A Characterization of Cloud Computing Adoption based on Literature
Antonio Carlos Marcelino de Paula
, Glauco de Figueiredo Carneiro
and Rita Suzana P. Maciel
Salvador University - UNIFACS, Salvador, Bahia, Brazil
Federal University of Bahia - UFBA, Salvador, Bahia, Brazil
Cloud Computing, Legacy Systems, Cloud Migration, Studies Report.
Context:The cloud computing paradigm has received increasing attention because of its claimed financial and
functional benefits. This paradigm is based on a customizable and resourceful platform to deploy software.
A number of competing providers can support organizations to access computing services without owning
the corresponding infrastructure. However, the migration of information systems and the adoption of this
paradigm is not a trivial task. For this reason, evidence from the literature reporting and analyzing experiences
in this migration should be widely disseminated and organized to be used by companies and by the research
community. Goal:Characterize main strategies and methodologies reported in the literature to describe and
analyze the adoption and migration to cloud computing Method: The characterization followed a four-phase
approach having as a start point the selection of studies published in conferences and journals. Results: Data
gathered from these studies reveal a tendency for companies to choose the public deployment model, the IaaS
service model, the amazon platform, and how the most important characteristics in the cloud adoption decision
are cost, performance, and security and privacy. Conclusion: Due to the variety of strategies, approaches and
tools reported in the primary studies, it is expected that the results in this characterization study would help in
establishing knowledge on how the companies should adopt and migrate to the cloud. These findings can be a
useful reference to develop guidelines for an effective use of cloud computing.
Cloud computing (CC) has increased its adoption by
enterprises in an attempt to include agile, flexible and
provident practices in their TI infrastructure. This
platform has also changed the way information sys-
tem users deal and perceive computing (Weiss, 2007).
The possibility to remotely use hardware and software
resources, as well as the expectation of economies of
scale are main reasons that drive the shift for migrat-
ing existing core business applications to the cloud
(Hashmi et al., 2011). Considering the availability of
a plethora of cloud services and providers (Stieninger
et al., 2014), many companies have engaged to trans-
fer their business processes mechanisms to the cloud
platform (Sadighi, 2014).
This scenario has created opportunities for en-
terprises that have manifested perceived inclination
toward cloud computing and the benefits reaped by
them such as low start-up cost, pay only for utilized
services, up-to-date resources, features, and rapid de-
ployment (Buyya et al., 2009)(Li et al., 2013). On
the other hand, moving to the cloud means giving up
incumbent information systems practices and facing
the initial perception of losing control of data that
in a previous scenario were stored in local servers
(Lee et al., 2013). The paper published in (Hurtaud
and de la Vaissire, 2011) considers that consolidat-
ing huge amounts of data within large public clouds is
also perceived as creating a massive point of failure in
the event of a communication breakdown (impairing
data availability) or espionage activities such as the
recent PRISM programme revelations (a clandestine
mass electronic surveillance data mining programme
created by the NSA - US National Security Agency).
Therefore, it is important to gain an understand-
ing of not only opportunities but also the challenges
regarding the migration and adoption as well as the
reasoning of an attractive cost-benefit relationship
and the selection of service providers that best fit
the stalkeholders needs (Li et al., 2012a) (Li et al.,
2012b). Previous studies have investigated the adop-
tion and acceptance of public cloud services at both
the individual and organizational levels (Chang and
Paula, A., Carneiro, G. and Maciel, R.
A Characterization of Cloud Computing Adoption based on Literature Evidence.
DOI: 10.5220/0006264600530063
In Proceedings of the 19th International Conference on Enterprise Information Systems (ICEIS 2017) - Volume 1, pages 53-63
ISBN: 978-989-758-247-9
Copyright © 2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Hsu, 2016). From the organizational perspective, sev-
eral studies have identified the factors that affect cloud
computing adoption,i.e., relative advantage, complex-
ity, compatibility for enterprise (reference omitted
due to the blinded review).
This paper presents a characterization study of
strategies and methodologies reported in the literature
to describe and evaluate the adoption and migration to
cloud computing study to identify evidence from the
literature related to strategies used to conduct studies
focusing on adoption and migration to the cloud com-
puting. The rest of this paper is organized as follows:
Section 2 outlines the research methodology; Section
4 analyzes the selected studies; Section 5 presents and
discusses the results of this characterization. Section
6 presents the threats to validity of the characteriza-
tion. The concluding remarks as well as limitations
and scope for future research have been discussed in
Section 7.
This work considered studies selected following the
phases described in Figure 1 to select the studies for
the characterization. The research questions are pre-
sented in Table 1.
2.1 Systematic Literature Review (SLR)
In contrast to a non-structured review process, a Sys-
tematic Literature Review (SLR) (Brereton et al.,
2007) and (Kitchenham and Charters, 2007) reduces
bias and follows a precise and rigorous sequence of
methodological steps to research literature. SLR rely
on well-defined and evaluated review protocols to ex-
tract, analyze, and document results as the stages con-
veyed in Figure 2. This section describes the method-
ology applied for the phases of planning, conducting
and reporting the review.
We aimed to answers the following questions by
conducting a methodological review of existing re-
RQ1. Which strategies are used by companies to
adopt and migrate to the cloud computing? Identify-
ing goals, proposals and motivations for the adoption
of CC, help organizations to better characterize their
needs and therefore provide conditions to a successful
migration. RQ2. Which factors companies consider
to assess the cost-benefit relationship of adoption and
migration to the cloud computing? The knowledge of
the costs and benefits of migration to the CC can be
used as a support for its planning and reference for
other companies. RQ3. How companies select cloud
computing service providers according to their needs
and profile? The knowledge of successful strategies
and problems raised by inappropriate selection of CC
providers allow organizations to be more confident to
identify providers that best fit their needs.
Publications Time Frame. We conducted a SLR in
journals and conferences papers from January 2005
to June 2016. In a first version of this study, we per-
formed the search from January 2005 to June 2015
and in this new version we extended it to June 2016.
The relevant studies selected in the previous step
was the start point for this characterization study
whose planning is described in the next section.
Identify the Needs for a Characterization Study.
To the best of our knowledge, there is no previous
study characterizing strategies, methodologies, ap-
proaches applied in the literature to describe and eval-
uate how companies adopt and migrate to cloud com-
puting. These results have the goal to understand how
companies have adopted and migrated to the cloud,
and how this experiences have been reported in the
literature. This is a requirement to develop guidelines
for an effective use of cloud computing, specially for
Specifying the Research Question. Considering this
context, we have focused on the following research
question: What are the main strategies and prefer-
ences adopted by studies reported in the literature ad-
dressing issues related to the migration to cloud com-
puting? We derived this research question in three
Specific Research Questions (SRQ) as follows and
presented in Table 1.
Table 1: Specific Research Questions (SRQ).
How can the studies from the selected
papers be organized to support the
identification of main strategies?
How can we classify elements and
strategies adopted in the studies from
the selected papers?
What are the main evidence of
tendencies and preferences in the
planning and execution of the studies
from the selected papers?
Phase 1: Applying the Search String. The applied
search string is presented as follows:
”case study” or ”simulation” or
ICEIS 2017 - 19th International Conference on Enterprise Information Systems
Figure 1: Study Characterization Phases.
Figure 2: Stages of the Study Selection Process containing the Studies included in the extended Version of this SLR.
”benchmarking” or ”experimental results”
or ”experience report” or ”empirical study”
The result of this phase was a list of 39 papers as a
subset of papers from the SLR (reference omitted due
to the blinded review) containing the aforementioned
Phase 2: Selecting the Papers. The 39 selected pa-
pers from the previous phase were analyzed to iden-
tify evidence of strategies adopted in the studies re-
lated to the migration of legacy systems to the cloud.
Papers that do not described their own studies were
discarded. The result of this phase was a list of 19
Phase 3: Building the Repository. In this phase we
created a repository of 19 selected studies with cor-
responding data. The decision of which type of ele-
ments should be included in the repository was based
on their relevance in the context of migration to the
cloud. We considered as relevant the elements that
were mentioned at least once in the selected stud-
ies. In this case, elements more cited are more rel-
evant in the set of the selected studies. To provide
an intuitive view of how these elements were orga-
nized and to facilitate the identification of the strate-
gies used in the studies, we decided to organize them
in a mental model as conveyed in Figure 3. The
nodes are numbered to identify the elements in the
structure. The node 1 represents cloud deployment
models: (1.1) Public, (1.2) Private and (1.3) Hybrid.
The node 2 represents the three service models: (2.1)
SaaS, (2.2) PaaS and (2.3) IaaS. The node 3 repre-
sents possibilities of cloud platforms identified in the
studies: (3.1) Amazon, (3.2) Azure, (3.3) Coresuite,
(3.4) Force.com, (3.5) Google, (3.6) Openstack, (3.7)
Rackspace and (3.8) Salesforce.com. The node 4 rep-
resents evaluation issues considered in the studies:
(4.1) Agility, (4.2) Auditability, (4.3) Accountability,
(4.4) Cost, (4.5) Performance, (4.6) Scalability, (4.7)
Elasticity, (4.8) Effort, (4.9) Flexibility, (4.10) Qual-
ity Assurance, (4.11) Governability, (4.12) Infrastruc-
ture, (4.13) Interoperability, (4.14) Timeframe, (4.15)
Business Popularity, (4.16) Information Technology
Skilled Staff, (4.17) Security and Privacy, (4.18) Busi-
ness Size and (4.19) Usability. The elements of
node 4.12 represent infrastructure resources and cor-
respond to the following elements: (4.12.1) Storage,
(4.12.2) CPU, (4.12.3) Memory and (4.12.4) Net-
work. The node 5 represents the types of studies
identified in the selected papers as follows (5.1) Fea-
sibility Study and (5.2) Experience Report. And the
node 6 is related to analysis method: (6.1) Difficulties
Analysis, (6.2) Proposed Solution and (6.3) Compar-
ison of Results.
A Characterization of Cloud Computing Adoption based on Literature Evidence
Figure 3: Findings from the Selected Studies.
From the metamodel conveyed in Figure 3, it was
possible to build the repository represented in Table 2
comprised by 19 studies. This repository is the deliv-
erable of Phase 3.
Table 2 represents data related to two specific re-
search questions: SRQ1 can be mapped to the col-
umn ”Studies” that contains the identification of the
selected studies for the characterization. The strategy
and phases to select the papers were described in the
paragraphs following Figure 1. The SRQ2 can be ad-
dressed in the identification of elements and strate-
gies in the studies. These elements and strategies
were classified and presented in the columns as De-
ployment Models, Service Models, Platforms, Char-
acteristics, Types of Study and Analysis Method. Re-
garding the specific research question (SRQ3), the ev-
idence identified in the studies will be discussed in the
next section.
Phase 4: Repository Analysis. The strategy to ana-
lyze the repository consisted in identifying the influ-
ence of the selected issues presented in Figure 3 and
according to the levels of abstraction prestend in Fig-
ure 4: Deployment Model, Service Models, Platform
and Characteristics. In the level of Service Model, a
new sub-level was created to contain the following el-
ements Software as a Service (SaaS), Platform as a
Service (PaaS) and Infrastructure as a Service (IaaS).
In the characteristics level, a new sub-level of infras-
tructure characteristics was created to contain the fol-
lowing elements: Memory, Processor and Storage.
In addition, two other levels comprise the repository:
Type of Study and Analysis Method. These two lat-
ter levels are considered as transverse elements when
compared to the other ones previously listed.
Adjusting the Study Characterization Phases. Af-
ter performing the phases presented in Figure 1, we
identified the opportunity to adjust it by the inclu-
sion of two new phases ”Create the Mental Model”,
represented in Figure 3 and ”Design the Abstraction
Level”, represented in Figure 4. The phase ”Create
the Mental Model” was included to enable the cre-
ation of a visual representation of a hierarchy of the
elements identified in the analysis. And the ”De-
sign the Abstraction Level” is a visual representation
to organize the identified elements according to their
groups in the cloud domain. As can be viewed in Fig-
ure 5.
In this section, we describe the main characteristics
of the 19 selected studies according to the level of ab-
ICEIS 2017 - 19th International Conference on Enterprise Information Systems
Table 2: Study Repository to Answer SRQ1.
Models (1)
Models (2)
Type of
Studies (5)
Method (6)
S2 Public 2.3 3.1 4.4 5.1 6.2
S8 Private 2.3 3.6 NM 5.1 6.3
S9 Public 2.1 3.5 4.4 and 4.8 5.1 6.2
S11 Public 2.3 3.2 4.5 5.1 6.3
S13 Public 2.3 3.1 4.4, 4.12.2 and 4.12.3 5.2 6.2
S16 Public 2.2 and 2.3 3.5 4.4 5.1 6.1
S25 Private 2.3 NM 4.17 5.2 6.1
S31 Public 2.1 and 2.2 3.3, 3.4 and 3.8 4.6 e 4.9 5.2 6.3
S32 Public 2.2 NM
4.4, 4.9, 4.14,
4.15, 4.16, 4.17 and 4.18
5.1 6.1
S40 Public 2.3 3.1
4.4, 4.12.1, 4.12.2,
4.12.3 and 4.12.4
5.2 6.3
S44 Public 2.2 3.2 4.5 5.2 6.3
S45 Hybrid 2.3 3.1 4.4 5.2 6.3
S46 Private 2.1 NM 4.17 5.2 6.2
S49 NM 2.1 NM 4.2, 4.11, 4.13 and 4.17 5.2 6.2
S50 Private 2.2 NM 4.4 and 4.5 5.1 6.3
S54 Public 2.3 3.1 4.4 and 4.5 5.2 6.3
S55 Public 2.2 and 2.3 3.1, 3.2, 3.5 and 3.7 4.4 and 4.5 5.2 6.2
S56 Public 2.3 3.1 and 3.5
4.4, 4.12.1, 4.12.2,
4.12.3 and 4.12.4
5.2 6.3
S57 Public 2.3 3.1, 3.2 and 3.7
4.1, 4.3, 4.4,
4.5, 4.10, 4.17,
4.19, 4.7, 4.12.1,
4.12.2 and 4.12.3
5.2 6.2
NM = Not Mentioned.
Figure 4: Level of Abstraction and Hierarchy of Elements
in the Studies.
straction and hierarchy of elements in the studies pre-
sented in in Figure 4. based on the strategy presented
before. We scrutinized the papers reporting the stud-
ies to identify relevant issues for analysis numbered
according to the items presented in Figure 3.
Study S2: Cost Modeling Tool Evaluation.
Goal: Evaluate a cost modeling tool called Cloud
Adoption Toolkit through the comparison of three cost
options: server acquisition, rental of equivalent in-
frastructure in the cloud, use of elasticity of demand
in the cloud. In the S2 study, the authors evalu-
ated the migration to a public cloud (1.1) using the
IaaS service model (2.3). They also used the Ama-
zon provider web services (3.1) to evaluate migration
costs (4.4) using the cost modeling tool (6) as part of
the Cloud Adoption Toolkit and hence the migration
feasibility (5.1) of specific services from the St An-
drews University to the cloud.
Study S8: Feasibility of Web Service Migra-
tion. Goal: Evaluate the feasibility (5.1) to migrate
a web service solution to the cloud. For this end, the
S8 study adopted a private cloud (1.2) with a IaaS
(2.3) as a service model. The authors informed that
OpenStack (3.6) was the chosen platform to migrate
two server to the private cloud. The first server hosts
a web service while the second hosts a database ser-
vice. The servers were replaced by two virtual ma-
chines instances. The authors did not mentioned the
characteristics as expected in the model presented in
Figure 3. The study presented a feasibility study (5.1)
to compare (6) the results.
A Characterization of Cloud Computing Adoption based on Literature Evidence
Figure 5: Adjusted Version of the Characterization Phases.
Study S9: Evaluation of ARTIST Project Tool.
Goal: The S9 study reports a feasibility study of three
tools aimed at evaluating the maturity, technical fea-
sibility (5.1) and business feasibility (5) in the context
of ARTIST project. The study describes the migration
of a Java application called PetStore to the following
scenario: public cloud (1.1) with the Google App En-
gine (3.5) as platform to evaluate cost (4.4) and effort
(4.8) to migrate the PetStore to the cloud application
in such a way that its users can access the application
as a SaaS (2.2).
Study S11: Feasibility of FTP Server Migra-
tion. Goal: The S11 study reports a feasibility evalu-
ation (5) of a FTP server migration to the cloud with
corresponding advantages and challenges of this mi-
gration. For this end, a FTP server was configured
in the public deployment model (1.1) using IaaS (2.3)
through the Windows Azure provider (3.2) with elas-
ticity resources (4.7). A performance (4.5) bench-
marking (6.3) was performed aiming at identifying
potential benefits and problems related with the mi-
gration of legacy systems to the cloud.
Study S13: Desktop-to-Cloud-Migration
(D2CM) Tool Evaluation. Goal: The S13 study
reports the evaluation of the D2CM Tool. The goal
is to investigate the performance (4.5) and usability
(4.19) of the tool. In the study, the authors adopted
the public deployment model (1.1), the IaaS service
model (2.3) and the tool D2CM to migrate an envi-
ronment to the Amazon EC2 (3.1). The D2CM tool
integrate a set of software libraries to support both the
migration and the management of experiments in the
cloud. The focus is the evaluation of the processor
(4.12.2), memory (4.12.3) and cost (4.4).
Study S16: Feasibility Study and Difficult
Analysis. Goal: The study focus on the evaluation
of the migration of three legacy systems from the
British Telecom to the Google App Engine (3.5). The
study uses a compatibility checklist to guide the cost
estimation (4.4) to migrate to the public deployment
model (1.1) through the use of a IaaS (2.3) and PaaS
(2.2) service models. In the case of the IaaS, the cost
consisted basically in the management of resources
without the need to change the code. However, when
compared with the PaaS scenario, the cost is higher.
The authors discussed the changes needed to migrate
the three legacy systems to the Google App Engine
(3.5) and the corresponding effort (4.8) and the feasi-
bility (5.1) of the migration.
Study S31: Feasibility Study Goal: Perform a
Feasibility Study (5.1) regarding the Migration of two
legacy systems to the cloud. The authors compared
the results (6.3) of the migration of Enterprise Re-
source Planning (ERP) systems of two companies to
the public cloud (1.1). The first migration used both
the SaaS (2.1) and PaaS (2.2) service models com-
bined with the platforms Salesforce.com (3.8) and
Force.com (3.4). On the other hand, the second mi-
gration used the PaaS (2.2) service model aimed at
improving issues such as flexibility (4.9) and scala-
bility (4.6) provided by the features of PaaS.
Study S32: Feasibility Analysis of a Migrating
an Account System to the Cloud. Goal: Present an
experience report regarding the selection of the effec-
tive service model for an accounting system. The fea-
sibility analysis considered the following issues in the
migration decision: public deployment model (1.1),
the PaaS service model (2.2), flexibility (4.9), cost
(4.4), business popularity (4.15), business size (4.18),
security and privacy (4.17), timeframe (4.14), skill
level of the IT staff (4.16). The authors did not men-
tioned the name of the selected platform (3).
Study S40: Selection of an Amazon EC2 In-
stance Configuration. Goal: Identify the best Ama-
zon EC2 (3.1) Instance Configuration in a public
cloud (1.1) using the IaaS (2.3) service model. The
study used a Feature Model to analyze issues related
to infrastructure (4.12) and cost (4.4) to compare a set
of instances configuration to identify the one that best
fit a specific scenario. The main contributions of the
study is the list of the characteristics as well as con-
figuration options of EC2 together with guidelines for
the configuration of an EC2 instance using the Ama-
zon EC2.
Study S44: Comparing the Performance of a
Legacy System with Its Corresponding Version in
the Cloud. Goal: Collect evidence of the perfor-
mance of a legacy system after its migration to the
cloud and compare with the previous legacy scenario.
The legacy system executed in a .Net platform and in
the cloud used the Microsoft Azure (3.2). The study
concluded that the performance (4.5) does not dete-
riorate when migrating to the cloud. Moreover, the
study presents potential advantages of the PaaS (2.2)
service model in a public cloud (1.1).
Study S45: A Game-theoretic Approach to the
Financial Benefits of Infrastructure-as-a-service.
Goal: Compare the issues of cost (4.4) of an owned
ICEIS 2017 - 19th International Conference on Enterprise Information Systems
data center with the cost of using the platform Ama-
zon EC2 (3.1). The study considered costs per year
for an owned data center as an investment cost amor-
tization and running costs. Investments were con-
sidered as acquisition costs for server and network
hardware together with operation system licenses (3
years write-off) as well as infrastructure and building
costs (15 years write-off). Running costs were con-
sidered as maintenance, power, administration, and
data transfer. The study discussed issues related to ef-
fects of hybrid clouds, reserved instances, economies
of scale and market form, availability risk.
Study S46: Service Provider Selection. Goal:
Select a Service Provider based on security and pri-
vacy (4.17) issues. The scenario is comprised of a
company that aims at providing their point of sale ser-
vices through a SaaS (2.1) in a private cloud (1.1).
Due to confidentiality reasons, the paper does not list
the providers (3) analyzed in the study.
Study S49: Service Provider Selection. Goal:
Evaluate the effectiveness of the Fusion and Aggrega-
tion for Geospatial Information (FAGI) framework to
select service providers. The study discussed the sig-
nificance and ramifications of a structured selection
of a Cloud Service Provider (CSP) in achieving the re-
quired assurance level based on an organization’s spe-
cific security posture. The following issues were con-
sidered in the provider selection: security (4.17), au-
ditability (4.2), portability (4.20), governability (4.11)
and interoperability (4.13).
Study S50: Migrating Legacy Applications to
the Cloud. Goal: this case study documents the mi-
gration of a text-mining application, acting as a proxy
for any legacy application, through a set of stages pro-
gressing towards deployment in a cloud environment.
The Private (1.1) deployment model and PaaS (2.2)
Service Model were used. The Platform (3) was not
mentioned. Performance (4.5) and cost (4.4) were
also evaluated in the study.
Study S54: Benchmark of Cloud Computing
Environments. Goal: Conduct a survey on a se-
lection of Cloud providers, and propose a taxonomy
of eight important Cloud computing elements cover-
ing service models (2), resource deployment, hard-
ware/infrastructure (4.12), runtime tuning , business
model (4.11), middleware, and performance (4.5).
Study S55: Evaluation of the CloudCmp Tool.
Goal: Compare the performance (4.5) and cost (4.4)
of AWS (3.1), Microsoft Azure 3.2), Google App
Engine (3.5) and Rackspace (3.7) cloud providers.
CloudCmp measures the elastic computing (4.7),
persistent storage (4.12.1), and networking services
(4.12.4) offered by a cloud along metrics that directly
reflect their impact on the performance of customer
applications. For this end, the study analyzed the de-
ployment of three simple applications on the public
cloud (1.1) using the IaaS (2.3) and PaaS (2.2) service
models to check whether the benchmark (6.3) results
from CloudCmp are consistent with the performance
experienced by real applications. In this case, the
study validated the conjecture that CloudCmps results
can be used by customers to choose cloud providers
in lieu of porting, deploying, and measuring their ap-
plications on each cloud. The applications included
a storage intensive e-commerce website, a computa-
tion intensive application for DNA alignment, and a
latency sensitive website that serves static objects.
S56 Study: Evaluate the Effectiveness of
Google Compute Engine (GCE) and Amazon EC2
to Deploy Scientific Applications. Goal: Use the
Cloud Evaluation Experiment Methodology (CEEM)
to benchmark GCE and compare it with Amazon
EC2, to help understand the elementary capability of
GCE for dealing with scientific problems.The exper-
imental results and analyses showed both potential
advantages of, and possible threats to applying GCE
to scientific computing. For example, compared to
Amazons EC2 service, GCE may better suit appli-
cations that require frequent disk operations, while
it may not be ready yet for single VM-based paral-
lel computing. Based on the fundamental evaluation
results, suitable GCE environments can be further es-
tablished for studies focusing on real science prob-
Study S57: Evaluation of the SMICloud
Framework. Goal: Evaluate the effectiveness of
the Service Measurement Index Cloud (SMICloud)
Framework to rank Cloud services based on QoS re-
quirements. The SMICloud framework provides fea-
tures such as service selection based on QoS require-
ments and ranking of services based on previous user
experiences and performance of services. SMICloud
uses the public cloud (1.1) and the IaaS service model
(2.3) to rank the following providers: Amazon (3.1),
Microsoft Azure (3.2) and Rackspace (3.7). The fol-
lowing characteristics were evaluated: Accountability
(4.3), Agility (4.1), Quality Assurance (4.10), Cost
(4.4), Performance (4.5), Security and Privacy (4.17),
Usability (4.19), Elasticity (4.7) and Infrastructure is-
sues Central Processing Unit (CPU) (4.12.2), Mem-
ory (4.12.3) and Storage (4.12.1).
In this section, we followed the instructions described
in the phase Characterization the Studies to answer
the Specific Research Questions SRQ1, SRQ2 and
A Characterization of Cloud Computing Adoption based on Literature Evidence
We followed the instructions described in ”Phases
for the Studies Characterization” in section 3 to select
the studies to answer the Specific Research Question
1 (SRQ1). The selected studies were then the refer-
ence to build a repository as presented in Table 2 to
support the identification of elements and strategies
used in studies focusing on cloud computing adop-
tion. To answer SRQ2, we analyzed the data avail-
able in the repository (Table 2) to identify elements
and strategies adopted in the selected studies. From
this point, we were able to organize the data in four
levels: deployment models, service model, platform
and characteristics.
Transversely to these four levels, we included an-
other two perspectives: type of studies and analysis
method. To deal with SRQ3, to identify evidence of
patterns and tendencies in the planning and execu-
tion of the selected studies, we analyzed separately
each level previously described considering both the
repository and the summary of each study presented
in the previous section. In the following paragraphs,
we present the results and discuss them.
Deployment Model Analysis. The deployment
model scenario obtained from the selected studies is
conveyed in Figure 6. As can be observed, there
is a tendency for the use of the public deployment
model in the studies, which corresponds to 68% of
the 19 studies. This can be explained by the avail-
ability of the public providers and the myriad of in-
stances types and profiles with corresponding pricing
they offer which can meet different study needs and
Figure 6: Deployment Models Identified.
Service Model Analysis. Similarly, the service
model scenario obtained from the selected studies in-
dicated that 68% of the 19 studies use the IaaS service
model. The Figure 7 illustrates this tendency. This
can be explained by the relatively deployment easy
of IaaS (2.3) when compared to PaaS (2.2) service
model. In the first case, the deployment can be exe-
cuted through the use of a virtual machine in the cloud
with the full stack of the legacy system to be analyzed
in the study. In the case of PaaS service model, there
is the need to configure the application in the cloud
using the original platform resources such as operat-
ing systems, databases and drivers.
Figure 7: Service Models Identified in the Studies.
Platform Analysis. The Figure 8 indicates that 42%
of the studies used the Amazon EC2 platform (3.1). It
should be mentioned that ve studies did not informed
the service provider used in their respective studies.
We could not find the reasons for the preference for
the Amazon EC2. However, there is the possibility
that this preference may be the result of the flexibility
of pricing plans for the provider available instances.
Figure 8: Platforms Identified in the Studies.
Characteristics Analysis. As can be seen in Table
3 and Figure 9, the characteristics that stood out in
the selected studies were: costs (4.4), performance
(4.5) and security/privacy (4.17). From the number
of occurrences, it is possible to conclude that cost is
by far the most influential characteristic in the cloud
computing adoption. Companies that adopt cloud
computing are willing to pay for resources that can
be allocated in a pay-as-you-go fashion. This can
lead to representative overall cost reduction as a re-
sult of several factors among the following: reduc-
tion of maintenance costs, of energy consumption, is-
sues related to purchasing software licenses and de-
preciation allowances now in charge of the provider,
just to mention some. The performance and secu-
rity/privacy characteristics are issues also prioritized
by users. With regard to security in a natural dis-
aster event,for example, the servers continue operat-
ing normally since the security policy of the service
providers located kilometers away from the headquar-
ters. Regarding the performance, it is possible to al-
locate resources in the cloud changing settings in the
provider and thereby allowing quick response to busi-
ness needs while increasing performance at times of
ICEIS 2017 - 19th International Conference on Enterprise Information Systems
peak processing. This is an important factor to lead
to considerable stability in the services provided by
a company. Moreover, due to elasticity, flexibility
and interoperability, previous demand resources can
return to their original configuration, contributing to
possible reduction of costs. The infrastructure allo-
cated to a specific instance (e.g. memory, storage and
CPU) can vary according to different priorities de-
pending on the profile of the type of service provided
to the final user.
Table 3: Study evidence Consolidation.
Characteristics Studies
S2, S9, S13,
S16, S32, S40,
S45, S50, S54,
S55, S57
S11, S44, S50,
S54, S55, S57
Security and Privacy
S25, S32, S46,
S49, S57
Infrastructure (Memory)
S13, S56, S57 03
Infrastructure (CPU)
S13, S56, S57 03
Infrastructure (Storage)
S56, S57 02
Flexibility S31, S32 02
Agility S57 01
Auditability S49 01
Accountability S57 01
Scalability S31 01
Elasticity S57 01
Effort S9 01
Quality Assurance
S57 01
Governability S49 01
Infrastructure (Network)
S56 01
Interoperability S49 01
Timeframe S32 01
Business Popularity
S32 01
IT Skilled Staff S32 01
Business Size S32 01
Usability S57 01
Figure 9: Characteristics Identified in the Studies.
Type of Study and Analysis Method. In Figure 10,
we can observe that studies use with more frequency
the strategy based on experience report. This is an
interesting finding that show possibilities to share ex-
perience among potentially interested parties in issues
related to cloud computing adoption and migration.
Figure 10: Types of Study.
Figure 11: Analysis Method.
On the other hand, in Figure 11, we can observe
a predominance of studies reporting difficulties, chal-
lenges and results obtained together with benchmark-
ing and difficulties analysis. This lead to 63% of the
occurrences in the studies, what brings possibilities
of lessons learnt in the community that are prone to
adopt the cloud computing paradigm.
The following types of validity issues were consid-
ered when interpreting the results from this charac-
terization. Conclusion validity. There may be bias in
data extraction. However, this was addressed through
defining a data extraction form to ensure consistent
A Characterization of Cloud Computing Adoption based on Literature Evidence
extraction of relevant data to answering the research
questions. The findings and implications are based
on the extracted data. Internal validity. One possi-
ble threat is the selection bias. We have addressed
this threat during the selection step as described in
Figure 1, i.e. the studies included in the character-
ization were identified through a thorough selection
process which comprises of multiple phases. Con-
struct validity. The studies identified from a previ-
ous SLR conducted by the authors were accumulated
from multiple literature databases covering relevant
journals and proceedings. One possible threat is bias
in the selection of publications. This was addressed
through specifying a research protocol that defines the
research questions and objectives of the study, inclu-
sion and exclusion criteria, search strings that we in-
tend to use, the search strategy and strategy for data
External Validity. The set of 19 papers selected in
Phase 4 as described in Figure 1 is a potential ex-
ternal validity threat. In this case, there is a threat
that the results so far obtained could not be general-
ized. However, the studies were selected having as a
start point the ones published between 2005 and 2015
from a previous SLR conducted by the authors. For
this reason, the set of the 19 studies are considered
representative enough as a sampling for this charac-
The analysis of data and the consequent identifica-
tion of strategies, approaches and tools reported in the
studies, could help in establishing knowledge on how
the companies should adopt and migrate to the cloud,
how the cost-benefit relationship can be evaluated as
well as providers can be selected.
The selection of commercial cloud providers is
a challenging task and depends on several factors.
Among other reasons, cloud providers continually
upgrade their hardware and software infrastructures.
The result is that new commercial Cloud services,
technologies and strategies gradually enter the market
(Li et al., 2013). Studies have shown that successful
migration to the cloud are usually driven by a set of
criteria to select providers that best fit the company
needs (Li et al., 2012b) (Li et al., 2010) (Garg et al.,
2013). According to the results of the characteriza-
tion presented in this paper, there is a tendency in the
studies for the public deployment model (1.1). An-
other important finding is the perception of cost re-
duction. This fact is associated with the absence of
the requirement to tie-up capital, to deal with techno-
logical obsolescence, hardware maintenance, as well
as purchasing software licenses and depreciation al-
lowances. In the cloud paradigm, these issues are now
in charge of the provider. We also identified a ten-
dency for the use of the IaaS model service. This can
be explained due to the relative less migration com-
plexity to the cloud that in this case is supported by
virtual machines. On the other hand, the adoption of
the PaaS model service has a potential drawback re-
ported in the studies the need to adapt the application,
including the need to rewrite parts of the code, re-
place libraries and APIs that can be not compatible
with the cloud provider environment. All these fac-
tors together contribute to a higher migration effort
and cost. However, the studies also highlighted the
need to evaluate the cost-benefit relationship of both
possibilities: IaaS and PaaS. Another finding was
the emphasis of the studies in experience report for-
mat revealing opportunities of lessons learnt sharing.
The opportunities of cloud migration and provider se-
lection were conducted considering issues related to
cost, performance and security/privacy. To sum up,
it was possible to identify a tendency in the simulta-
neous use of public providers deployment model, the
IaaS service model and the Amazon platform. As a
future work, we plan to conduct a survey focusing on
companies that migrated to the cloud to confirm the
results of this characterization study.
Brereton, P., Kitchenham, B. A., Budgen, D., Turner, M.,
and Khalil, M. (2007). Lessons from applying the sys-
tematic literature review process within the software
engineering domain. Journal of systems and software,
Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., and
Brandic, I. (2009). Cloud computing and emerging
it platforms: Vision, hype, and reality for delivering
computing as the 5th utility. Future Generation com-
puter systems, 25(6):599–616.
Chang, Y.-W. and Hsu, P.-Y. (2016). Investigating switch-
ing intention to cloud enterprise information systems:
an analysis at the organizational level. International
Journal of Information Management.
Garg, S. K., Versteeg, S., and Buyya, R. (2013). A frame-
work for ranking of cloud computing services. Future
Generation Computer Systems, 29(4):1012–1023.
Hashmi, S. I., Clerc, V., Razavian, M., Manteli, C., Tam-
burri, D. A., Lago, P., Di Nitto, E., and Richardson, I.
(2011). Using the cloud to facilitate global software
development challenges. In 2011 IEEE Sixth Inter-
national Conference on Global Software Engineering
Workshop, pages 70–77. IEEE.
Hurtaud, S. and de la Vaissire, L. (2011). How to ensure
ICEIS 2017 - 19th International Conference on Enterprise Information Systems
control and security when moving to saas/cloud appli-
Kitchenham, B. and Charters, S. (2007). Guidelines for per-
forming systematic literature reviews in software en-
gineering. In Technical report, Ver. 2.3 EBSE Techni-
cal Report. EBSE.
Lee, S.-G., Chae, S. H., and Cho, K. M. (2013). Drivers
and inhibitors of saas adoption in korea. International
Journal of Information Management, 33(3):429–440.
Li, A., Yang, X., Kandula, S., and Zhang, M. (2010). Cloud-
cmp: comparing public cloud providers. In Proceed-
ings of the 10th ACM SIGCOMM conference on Inter-
net measurement, pages 1–14. ACM.
Li, Z., O’Brien, L., Cai, R., and Zhang, H. (2012a). Towards
a taxonomy of performance evaluation of commercial
cloud services. In Cloud Computing (CLOUD), 2012
IEEE 5th International Conference on, pages 344–
351. IEEE.
Li, Z., O’Brien, L., Zhang, H., and Cai, R. (2012b). On a
catalogue of metrics for evaluating commercial cloud
services. In Proceedings of the 2012 ACM/IEEE 13th
International Conference on Grid Computing, pages
164–173. IEEE Computer Society.
Li, Z., Zhang, H., OBrien, L., Cai, R., and Flint, S. (2013).
On evaluating commercial cloud services: A sys-
tematic review. Journal of Systems and Software,
Sadighi, M. (2014). Accounting System on Cloud: A Case
Study. In 2014 11th International Conference on In-
formation Technology: New Generations, pages 629–
632. IEEE.
Stieninger, M., Nedbal, D., Wetzlinger, W., Wagner, G.,
and Erskine, M. A. (2014). Impacts on the Organiza-
tional Adoption of Cloud Computing: A Reconceptu-
alization of Influencing Factors. Procedia Technology,
Weiss, A. (2007). Computing in the clouds. Computing, 16.
Table 4: Studies included in the review.
ID Author, Title Venue Year
S2 A. Khajeh-Hosseini, D. Green-
wood, J. W. Smith and I. Som-
merville, The Cloud Adoption
Toolkit: Supporting Cloud Adop-
tion Decisions in the Enterprise.
Top Ten Cited Paper According to
Google Scholar
SPE 2012
S8 O. Sefraoui, M. Aissaoui and M.
Eleuldj, Cloud computing migra-
tion and IT resources rationaliza-
ICMCS 2014
S9 J. Alonso, L. Orue-Echevarria, M.
Escalante, J. Gorronogoitia and D.
Presenza, Cloud modernization
assessment framework: Analyz-
ing the impact of a potential mi-
gration to Cloud.
Table 4: Studies included in the review (cont.).
ID Author, Title Venue Year
S11 L. Zhou, CloudFTP: A Case
Study of Migrating Traditional
Applications to the Cloud.
ISDEA 2013
S13 S. N. Srirama, V. Ivanistsev, P.
Jakovits, and C. Willmore, Direct
migration of scientific computing
experiments to the cloud.
HPCSim 2013
S16 Q. H. Vu and R. Asal, Legacy Ap-
plication Migration to the Cloud:
Practicability and Methodology.
S25 A. Michalas, N. Paladi and C.
Gehrmann, Security aspects of e-
Health systems migration to the
HealthCom 2014
S31 T. Boillat and C. Legner,
Why Do Companies Migrate
Towards Cloud Enterprise
Systems? A Post-Implementation
CBI 2014
S32 M. Sadighi, Accounting System on
Cloud: A Case Study.
ITNG 2014
S40 J. Garca-Galn, P. Trinidad, O. F.
Rana and A. Ruiz-CortsAutomated
configuration support for infras-
tructure migration to the cloud.
FGCS 2015
S44 P. J. P. da Costa and A. M. R.
da Cruz. Migration to Windows
Azure Analysis and Comparison.
S45 J. Knsemller and H. Karl A game-
theoretic approach to the finan-
cial benefits of infrastructure-as-
FGCS 2014
S46 H. Mouratidis, S. Islam, C. Kallo-
niatis and S. Gritzalis. A frame-
work to support selection of cloud
providers based on security and
privacy requirements.
JSS 2013
S49 C. Tang and J. Liu.Selecting a
trusted cloud service provider for
your SaaS program.
COSE 2015
S50 F. CRowe, J. Brinkley and N.
Tabrizi.Migrating Legacy Appli-
cations to the Cloud.
S54 R. Prodan and S. Ostermann. A
Survey and Taxonomy of Infras-
tructure as a Service and Web
Hosting Cloud Providers. Top
Ten Cited Paper According to
Google Scholar
IWGC 2009
S55 A. Li, X. Yang, S. Kandula and M.
Zhang. CloudCmp: Comparing
Public Cloud Providers. Top Ten
Cited Paper According to Google
IMC 2010
S56 Z. Li, L. OBrien, R. Ranjan and
M. Zhang. Early Observations on
Performance of Google Compute
Engine for Scientific Computing.
S57 S. K. Garg, S. Versteeg and R.
Buyya. A framework for rank-
ing of cloud computing services.
Top Ten Cited Paper According to
Google Scholar
FGCS 2013
A Characterization of Cloud Computing Adoption based on Literature Evidence