Cloud Suitability Assessment Method for Application Software
Mesfin Workineh
, Nuno M. Garcia
and Dida Midekso
IT-doctorial program, Addis Ababa University, Addis Ababa, Ethiopia
Universidade da Beira Interior, Faculty of Engineering, Computer Science Department, Covilhã, Portugal
Instituto de Telecomunicações, ALLab-Assisted Living Computing and Telecommunications Laboratory, Covilhã, Portugal
Universidade Lusófona de Humanidades e Tecnologias, Lisbon, Portugal
Department of Computer Science, Addis Ababa University, Addis Ababa, Ethiopia
Keywords: Cloud Computing, Migration, Application Software, Suitability, Decision-making, Method.
Abstract: The advantages and initial adoption success stories of the Cloud computing inspire enterprises to migrate
their existing applications to the Cloud computing technology. As a result, the trend of migrating existing
application software to the Cloud grows steadily. However, not all applications are ideal candidates to be
ported. Moreover, very often client organizations do not have the appropriate methods to determine which
of their IT services are appropriate for migration. In this respect, a method is required to assess the
suitability of the existing applications before embarking on migration. This study designs a method to assess
Cloud suitability of exiting application software following the design science approach. The method is a
multi-step approach composed of seven activities, devised with the goal of reducing the risk of making
wrong migration decisions. Further research will be used to validate and refine the proposed method.
The trend for adoption of Cloud computing has been
increasing from time to time but the migration of the
existing systems to Cloud solution is still in its
infancy (Banerjee, 2012; Loebbecke et al., 2012).
However, to benefit from Cloud solution, the
number of organizations migrating existing software
systems to Cloud computing environments have
been growing steadily (Juan-Verdejo et al., 2014;
Binz et al., 2011). Beside the potential advantages,
the initial success stories of Cloud computing
adoption inspire enterprises to migrate their
existing applications to a Cloud-based
architecture (Andrikopoulos et al., 2014, 2013; Juan-
Verdejo, 2012).
Migration of an application on to Cloud also
looks as an attractive investment for enterprises
(Banerjee and Mohapatra, 2013). However, not all
applications are ideal candidates to be ported to a
Cloud platform, or hosted on a Cloud infrastructure
(Chantry, 2009; Böhm et al., 2010; Abduljalil et al.,
2012; Jamshidi et al., 2013). In this respect, the
Cloud suitability of application software must be
assessed before embarking on migration. But, client
organizations lack appropriate method to assess
Cloud suitability of IT services (Loebbecke et al.,
2012; Banerjee, 2012).
Cloud suitability assessment is an initial activity
of the migration method (Khajeh-Hosseini et al.,
2012). The outcome of this assessment phase
determines whether or not to proceed with further
Relatively less emphasis has been given in the
literature to Cloud suitability assessment method.
Literature also revealed that most of the Cloud
computing adoption decisions are made in
qualitative manner (Kaisler et al., 2012). Even the
existing quantitative methods aggregate the value for
all criteria as a single value to make decision (Deb,
2010; Beserra et al., 2012; Menzel and Ranjan,
2012; Menzel et al., 2013). But in real cases, the
value of certain criteria must achieve a minimum
benchmarked value and it might not be also
compensated by positive value of other criteria for
the migration to be effective. Hence, a new
systematic method which handles such kind of non-
compensated criteria independently is required.
The method we have proposed considers the
technological, the target Cloud, the risk willingness,
the application nature, and the organization and
business as decision area to assess Cloud suitability
Workineh, M., Garcia, N. and Midekso, D.
Cloud Suitability Assessment Method for Application Software.
DOI: 10.5220/0006365206260631
In Proceedings of the 7th International Conference on Cloud Computing and Services Science (CLOSER 2017), pages 598-603
ISBN: 978-989-758-243-1
Copyright © 2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
of the existing application software. This method
guides the decision maker to make an informed
migration decision. As a result, the risk of making a
decision contrary to the organizational objectives
can be reduced.
The remainder of this paper is organized as
follows. Section 2 gives a brief description of the
related work. Section 3 presents the approach used
to develop the method. Section 4 introduces the
activities of Cloud suitability assessment method.
Section 5 gives conclusions and future research.
Most of the migration approaches proposed in the
literature uses different approach to check the
alignment of legacy applications with Cloud
(Beserra et al., 2012; Khajeh-Hosseini et al., 2012;
Menzel and Ranjan, 2012; Menzel et al., 2013;
Andrikopoulos et al., 2014). But there are very few
exceptional works which tried to assess Cloud
suitability of enterprise application software (Deb,
2010; Kishore et al., 2011; Misra and Mondal, 2011;
Juan-Verdejo and Baars, 2013; Frey et al., 2013).
Deb (2010) proposed an approach to determine
suitability of enterprise applications for the Cloud
based on the Analytic Hierarchy Process (AHP)
approach. The method evaluates Cloud suitability of
applications in three dimensions: business,
technology and risk appetite of an enterprise.
Kishore et al. (2011) assessed Cloud suitability
of a particular web service using a Turing machine
approach and classified the web service as suitable
or unsuitable to be deployed over Cloud. The
authors consider properties of web services and
Cloud services as evaluation criteria.
Misra and Modal (2011) identify a company’s
suitability for migrating to the Cloud environment
and model Return on Investment from using Cloud
computing. Companies’ business key characteristics
as well as pre-existing IT resources were used to
identify suitability of companies for Cloud. They
used mathematical modeling approach to compute a
suitability index based on credit they assigned to
different factors. The model set an upper and a lower
cut-off point to assess organization as suitable, may
or may not be suitable or unsuitable.
Frey and Hasselbring(2011) proposed CloudMIG
approach to migrate software system to IaaS or PaaS
Cloud environments. The approach classifies
existing software systems regarding their Cloud
suitability into five classes as: Cloud incompatible,
Cloud compatible, Cloud ready, Cloud aligned, and
Cloud optimized.
Juan-Verdejo and Baars (2013) proposed a
framework to assess suitability of software
components migrating to a hybrid Cloud
deployment model. The framework was modeling
the interdependencies between the software
components taking into account many parameters.
The method we propose assesses the Cloud
suitability of targeted application based on multiple
criteria decision making approach and assign to
preference-ordered predefined classes. It considers
Cloud suitability assessment as classification or
sorting problem. Unlike other methods proposed in
the literature, our method doesn’t aggregate the
whole criteria as a single value to compute
suitability index. Rather, it classifies criteria as the
one that can be compensated or not compensated by
merit of other criteria to conduct sorting into two
stages. But none of the proposed methods in
literature performs like that.
As the output of this research is an artifact that is a
method, design science approach is used to design
this method. Design science research is well suited
for a research that needs to create practical IT
artifacts such as construct, model, method, or an
instantiation (Hevner et al., 2004). Design science
research project must contain three clearly
identifiable and closely related cycles (relevance,
design and rigor) of activities (Hevner, 2007).
Hence, our research method was structured based on
these three-cycles of activities as shown in figure 1.
Figure1: Methodology adopted from (Hevner et al., 2004).
To ensure the rigorous of the research, proposed
method was grounding on theory and involves
expert evaluation. In the design cycle activity the
method is going to built based on multi-criteria
decision making approach (MCDMA) and evaluated
by the domain expert iteratively. In the following
subsection, MADMA steps used to designing the
cloud suitability assessment method are detailed.
Cloud Suitability Assessment Method for Application Software
Multi-criteria decision making approach is used to
develop Cloud suitability assessment method. The
method we propose involves a number of activities
as shown in the Figure 2. These activities are
generic, later to be tailored to a specific organization
and to specific multi-criteria decision making
techniques and tools.
Figure 2: Activities for Cloud suitability assessment.
4.1 Determine Hierarchical Structure
To assess the Cloud suitability of application
software, different decision areas or components
must be considered. Lantana Consulting Group
states that suitability is difficult to define and
measure precisely, and therefore it is easiest to look
at suitability in terms of components (Lantana
Consulting Group, 2011).
In this step, all criteria and sub criteria in each
component must be identified iteratively to construct
hierarchical structure. The literature review and
stakeholders interviews are used to identify these
components and criteria in the hierarchical structure.
There have been a few attempts to model Cloud
suitability of enterprise application software, but
none of them incorporate all the necessary
components. Some of them consider the suitability
of applications for Cloud only from the technical
point of view (Frey and Hasselbring, 2011; Menzel
and Ranjan, 2012). The other studies consider it
from economical point of view (Khajeh-Hosseini et
al., 2012; Misra and Mondal, 2011). But in the
Cloud environment, the decisions taken at business
level will raise constraints to the technology and
vice-versa (Orue-Echevarria et al., 2012). This study
incorporate all together and identified five different
decisions areas from literature such as: technology,
business and organization, nature of application, risk
willingness and targeted Cloud (Banerjee, 2012;
Beserra et al., 2012; Orue-Echevarria et al., 2012).
4.1.1 Technology
A technology component refers to technology up on
which an application relies to give services. As the
Cloud computing technology gives its service based
on the Internet, the decision of migration of an
application to Cloud must consider the availability
of network infrastructure and the network
bandwidth. Network bandwidth is a critical factor to
be considered because higher bandwidth usually
means higher costs (Banerjee, 2012) and low
bandwidth may seriously hamper the availability of
the application in view of candidate workloads.
4.1.2 Risk
Risk refers to chance of dangers that are associated
with “living in the Cloud”. Migrating application is
expected to respect constraints imposed by the
Cloud provider and to provide expected quality of
service. These constraints may affect the enterprise
policies related to privacy and/or security, for
instance, to share virtual machine with other
customers of the provider. This condition may result
in security or privacy breaches. Therefore Cloud
suitability decision must consider the risk appetite of
the organization (Deb, 2010; Beserra et al., 2012).
4.1.3 Nature of Application Software
Nature of application software assesses how the
migrating application software characteristics fit
with the Cloud computing environment. To fit to
Cloud environment an application may need to be
adapted, therefore the migration complexity and cost
depends on the way that application was previously
designed. For instance an application implemented
as service oriented architecture can be migrated to
the Cloud platform in an easier manner when
compared to a composite application that is
implemented in a multi-tier architecture. Therefore,
different factors related to nature of application must
be taken into consideration to determine how well an
application is suited for Cloud.
4.1.4 Business and Organization
The criteria on Business and Organization refer to
the economical aspect of the migration and traits of
organization owning the application. The migration
of application software is not simply lifting and
putting the application to different platforms. It
requires assessing the application with respect to the
organization’s portfolio to determine how well it is
suited for Cloud environments. For instance, legal
CLOSER 2017 - 7th International Conference on Cloud Computing and Services Science
and regulatory constraints like enterprise-specific
policies, industry-specific laws and regulations, and
national privacy legislation that have to be respected
after the application and enterprise data have been
migrated (Juan-Verdejo, 2012). Beside socio
technical suitability, the migration of an application
must be economically feasible for the organization
to be able to reap benefits from the migration.
4.1.5 Targeted Cloud Environment
The targeted Cloud environment refers to different
characteristics of the targeted Cloud and constraints
that are imposed by Cloud providers. It is important
to consider characteristics of the Cloud as it is the
destination for the application to be migrated. The
characteristics of the Cloud like scalability,
availability and reliability (Kishore et al., 2011)
affects the suitability for an application running on
the Cloud. A migrated application is also expected
to satisfy the constraints imposed by the Cloud
provider, for instance, the access to the file system,
the number of files, or number of calls to specific
methods (Frey et al., 2013). Likewise, the Cloud
environment has to comply with security, privacy,
performance, availability and regulatory
requirements of the targeted application (Banerjee
and Mohapatra, 2013).
4.2 Define Suitability Classes and Profile
Garg et al.(2011) defined suitability of a Cloud
provider for customer requirements and quantified
as values between 0 and 1(as ratio scale). Kishore et
al. (2011) measure suitability using nominal
scale(yes/no). Other scholars measure suitability
using ordinal scale (Frey and Hasselbring, 2011;
Misra and Mondal, 2011).
Similar to Frey and Hasselbring (2011) our
Cloud suitability assessment method uses five
ordinal scales (extremely suitable, very suitable,
suitable, slightly suitable, and unsuitable) to measure
or describe the Cloud suitability of an application
software. These measurement scales are considered
as classes to sort applications software based on
their suitability for the Cloud migration. Sorting of
the application requires to compare aggregated value
of an application with some reference profiles that
distinguishes the classes.
Reference profile r
of a class k is defined as a
vector of local profiles (r
, r
, … , r
)) for
each criterion (C
; C
; . . . ; C
) where r
is the local
profile class k for C
criterion. The local profile
refers to the minimum performance on each positive
criterion or maximum performance on each negative
criterion that application software satisfies to be
belongs to a class. The local profile for each class is
determined by the organization’s decision analyst
based on the business needs of that organization.
Sometimes it is difficult to define the reference
profile of a class. In that case, it is required to define
the central profiles of a class (Ishizaka et al., 2013)
as an average value an application software system
must satisfy on each criterion to belong to a class. If
there is equal distance between two central profiles,
then the reference profile of a class is determined as
an average value of the central profile of a the class
and preceding class (r
+ r
4.3 Categorize Criteria
Most of existing Cloud computing adoption decision
approaches are based on a qualitative approaches
(Kaisler et al., 2012). Even the existing quantitative
approaches evaluate the decision in such a manner
that the risk or cost of one criterion is compensated
by the merit of other criteria, but this is not always
true. There is a case where the deficiency in one
criterion cannot be compensated by merit of one or
more other criteria, for example, an application may
be evaluated to have high suitability scores in the
application nature, and business value, but it may
not be a good candidate for migration if the risk
exposure is higher than the level of risk an enterprise
is willing to accept.
Taking this into consideration the criteria used to
assess Cloud suitability of application software
system is categorized as screening criteria and
evaluation criteria. Screening criteria are a set of
criteria whose limitation should not be offset by the
strength of the other criteria. In such a case for an
application to be suitable for migration the minimum
limit of profit criteria must satisfied or must not
exceed the maximum limit of cost criteria.
Evaluation criteria are a set of attributes used to
assess Cloud suitability of a software system whose
value may be offset by the strength of the other
criteria. An application can assume any value in
these criteria from their domain.
4.4 Determine Relative Priority and Scale
Each dimensions and respective criteria have
decisive effect whether to move or not move legacy
software system to Cloud, but their relative
importance or weight is different (Menzel et al.,
2013). Therefore, such weight has to be set based on
Cloud Suitability Assessment Method for Application Software
the business needs of an organization and type of
migrating application.
There are different mechanisms to set such a
weight for criteria from decision maker preference
score. AHP is one of the mostly used methods to
determine relative weights of each attributes from
pair wise comparisons of each attributes in multi
criteria decision making approach.
Some criteria may not have standards of
measurements for instance security. For such kind of
criteria it required to set measurement scale and
possible value (domain) for criteria.
4.5 Select Sorting Model
Cloud suitability assessment method evaluates
application software using a set of criteria and
categorizes them into different classes. If these
classes are ordered such kind of a problem is said to
be sorting problem, otherwise, it is said to be
classification problem (Zopounidis and Doumpos,
2002). Sorting or classification model can be
developed by different disciplines such as: statistics,
artificial intelligence, and operation research. This
study considers operation research approach to
develop the method. The MCDMA has the
following advantages (Zopounidis and Doumpos,
2002): allows to incorporate decision maker’s
opinion; and needs not be data intensive to generate
a classification or sorting model.
4.6 Compute Suitability Index
The method we have proposed first assign a value
for application against each screening criteria and
then multiply with respective global weight to
classify the application using none compensatory
multi criteria classification method as that can be
adapted for migration, stay on primes, or
redeveloped for migration. Then if the application
can be adapted for migration, go to second phase of
analysis to show extent of its suitability.
In the second phase a weighted value of
evaluation criteria aggregated to give suitability
index using selected compensatory MCDMA. The
suitability index could then be used to assign
application into different suitability classes.
4.7 Assign to Specific Class
A degree of alignment during the reengineering
process may be different for applications in each
class and this degree of alignment is considered as
the weight for the class, the weight can be generated
from the preference score of the decision maker.
The local reference profile, which is set by the
decision analyst, is multiplied by the weight of
respective class to determine the global reference
profile or global central profile of a class. Then the
global reference profiles are aggregated as a single
value using similar approach used to aggregate the
suitability index. Finally the suitability index of an
application compared against this aggregated
reference profiles of a class to assign a candidate
application software to a specific class.
This research proposed a method to assess Cloud
suitability of application software in five different
decision areas. The three closely related cycles of
activity of design science was identified and used to
structure this research. In design cycle, based on a
MCDMA seven steps proposed to assess Cloud
suitability. The proposed method guides the decision
maker to make an informed migration decision.
Unlike other existing method this method
proposed two stage evaluation approaches to assess
Cloud suitability of legacy application based on two
different groups of criteria. It is unique approach in
considering suitability assessment as multi-criteria
sorting problem.
This research is a work-in-progress to assess the
Cloud suitability index of an application. In future
research, list of criteria in each decision area and
their respective measurement scale will be identified
and validated using expert evaluation. Finally, the
method will be validated empirically taking specific
multi-criteria decision making approach.
The authors acknowledge the contribution of COST
Action IC1303 AAPELE Algorithms, Architectures
and Platforms for Enhanced Living Environments.
Authors affiliated with the Instituto de
Telecomunicações also acknowledge the funding for
the research by means of the program FCT project
UID/EEA/50008/2013. (Este trabalho foi suportado
pelo projecto FCT UID/EEA/50008/2013).
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Cloud Suitability Assessment Method for Application Software