A Knowledge based Decision Making Tool to Support Cloud
Migration Decision Making
Abdullah Alhammadi, Clare Stanier and Alan Eardley
Faculty of Computing, Engineering and Sciences, Staffordshire University,
Beaconside, Stafford, Staffordshire, ST18 0AD, U.K.
Keywords: Cloud Computing, Cloud Computing Migration, Analytical Hierarchical Approach (AHP), Case based
Reasoning (CBR).
Abstract: Cloud computing represents a paradigm shift in the way that IT services are delivered within enterprises.
Cloud computing promises to reduce the cost of computing services, provide on-demand computing
resources and a pay per use model. However, there are numerous challenges for enterprises planning to
migrate to a cloud computing environment as cloud computing impacts multiple aspects of enterprises and
the implications of migration to the cloud vary between enterprises. This paper discusses the development
of an holistic model to support strategic decision making for cloud computing migration. The proposed
model uses a hybrid approach to support decision making, combining the analytical hierarchical approach
(AHP) with Case Based Reasoning (CBR) to provide a knowledge based decision support model and takes
into account five factors identified from the secondary research as covering all aspects of cloud migration
decision making. The paper discusses the different phases of the model and describes the next stage of the
research which will include the development of a prototype tool and use of the tool to evaluate the model in
a real life context.
1 INTRODUCTION
Business are currently coming to terms with the
paradigm shift in computing resources known as
cloud computing, which has been classified by
Gartner as one of the most important 10
technologies (Hashizume et al. 2013). Cloud
computing has a number of definitions, depending
on perspective. A widely used definition is that
developed by NIST (National Institute of Standards
and Technology) which defines cloud computing as
“a model for enabling convenient, on-demand
network access to a shared pool of configurable
computing resources […] that can be rapidly
provisioned and released with minimal management
effort or service provider interaction” (NIST, 2011).
This definition focuses on the technical
characteristics of cloud computing rather than the
business perspective. Cloud computing has also been
defined as the provision of virtual computing
resources that provide an on-demand service,
dynamically scalable, shared services, which require
minimal management effort using the Opex paying
model (Marston et al. 2011). This second definition
extends the NIST understanding to include business
aspects and is the sense in which cloud computing is
understood in this paper.
Cloud computing is usually understood to
include three different service models, Software as a
service (SaaS), Platform as a service (PaaS), and
Infrastructure as a service (IaaS). There are two
major types of deployment model which are private
cloud and public cloud, and these are extended to
include hybrid and community clouds (Mell &
Grance 2011). Adopting cloud computing changes
not only technology but also the way in which
enterprises manage their business (Gonzenbach et al.
2014). Migrating enterprise resources to a cloud
solution involves decision making at strategic,
tactical and operational levels and potentially
impacts all aspects of the organisation.
2 CLOUD MIGRATION ISSUES
Migrating services and systems to the cloud has
business as well as technological implications
(Gonzenbach et al. 2014;). One of the factors
637
Alhammadi A., Stanier C. and Eardley A..
A Knowledge based Decision Making Tool to Support Cloud Migration Decision Making.
DOI: 10.5220/0005464006370643
In Proceedings of the 17th International Conference on Enterprise Information Systems (ICEIS-2015), pages 637-643
ISBN: 978-989-758-096-3
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
restricting the growth of cloud computing is the
issues involved in migrating existing systems to the
cloud model. Research on migration to cloud
provision has tended to be based in four main areas;
the decision making stage including analysis of
benefits and risks, identification of factors which
affect cloud migration processes, solutions for
specific cloud infrastructure and/or applications and
case study based evaluation of the migration
process.
Cost and benefits and risk analysis of cloud
migration for a single service model were discussed
by (Khajeh-Hosseini et al. 2011; Martens and
Teuteberg 2011; Yam et al. 2011; Johnson and Qu
2012; Khajeh-Hosseini et al. 2012; Azeemi et al.
2013; Armenise et al. 2014).These studies focused
only on cost and risk analysis, and did not discuss
how deployment and service models should be
selected and how to do the actual migration. In
addition to models which focus on the business
issues, there are approaches which consider
migration from an application perspective. The
literature shows that several studies propose a
migration framework (Wang et al. 2013; Alonso et
al. 2013; Menzel and Ranjan 2012; Tran et al. 2011;
Meng et al. 2011; Binz et al. 2011). The key
problem with these studies is that they focus only on
migrating applications without taking into account
other issues.
Cloud migration has also been studied from the
perspective of deployment models and cloud service
providers (CSPs) selection. Nussbaumer and Liu
(2013) proposed a cloud migration framework to
analyse the business requirements and select cloud
service providers. Similarly, Kaisler et al. (2012)
developed a framework to support cloud migration
decision making which matched cloud solutions to
business requirements.
There has been limited evaluation of cloud
migration to date. Some empirical studies have been
carried out to identify cloud adoption factors (Lian
et al. 2014; Chang et al. 2013; Alshamaila et al.
2013; Carcary et al. 2013; Khajeh-Hosseini et al.
2010) but there is a lack of studies relating to cloud
migration in developing economies. There have
been a number of industry and vendor studies.
However, these studies tend to be vendor specific,
as with the Amazon migration strategy which is built
around the Amazon Web Services (AWS) platform
(Varia 2010) or consider only a subset of issues
(Parakala and Udhas 2011) and again are focused
on developed economies.
The studies presented so far have several
limitations; firstly, most of the models and
frameworks discussed focus on only one or two
aspects of cloud migration. Secondly, some of these
models and frameworks provide an approach for
migrating applications to the cloud and they focus
on technical aspects only without considering
organisational, security and economic factors.
Thirdly, numerous studies have been undertaken to
identify the factors that determine cloud computing
adoption but these studies do not provide
implementation guidance for decision makers. The
literature review has identified the need for an
holistic approach to migrating IT systems to cloud
computing. The variety of cloud migration
frameworks and models at different decision making
levels emphasise the need for an integrated, strategic
approach, to manage the cloud migration process
from the different standpoints of all decision making
levels. The contribution of this research is an holistic
model for decision making in cloud migration which
can be applied both for developing and developed
economies.
3 DECISION MAKING
APPROACH
Organisations are affected by internal and external
factors as well as tangible and intangible factors.
The literature shows that there is little research that
provides an implementation guidance for supporting
decision making during cloud computing
adoption(Gonzenbach et al. 2014; Azeemi et al.
2013; Alshamaila et al. 2013). Multi-criteria
decision making (MCDM) is defined as “the
evaluation of the alternatives for the purpose of
selection or ranking” (Özcan et al. 2011). The
decision making literature provides different
methods and approaches to support MCDM decision
making in different fields including planning,
outsourcing, purchasing and investment (Özcan et
al. 2011)). These approaches include the analytic
hierarchy process (AHP) and the technique for order
of preference similarity to ideal solution
(TOPSIS),which are both are widely used in
decision making especially in outsourcing which is a
related field to cloud migration (Perçin 2009). AHP
has been used in IS outsourcing (Akomode et al.
1998; Yang & Huang 2000; Yang et al. 2007; Bruno
et al. 2012). Menzel & Ranjan (2012) used an AHP
approach to selecting service providers in a cloud
computing environment although this study was
limited to the consideration of technical aspects.
Kahraman et al. (2009) used TOPSIS to evaluate
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service providers, Perçin (2009) used a hybrid
approach by combining the AHP and TOPSIS to
evaluate the third party logistic providers.
3.1 The Analytic Hierarchy Process
(AHP)
AHP is a multiple criteria decision making tool
developed by Saaty in 1980 which decomposes a
problem into subproblems and then aggregates the
subproblems to obtain the optimum solution (Saaty
1994; Yang and Huang 2000; Bernasconi et al.
2010). Saaty (2008) defines AHP as “a theory of
measurement through pairwise comparisons, which
relies on the judgments of experts to derive priority
scales” (2008, p. 83). Saaty’s definition emphasizes
the experience of decision makers as the main driver
in judgement making. One of the benefits of AHP is
that it provides an MCDM method “for measuring
either subjective or objective components without
compromising any of these perspectives” (Akomode
et al. 1998, p. 116). AHP can be defined as a
multicriteria decision making method, to measure
subjective and objective attributes based on the
expertise of decision makers.
The AHP method is based on three fundamental
pillars, the hierarchical structure of the model,
pairwise comparison of criteria and alternatives, and
finally synthesis of the priorities (Dağdeviren et al.
2009). In the structure of the model, problem solving
goal come in the top of the hierarchy. The criteria
come in the second level of hierarchy, and each one
of the criteria may have subcriteria. Alternatives or
solutions come at the lowest level of the hierarchy
(Saaty 1994).
3.2 Cased based Reasoning (CBR)
AHP is based on the knowledge and expertise
available to the decision makers and the decision
makers’ understanding of the problem (Levary
2008). The information available to the decision
makers is critical to the success of the approach.
This investigation therefore uses CBR to improve
the information available to decision makers by
retrieving similar cases to support the evaluation of
the problem. CBR is a knowledge based problem-
solving approach that relies on past, similar cases to
find solutions to problems (Allen 1994), to modify
and critique existing solutions and explain
anomalous situations (McIvor and Humphreys
2000a). The CBR approach is widely used in a
number of different disciplines (Hsu et al. 2004;
Maurer et al. 2010). Hsu et al. (2004) described
CBR as having 5 phases as follows: presentation,
retrieval, adaptation, validation and update. As
problems and solutions differ, CBR adapts, but also
criticizes and modifies similar cases (McIvor and
Humphreys 2000b). Işıklar et al., (2007) claim that
using CBR can reduce the likelihood of repeating
mistakes and encourages learning over time.
4 DEVELOPMENT OF THE
CLOUD MIGRATION
DECISION MODEL
This section presents a cloud migration decision
model which integrates an AHP approach with CBR.
The AHP approach supports decision makers in
weighting criteria to allow the evaluation of options
and selection of the best IT services delivery model.
However, one criticism made of AHP is that the
approach relies on users being able to make
judgments based on expertise and available
knowledge to deal with uncertainty (Dağdeviren et
al. 2009). For this reason, as discussed in section
3.2, this study strengthens the AHP approach with
CBR, using previous cases to help decision makers
weight criteria and validate their results. An
additional reason for using CBR, is that the CBR
approach is able to handle incomplete and imprecise
data (Işıklar et al. 2007) and this is relevant in the
context of cloud migration decision making.
The Cloud Migration Decision Model was
developed in three phases: the first phase consists of
the CBR element, the second phase consists of the
AHP element and the third phase integrates the CBR
element with the AHP element to support decision
making for cloud migration.
Phase One: Case Based Reasoning
This phase developed the case base to store previous
cases. Each case is indexed with five attributes and
each one of these attributes has a pre-defined value.
The attributes used are firm size, sector type, firm
status and IT maturity rate and level of technological
diffusion. The attributes chosen were identified from
the literature and validated during fieldwork which
confirmed these factors as relevant to cloud
migration decision making.
Enterprise size: enterprise size has been
identified to be one of the determinants of
cloud computing adoption (Avram 2014;
Alshamaila et al. 2013).
Industry sector: cloud adoption rates have
been shown to vary between sectors (Low et
al. 2011)
AKnowledgebasedDecisionMakingTooltoSupportCloudMigrationDecisionMaking
639
Enterprise status: the literature shows that
startup enterprises find it easier to adopt
cloud computing than established enterprises
(Gupta et al. 2013; Alshamaila et al. 2013)
IT maturity level: IT enterprise maturity rate
has been shown to affect the adoption of a
cloud computing environment (Khajeh-
Hosseini et al. 2012).
Technology Diffusion : technology diffusion
in general and specifically for cloud
computing varies between developing and
developed countries (Avram 2014; Molla
and Licker 2005) and this influences cloud
migration issues
Phase 2: AHP Model
This phase develops the AHP model, which uses
pairwise comparison to weight the criteria, sup-
criteria and alternatives. The model is shown in
figure 1. Level 1 presents the problem solving goal;
Level 2 presents the criteria and Level 3 presents the
alternatives for the problem solution which for this
scenario have been identified as providing an in
house service, adopting a traditional outsourcing
solution or migrating to a cloud computing solution.
The criteria in the second level of the AHP model
are based on five factors derived from the literature
and validated by fieldwork in Saudi Arabia. The
factors are: strategic, technical, security, economic
and regulatory. Each criteria has a set of subcriteria,
which provide more detailed factors for decision
making.
Phase 3: Integration
This phase combines the CBR element with the
AHP element. Using the AHP model described in
Phase 2, pairwise comparisons are performed for
sub-criteria with respect to the main criteria (parent
in hierarchy), while pairwise comparisons are
performed for criteria with respect of the goal. In the
case of alternatives, there are two ways to rank the
alternatives, which are absolute measurement and
relative measurement. Relative measurement
performs the pairwise comparisons between the
alternatives with respect to each criterion. While in
absolute measurement the alternative ranked with
standard scale (Saaty 1994).
The first step in the model is comparing the new
case with stored cases and finding similar cases as
shown in figure 2. When the similar case is found,
the AHP will be run to weight the criteria. Then, the
Figure 1: Cloud Migration Decision Model.
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AHP result will be compared with result of similar
case, and if the decision makers are satisfied with
result the new case will be added to the case base,
otherwise the AHP process repeats. If the new case
is not similar to the stored cases, the decision maker
will run the AHP approach and add the case as a
new case to case base. The process is illustrated in
figure 2.
Figure 2: Flow chart of the process of cloud migration
decision model.
5 DISCUSSION OF THE MODEL
Combining the AHP approach with CBR provides
users with a knowledge base to support decision
making. The decision as to whether to migrate to
the cloud is a strategic decision which may not occur
more than once in an enterprise’s life cycle. This
means that users may lack the necessary
underpinning knowledge to develop appropriate
weightings and as discussed in section 3.2, this is
one of the limitations of the AHP approach. Using
CBR to provide a knowledge base gives users access
to information about decisions taken in similar and
different contexts and allows users access to a wider
range of experiences.
The CMDM model was evaluated with expert
users from a cloud services provider in Saudi Arabia
to check the relevance of the model for use in an
industry setting. The validation process consisted of
three phases; model concept, factors validation and
CBR attributes validation. The model concept phase
examined whether the AHP criteria identified were
comprehensive and supported structured
examination of the problem, enhanced
communication and reduced decision making times.
The factors validation phase reviewed the subcriteria
and the attributes identified for the CBR element of
the model. The final phase of the validation of the
prototype model involved open questions and an
overview of the model approach. The validation
process provided support for the model concept and
allowed some elements of the model, such as the
description of the subcriteria, to be fine tuned based
on the feedback from the validation process. The
validation process also identified the need for a user
friendly tool to implement the model and allow it to
be used by service users who may lack the expert
knowledge of service provider.
6 PROPOSED DEVELOPMENT
OF CMDM TOOL
The next stage of the research is the development of
a prototype case tool to implement the CMDM. The
model is primarily intended for service users rather
than service providers but as noted in section 5,
service users may lack the expertise to apply the
model in the absence of a tool. Using the tool in a
real life context with service providers and service
users will also allow further validation of the
CMDM. One of the issues identified in section 2
with respect to cloud migration is that there are a
limited number of migration case studies and that
most research around cloud migration has been
carried out in developing economies. In order to
provide a tool which will support an holistic
approach to cloud migration and where the cases
will be relevant to both developed and developing
economies, the CBR element of the tool will include
cases identified from the literature and cases
developed from the field work carried out as part of
this research in Saudi Arabia which is classified as a
developing economy for the purposes of cloud
migration.
7 CONCLUSION AND FUTURE
WORK
Cloud migration decision making has been
investigated in a number of studies, however, these
studies tend to focus on different aspects of the
AKnowledgebasedDecisionMakingTooltoSupportCloudMigrationDecisionMaking
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migration process such as cost and security aspect
and none of the studies reviewed considered all
elements of the decision making process for cloud
computing. There are very few studies of the cloud
migration process in developing economies. This
paper presents an holistic model to support cloud
computing migration decision making. The model
includes all the features which were identified from
the literature and these features were validated in the
field with expert users. The CMDM presented in this
paper uses a hybrid approach, combining AHP and
CBR to strength the support for decision making and
addressing the limitations of a pure AHP approach.
This is an on-going research project; the next stage
of this research is to design a prototype tool to
evaluate the model and test and validate the model in
a real life context.
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