource provisioning across multiple infrastructures
providers. We defined a task-related context, user-
related context and resource-related context. This
paper presents a Cloud service description ontol-
ogy to model the resource-related context (i.e., the
resource-related context is explicitly defined by Cloud
providers, it is mainly related to the resource/service’s
characteristics. Generally, this information models
the competitive advantage that Cloud providers may
have over one another) in order to facilitate service
(resource) selection from a set of available ones.
To construct the ontology, we proceeded in two
steps. First, we identified its concepts by analyzing
standards and proposals from the literature. Secondly,
we followed the design principles, presented on (Gru-
ber, 1995), which are objective criteria for guiding
and evaluating ontology designs; they include clar-
ity, minimal encoding bias, extendability, coherence
and minimal ontological commitments. In addition
to these principles, we followed name standardization
which, as suggested in (Cesar et al., 1998), eases the
understanding of the ontology. Following these prin-
ciples, we obtained the concepts shown in Figure 1
and which we detail next. To create the ontology,
we used the prot`eg`e
editor which is the most widely
used, freely available, platform independent technol-
ogy for developing and managing terminologies, on-
tologies and knowledge bases. It is written in Java and
uses Swings to create the complex user interface.
Figure 1: The Top Level Concepts.
3.1 The Actor Class
The Actor class presents the different actors which
participate in Cloud systems. We distinguish be-
According to the mOSAIC (Moscato et al., 2011),
the Developer which develops the Cloud services
and the Administrator which configures them.
As NIST (NIST, 2013), the Broker which moni-
tors, negotiates, schedules, etc. these Cloud ser-
As IBM (Behrendt et al., 2011), the Consumer
which uses the service and the Provider which
offers/has the Cloud service. The NIST (NIST,
2013) classifies the consumer as IaaS, PaaS and
According to the NIST (NIST, 2013), the
Consumer can be an Application Developer
who designs and implements services (application
software), an Application
Admistration who con-
figures and monitors the performances, an Ap-
Deployer who publishes services into
the Cloud system and an Application
Tester who
runs and tests services. In similar, we classify
three types of providers namely IaaS, PaaS and
3.2 The Deployment Model Class
Each provider follows/has a deployment model.
By referring to NIST (NIST, 2013), the Deploy-
Model class includes Private, Public, Hybrid,
Community and Federation sub-classes. In private
model, the services are provisioned for exclusive use
by a single organization. In community model, the
services are provisioned for exclusive use by a spe-
cific community of consumers from organizationsthat
have shared concerns. The services are provisioned
for open use by the general public in the public model.
The hybrid model is a composition of two or more dis-
tinct Cloud models (private, community, or public).
In our work, we haveextended this class by adding
the federation subclass (Figure 2). In the federa-
tion model, the services are provisioned by a group
of Cloud providers that voluntarily collaborate with
each other to exchange resources (Grozev and Buyya,
2012). The federation model has a type and an ar-
chitecture. The federation type can be vertical which
spans multiple levels, or horizontal which takes place
on one level of the cloud stack. The federation ar-
chitecture can be installed with broker (centralized)
where there is a central broker that performs and fa-
cilitates the resource allocation or without broker (de-
centralized) where the cloud providers communicate
and negotiate directly with each other without media-
tors like the peer to peer architecture.
Figure 3: The Virtual Machine’s Characteristics.
Figure 2: The Deployment Models.
3.3 The Essential Characteristics Class
Each provider has an essential characteristics defined
by the NIST standard (NIST, 2013). In fact, in our
work, the Essential
Characteristics class includes the
following sub-classes.
Demand Self Service: The provider offer
computing capabilities, such as server, network
and storage as needed automatically without re-
quiring human interaction.
Network Access: The resources’ capabil-
ities are available over the network and accessed
through standard mechanisms that promote use by
heterogeneous thin or thick client platforms.
Pooling: The resources are pooled to
serve multiple consumers using a multi-tenant
model, with different physical and virtual re-
sources dynamically assigned and reassigned ac-
cording to consumer demand.
Measured Service: The Cloud systems automati-
cally leverage a metering capability at some level
of abstraction appropriate to the type of service.
Elasticity: The resources’ capabilities can
be elastically provisioned and released to scale
rapidly outward and inward commensurate with
3.4 The Service Class
The NIST (NIST, 2013) defines the services offered
by providers as SaaS, PaaS and IaaS. Each provider
offers a service that is in reality a virtualized resource.
That is, an IaaS provider offers either a virtual ma-
chine or a storage space, a PaaS provider offers a plat-
form, whereas a SaaS provider offers a Software or
1. IaaS
Machine class: By accessing to IaaS
providers’ catalogs (Cloud, 2013), (Henry,
2009), (Russell and Cohn, 2012), we notice that
a VM has a location and a type. Depending
to the type, we distinguish the following VM’s
characteristics a(shown in Figure 3:
Resource: The bandwidth is a net-
work resource which has a Delay, Through-
put, Protocol and Price
Per Data Transfer.
Storage Space: The Memory is the storage
space which has Size and Allocation Strategy.
Computational Resource: The CPU is a com-
putational resource. It has Core(s), Frequency
and Architecture.
OS: The operating system of a VM.
Use Case: While examining some Cloud
providers (Cloud, 2013), (Henry, 2009), we
notice that a VM has a use case such as Op-
Memory, Optimized Calculation and
General Use.
Price Per Hour: The VM’s allocation price
per hour.
Price Per Instance: The VM’s allocation
price per instance.
Just to note here that, first, the relation between
the VM, Location and Type classes must be a
part of relation. Indeed, wherever Type and Lo-
cation exist, it is as part of VM, and their pres-
ence implies the presence of the VM. Similarly
for Type class and its all sub-classes. To de-
scribe a VM, we categorize functional and non-
functional properties which are defined in Sub-
section 3.5 (Figure 4).
Figure 4: The Virtual Machine Description.
Space: A Storage space can be pro-
vided with or without a VM. This class
covers Distributed
File System (is a file sys-
tem that allows access to files across the
multiple hosts within the Cloud), Repli-
Relational Database (is a distributed
database which allows the copy of data from a
database in one resource to a database in an-
other) and Disk sub-classes. Like the Type
(of VM) class, these sub-classes are part of
Space class. The storage space has
a Price
Per Data Size, Storage Format, Stor-
age Size Min (the minimum storage capacity)
and Storage
Size Max (the maximum storage
capacity) (shown in Figure 5).
Figure 5: The Storage Space Class.
2. PaaS
Service: The Platform class includes IDE
(an integrated development environment), API
(an Application Programming Interface), Appli-
Server (an environment where applications
can run), Web
Server (server used to publish
websites) and Database (an organized collection
of data) subclasses. These all cited sub-classes
are part of Platform class. Like the IaaS re-
source, the PaaS resource has a Price
Per Hour
and PaaS Service Description which includes the
functional and the non-functional properties.
These properties will be detailed in Subsection
3. SaaS Service: The Software has a
Price Per Hour, Price Per License,
Per Instruction, Price Per User Number
and a SaaS Service Description which includes
a functional and non-functional properties which
are detailed in Subsection 3.5.
3.5 The Property Class
The Property subclasses contain all elements needed
for describing characteristics of Cloud resources.
We distinguish the Functional
Property and the
Functional Property subclasses. The first de-
fines the service function (i.e., what a service is sup-
posed to accomplish) while the second specifies the
service quality or criteria. For each service provided
(IaaS, PaaS and SaaS), we define a set of functional
and non-functional properties.
3.5.1 The Functional Property Class
1. The Functional
IaaS Property class: this class in-
cludes the Functional VM Property and the Func-
StorageSpace Property sub-classes.
(a) The Functional VM Property class: in this
class, we classify the following subclasses
(shown in Figure 6):
The Virtualization class: it is a mecha-
nism for operating multiple systems, servers,
or applications on the same physical server
as a virtual machine. This class covers
the Full-Virtualization (virtualization of the
OS without any modification) and the Para-
Virtualization (virtualization of the OS with
Management class: it covers Backup,
Recovery and Scheduling. The first consists
on creating data images to storage while the
second consists in recovering data in disasters
or failures. The scheduling is the allocation of
cloud resources to consumers.
Monitoring class: it detects slow or fail-
ing components, such as overloaded or failed
Federation class: it allows a set of
Cloud providers to federate together.
Replication class: it involves sharing informa-
tion to ensure consistency between redundant
Migration class: it consists in moving from
the VM overloaded to another underloaded.
(b) The Functional StorageSpace Property class:
such as the Functional VM Property class,
this class covers the Virtualization, StorageS-
Management, StorageSpace Monitoring,
Federation, Replication and Migration
Figure 6: The Virtual Machine Functional Property.
Figure 7: The PaaS Functional Property.
2. The Functional
PaaS Property class: its sub-
classes are described in Figure 7. We dis-
tinguish the Deployment, the Development,
the Testing, the Execution, the Replication,
the Cloud
Federation, the Platform Management
which includes the Database and Applica-
Management and the Platform Monitoring
subclass which includes Application
and Database Monitoring.
3. The Functional
SaaS Property class: we define
the following sub-classes (Figure 8): ERP (is an
Enterprise Resource Planning system which au-
tomates and tracks a variety of business func-
tions across different departments of an organi-
zation), CRM (is a Customer Relationship Man-
agement which provides good customer manage-
ment, opportunities and altogether better cus-
tomer support), Humain Capital Management (it
evolves and automates processes such as payroll,
performance assessments, recruitment and train-
ing), Collaborative
Tools (it allows users to col-
laborate and to work in groups within and be-
tween companies, Content Management (it is a
process enabling company, agency or organiza-
tion to obtain, organize, store and deliver infor-
mation essential to its operation in the most effi-
cient manner), Mailing (it is a mail management
Figure 8: The SaaS Functional Property.
application), Social
Network (a social application
that enables users to interact and share data) and
Gaming (an interactive application that is
used for entertainment, role playing and simula-
3.5.2 The Non Functional Property Class
Basically, non-functional requirements relate to the
qualities of the service (QoS) that cut across user fac-
ing features, such as security, reliability, availability,
etc. In this work, we present the QoS properties while
referring on the ISO-9126 (ISO/IEC, 2001) and non-
functional properties which are specific to each ser-
vice (namely IaaS, PaaS and SaaS).
1. The Non
Functional IaaS Property class: this
class includes the Non Functional VM Property
and the Non Functional StorageSpace Property
(a) The Non
Functional VM Property class: in
this class, we classify the following subclasses
(shown in Figure 9):
Auto Scaling class: In (Alhamad et al.,
2010), the elastic provisioning is defined as a
concept in which computing resources can be
scaled up and down easily by the cloud ser-
vice provider. According to this definition, we
add the following subclasses: VM
Scale Up
(the maximum number of VMs provided for
one user) and VM Scale Down (the minimum
number of VMs provided for one user).
Speed class: the processing speed
of the CPU.
Transfer Speed class: the processing
speed of the network.
IaaS class: it includes the Integrity (the
assurance that the information can be ac-
cessed or modified only by those authorized to
do), Security, Flexibility (the ability to simply
and instantly manage cloud services), Compli-
ance (the services are conform to standards),
Tolerance (the ability to manage auto-
Figure 9: The Non-Functional Virtual Machine Properties.
Figure 10: The Non-Functional Storage Space Properties.
mated way component failures) and Availabil-
ity (the services are accessible).
(b) The Non Functional StorageSpace Property
class: in this class, we classify the following
sub-classes (shown in Figure 10):
Type class: it indicates the access type
of information that can be either read and/or
Time class: it describes the informa-
tion storage time.
IOPS class: it includes the input and output
operations per second.
IaaS class: it includes the In-
tegrity, Security, Flexibility, Compliance,
Tolerance and Availability properties.
2. The Non
Functional PaaS Property class:
this class includes Number
Developers, Lan-
guage Of Development, Platform Type, Plat-
Architecture and QoS PaaS which includes
the Portability (the usability of the same ap-
plication in different environments), Security,
Flexibility (the ability to adjust in needs), Com-
pliance, Fault
Tolerance (the ability to continue
when a resource failure occurs) and Availability
properties (Figure 11).
3. The Non
Functional SaaS Property class: refer-
ring to (KPMG, 2013), the Multi Tenancy (mul-
tiple users can simultaneously access the same
service), the Integration (integrate the data of
users in the SaaS), the No
Vendor Lock In, the
Customization (each user can easily cus-
tomize applications to fit their business pro-
Figure 11: The Non-Functional PaaS Properties.
Figure 12: The Non-Functional SaaS Properties.
cesses without affecting the common infrastruc-
ture), the No
Large Up Front Investment (allow-
ing customers to avoid the large initial investment
in IT infrastructure), the Contract
Length (the pe-
riod that the software product is legally available
for a client) and the QoS
SaaS which includes the
Efficiency (the ability to accomplish a job with
an optimal performance), Satisfaction, Usability,
Adaptability (the ability to adjust in needs), Secu-
rity, Flexibility, Compliance, Fault
Tolerance and
Availability properties (Figure 12).
3.6 Instances Creation
In this subsection, we associate real world instances to
the proposed concepts by referring to a set of Cloud
providers’ catalogs available on the Internet (such as
Amazon EC2
, Rackspace
, HP Cloud
, etc.). We
present an extract of instances related to the IaaS,
PaaS and SaaS services while focusing on some of
functional and non-functional properties (Tables 1, 2,
Once constructed, the ontology needed to be evalu-
Table 1: An Extract of IaaS Individuals.
Domain Object Property Range
AmazonEC2 hasIaaSService VM11, ...
MicrosoftAzure hasIaaSService VM21, ...
... ... ...
VM11 hasType XLarge
hasPricePerHour 0.64$
hasMemory 16GO
hasFrequency 2.7GHz
hasNetwork 50Gbps
hasLocation Asia Pacific
hasUseCase General Use
hasNonFunctionalVMProperty Confidentiality, Migration, ...
VM21 hasType Small
hasPricePerHour 0.055$
hasMemory 2GO
hasFrequency 2.5GHz
hasNetwork 20Gbps
hasLocation US West
hasUseCase General Use
hasNonFunctionalVMProperty Auto Scaling, Cloud Federation, ...
... ... ...
Table 2: An Extract of PaaS Individuals.
Domain Object Property Range
GoogleAppEngine hasPaaSService Platform11, ...
CloudFoundry hasPaaSService Platform21, ...
... ... ...
Platform11 hasLanguage Java
hasDBMS Oracle
hasPricePerHour 0.7$
hasLocation Asia Pacific
hasNonFunctionalPaaSProperty Security, Integrity, Fault Tolerance
Platform21 hasLanguage Python
hasPricePerHour 0.4$
hasLocation Europe
hasNonFunctionalPaaSProperty Cloud Federation, Security, Integrity, Consistency
... ... ...
ated in terms of its qualitative and quantitative as-
pects. The qualitative evaluation treats the logical or
conceptual coherence while the quantitative evalua-
tion is based on some structural criteria (Pradel et al.,
1. Qualitative Evaluation: Such evaluation tends to
produce a set of properties that reflect the respect
for quality criteria. In our work, we are referring
to the following criteria defined by Staab et al. in
(Staab and Studer, 2004) to improve the qualita-
tive evaluation of our proposed ontology:
Duplication: checks if the elements that can be
deducted needn’t to be explicitly mentioned,
Disjunction: checks if the class is the conjunc-
tion of disjoint classes,
Consistency: checks if the definition of a class
does not lead to a contradiction.
Pellet (Evren et al., 2007), Fact++ (Tsarkov and
Horrocks, 2006) and HermiT (Shearer et al.,
2008) are reasoners which allow the evaluation
of class hierarchy, object property hierarchy,
data property hierarchy, class assertions, ob-
ject property assertions and same individuals.
In this work, to validate our ontology, we
Table 3: An Extract of SaaS Individuals.
Domain Object Property Range
SalesForce hasSaaSService Software11, ...
DoliCloud hasPaaSService Software21, ...
... ... ...
Software11 hasCRM SugarCRM
hasPricePerHour 2.5$
hasLocation Asia Pacific
hasNonFunctionalSaaSProperty Multi Tenancy, Reusability
Software21 hasERP ERP5
hasPricePerHour 4.5$
hasLocation Europe
hasNonFunctionalSaaSProperty Multi Tenancy, Privacy, Availability
... ... ...
Table 4: Users’ Query Examples with Using Ontology and Google.
Query Using the Proposed Ontology Using the Google Engine
Q1 IaaS Provider and hasService value Virtual Machine and IaaS provider
hasPricePerHour max 1$ and hasMemory min 7GO and +price per hour<= 1$
hasNonFunctionalVMProperty value ”Auto Scaling” ˆˆstring +auto scaling
Q2 PaaS Provider and hasService value Platform PaaS provider
and hasPricePerHour max 1.5$ and hasDBMS value Oracle and +Oracle+Java
hasLanguage value Java and +price per hour<= 1.5$
hasNonFunctionalVMProperty value ”Confidentiality” ˆˆstring +confidentiality
Q3 SaaS Provider and hasService value Software and SaaS provider
hasPricePerHour max 4.5$ and hasERP value ERP and +ERP5+multi-tenancy
hasNonFunctionalVMProperty value ”Multi Tenancy” ˆˆstring +price per hour<= 4.5$
choose the Fact++ reasoner which is an open
source, free and available with prot`eg`e. So,
while applying the reasoner, we remark there
exist inconsistency errors. So, we mustn’t
define the Non
Functional IaaS Property,
the Non Functional PaaS Property and the
Functional SaaS Property classes as disjoint
classes in order to obtain a coherent and valid
Cloud description ontology. Since there exist
QoS individuals which are ISO properties. These
properties describe the non-functional IaaS
services, the non-functional PaaS services and the
non-functional SaaS services.
2. Quantitative Evaluation: The majority of existing
works, evaluate their proposed ontologies while
comparing them with a reference ontology or with
a domain corpus which includes a set of ontolo-
gies and providing a set of similarity measures.
However, in our case, there do not exists a stan-
dard or open ontology to refer to it to it compar-
ing with ours. So, we present users with our on-
tology and ask them to give their Cloud service
queries in order to evaluate its pertinence. In-
deed, we have interviewed three types of users.
The first one is an IaaS user who asks for IaaS
providers which offer a virtual machine that has
a price per hour at maximum 1$, a memory ca-
pacity at minimum equal to 7GO and guarantee
the auto-scaling non-functional property (query
Q1). The second is a PaaS user who requests PaaS
providers which offer a platform that has a price
per hour at maximum 1.5$, an Oracle database,
the Java as a language of development and guar-
antee the confidentiality non-functional property
(query Q2). The third user is a SaaS user who
asks for SaaS providers which specify an ERP5
as software, a location at Europe zone and offer
at least the multi-tenancy non functional require-
ment (query Q3). Then, we compare the results
obtained while interrogating the ontology and the
web search engine Google
with users’ queries.
Table 4 describes the queries written in DL-Query
language (Horridge and Drummond, 2008) which
is a plugin for prot`eg`e to make it possible for ex-
Table 5: Query Results with Using Ontology and Google.
Query Ontology Google Correct Correct
Results Results Google Results Ontology Results
Q1 10 13000 1 10
Q2 7 705000 0 7
Q3 5 1280 0 5
Table 6: Results Correctness with Using Ontology and
Query Google Ontology
Correctness Correctness
Q1 0.008% 100%
Q2 0% 100%
Q3 0% 100%
posure users’ queries using the proposed ontol-
ogy. We take also these examples to find results
obtained by the Google web search engine (i.e.,
without using ontology). When interrogating the
ontology and the Google web search engine with
the users’ queries in order to select appropriate
Cloud service provider, we remark that the results
carried out using the Google are not precise and
not adequate. Indeed, we find redundant results
and parasite links (i.e., links for blogs, publicity
pages, documents, providers’ catalogs, etc.). In
opposition, while integrating our proposed ontol-
ogy, we obtain quickly and precise results which
satisfies all users (i.e., all results are correct or
precise signifies that all founded Cloud providers
offer all functional and non-functional user’s re-
Table 5 presents the correctness of both our ontol-
ogy and the Google web engine while discovering
services for the Cloud users. As shown, the ontol-
ogy has a high value of correctness which make it
a relevant and pertinent way for users to select the
appropriate services.
In this work, we proposed a comprehensive ontology
for Cloud service description that covers the three lay-
ers of Cloud models, namely IaaS, PaaS and SaaS.
Our proposed ontology treats the functional and the
non-functional properties of services. In addition, it
improves the interoperability problem among multi-
ples Cloud infrastructures, platforms and applications
within a Cloud federation environment. Furthermore,
it helps users to discover and select appropriate Cloud
services and that is proved by interrogating it with
some examples of users’ queries. The ontology pre-
sented in this paper is mainly based on standards, lit-
erature study, Cloud providers catalogs, analysis and
comparisons of existing Cloud Computing services
ontologies and taxonomies.
As a future work, we aim to improve the eval-
uation of the proposed ontology. First, we plan to
work on populating the ontology with more individ-
uals from real Cloud systems to increase the number
of instances (since the evaluation depends on the in-
troduced population). Second, we investigate to in-
terrogate more number of users’ requests and conse-
quently to determine the precision, the recall and the
inference ability of the ontology. Third, we intend to
compare our proposed ontology with another of the
same field. As another perspective, we envisage to ex-
tend our ontology to capture the dependency between
services across the three Cloud models (namely, IaaS,
PaaS and SaaS).
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