E-learning based Cloud Computing Environment: A Systematic Review, Challenges, and Opportunities

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Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.
Digital Object Identifier 10.1109/ACCESS.2017.Doi Number
E-learning based Cloud Computing
Environment: A Systematic Review,
Challenges, and Opportunities
Hana Eljak1, Ashraf Osman Ibrahim2, 3,* (Senior Member, IEEE), Fakhreldin
Saeed4, Ibrahim Abaker Targio Hashem5, Abdelzahir Abdelmaboud6, Hassan
Jamil Syed2, Anas W. Abulfaraj7, Moh Arfian Ismail8 and Abubakar Elsafi9
1 Al Neelain University, Information Technology, Khartoum, Sudan
2 Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia
3 Creative Advanced Machine Intelligence Research Centre, Faculty of Computing and
Informatics, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu, Sabah, Malaysia
4 University of Roehampton, Computing Department
5 Faculty of Computing and Informatics, University of Sharjah, Sharjah, United Arab Emirates
6 Department of Information Systems, King Khalid University, Muhayel Aseer, Saudi Arabia
7 Department of Information Systems, King Abdulaziz University, Rabigh, Saudi Arabia
8 Faculty of Computing, Universiti Malaysia Pahang, Pekan, Malaysia
9 Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Saud i Arabia
* Correspondence: Ashraf Osman Ibrahim; ashrafosman@ums.edu.my.
“ This work was funded by the Deanship of Scientific Research at King Khalid University through a large group Research Project under grant
number (RGP2/175/44).”
ABSTRACT New technologies drive educational shifts, transforming offline to online learning.
This study investigates e-learning and cloud computing integration to understand synergies and
their potential impact. The study addresses two primary research questions: the influence of e-
learning on factors like architecture, software, performance, security, hardware, network, and
virtual aspects, and the examination of cloud computing services and models such as SaaS, PaaS,
IaaS, and S.O.A. The research aims to provide insights into how e-learning is incorporated in a
cloud computing environment. The motivation behind this study is to investigate the intricate
relationship between e-learning and cloud computing. By analyzing 154 scientific papers, the
study delves into the specifics of this integration, highlighting trends and areas that have received
more attention. The study examines e-learning in a cloud computing environment, focusing on
architecture (27%), general topics (21%), software (19%), and performance (18%). Virtual
environments have fewer security issues, while storage and network focus are more prevalent.
Cloud computing services are mainly all services, with software as a service (18%),
infrastructure as a service (17%), and platforms as a service (10%). Most studies are based on
public clouds (74%), all other models (11%), and hybrid clouds (3%). The study examines e-
learning integration in cloud computing, highlighting limitations in hybrid and private clouds,
specialized infrastructure, and a gap in platforms and infrastructure offerings.
INDEX TERMS Cloud computing, E-learning, environment, educational, e-learning based Cloud computing,
systematic.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3339250
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

VOLUME XX, 2017 9
I. INTRODUCTION
COVID-19 made us realize the importance of e-learning in
filling the gap in the education process during lockdown. This
resulted in the introduction of numerous e-learning platforms
that assist students and institutions in accessing and managing
educational resources and real-time virtual classrooms.
Simultaneously, the cloud computing environment has
improved and is the standard for such applications. Cloud
computing has changed the traditional web-based e-learning
environment by providing internal or external programs that
organize academic performance in a cloud-based environment
and providing full software support and substantial computing
resources implemented anywhere and anytime as the
educational institution desires. Cloud computing improves e-
learning performance. It is for higher institutions such as
institutes and universities, and it is called Cloud Campus, as it
reduces the infrastructure and is more flexible in technology
[1].
In recent years, e-learning and cloud computing have become
increasingly prevalent in educational environments, offering
new opportunities for online learning and collaboration [2][3].
E-learning, which refers to the use of electronic technologies
to deliver educational content, has been shown to offer
flexibility and accessibility to learners. In contrast, cloud
computing, which involves using remote servers to store,
manage, and process data, can provide scalability and cost-
effectiveness for educational institutions [4]. However, there
is still much to be understood about the interaction of these
technologies and the potential benefits and limitations they
offer in educational settings.
To address this knowledge gap, this study analyzed the impact
of e-learning in a cloud computing environment by reviewing
154 related scientific papers. The study focused on research
questions about the effects of e-learning and cloud computing
services and models, including architecture, software,
performance, security, hardware, network, and virtualization.
By examining existing research in this area, this study
provides insights into the current state of e-learning in a cloud
computing environment. It highlights potential areas for future
research and development.
Therefore, it focuses on detecting the dimension of the
empirical use of cloud computing environments to build E-
learning platforms. This systematic study method was used to
answer the research questions. The results show that most
selected studies focus on architecture, followed by general
topics such as software, performance, security, storage,
network, hardware, control, management, and virtualization.
The objectives of this study are as follows:
 To present a comprehensive and systematic review
of e-learning based on cloud computing
simultaneously with their advantages and
challenges.
 To review most methods in e-learning cloud-based
and associated characteristics and drawbacks.
 To specify the supplementary services and models
cloud computing can provide to e-learning.
 To discuss the challenges of integrating e-learning
into cloud technologies.
A. AREAS OF INTEREST OF THE STUDIES
The web-based e-learning environment has become
inappropriate to the requirements of society, as there are many
problems, such as cost, maintenance, management, and others
in e-learning systems, which have prompted educational
institutions to search for practical solutions. The rapid growth
of cloud computing and the provision of appropriate services
such as word processing programs, presentations, and
databases force educational institutions to turn to cloud
computing companies for the right solutions in terms of
Hardware, software, and cost so that applications, programs,
and services can be run online with the option of expanding on
demand [2].
B. MOVING TO CLOUD BASE E-LEARNING
Cloud computing is a standard that provides easy-to-request
network contact for shared networking resources and is a type
of service—platform as a Service (PaaS) and Software as a
Service (SaaS). In addition to the N.I.S.T. definition, four
types of clouds can access all services, including Hardware as
a Service (HaaS), Database as a Service (DaaS), and Business
Process as a Service (BPaas) [3]. Data as a Service (DaaS)
container for several services for learning organizations within
the use of cloud computing for it drives far away additional
services supplied via the cloud, approaches a wide range of
external data sources, and is capable of transporting positive
impacts for organizations, particularly for developing
countries that suffer from a variety of problems getting
information and assistance among organizations for similar or
related data and information—software as a Service (SaaS)
on-demand prepared software according to their needs.
Learning software could be involved as well. Customers are
provided additional free or paid software delivered via the
cloud that is not installed on the device. The PaaS (Platform as
a Service) stores the data for testing, establishing, developing,
hosting, and maintenance. On request, the software industry
engineers in PHP or Java can use the software environment,
such as the integrated development environment (IDE), or
application software development stage, such as the software
development kit (SDK). IaaS (Infrastructure as a Service)
refers to infrastructure managed and delivered to users on
demand, such as storage, memory, and networking. It must be
allowed to manage the infrastructure through different types
of consumer communication with the cloud domain [4].
C. MOTIVATION OF THE STUDY
The motivation behind this study arises from the
transformative influence of emerging technologies on the
educational landscape, particularly the shift from traditional
offline learning to online platforms. This transition has been
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3339250
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

VOLUME XX, 2017 9
driven by the rapid advancements in new technologies, which
have created innovative learning environments. As e-learning
gains prominence, a natural curiosity arises about the potential
synergies that could be harnessed by integrating it with
another groundbreaking technology: cloud computing. The
impetus is to understand how the union of these two fields, e-
learning and cloud computing, could result in a mutually
beneficial relationship, potentially enhancing the quality,
accessibility, and efficiency of educational experiences.
D. CONTRIBUTIONS AND DIFFERENCES FROM
PREVIOUS STUDIES
Even though many previous studies have introduced e-
learning in cloud computing, there are areas for improvement
in explaining the interaction between cloud services and cloud
development models and the affected areas in e-learning.
Table 1 summarizes the related studies reviewed and surveyed
in cloud-based education and compares this study to previous
studies. The contributions of this study are summarized as the
following points:
 Provide Analysis of the previous studies from
2010 to the present, which include e-learning-
based cloud computing, answering questions, and
presenting them in the form of diagrams.
 An in-depth analysis of the e-learning-based cloud
computing environment from 2010 to date, along
with their existing solutions and respective
limitations.
 Analyze the performance of SaaS, IaaS, and PaaS
on e-learning.
 Provide opportunities, open issues, challenges, and
limitations of e-learning based on cloud
computing.
 Analyze and compare the relevant studies in the
same area, considering different factors, as shown
in Table 1.
Through this study, we explain the interaction between e-
learning and cloud computing by defining the type of service,
type of development model, and the solution provided in
education, whether it is software, performance, storage, or a
virtual environment, for previous studies in the period from
2010 to 2022, to clarify the interaction in the e-learning
environment based on cloud computing and its limitations,
as well as trends and future work.
II. RELATED WORK
There are logical reasons to move to cloud computing, such as
lower cost per usage, improved performance, availability of
software packages and higher processing capabilities in
Hardware, automatic software upgrades, saving login times to
the cloud, and increased data reliability. All data is complete
in the cloud. No one can an unauthorized individual have
access to data, which improves security [5]. Cloud computing
technologies are commonly used to improve educational
institutions' effectiveness, cost-effectiveness, and
acceptability [6].
A. STRUCTURAL DESIGN OF E-LEARNING SYSTEMS
The structural design of e-learning systems refers to the
organization, architecture, and framework that underlie the
development and functioning of digital learning
environments. It encompasses various elements and
components designed to facilitate effective online education.
Many studies discuss the structure of e-learning in cloud
computing. Study [6] provided a standard proposal or model
of three layers. The first-layer Cloud Management System
contains subsystems that enable content delivery, resource
management, and content creation, as well as evaluation and
monitoring, with which users interact via the Internet using
user interface software. The second layer is a service provided
via the cloud (software, platforms, and infrastructure). The
third layer represents the hardware components of computers,
networks, central processing units, and memory [7]. Fig.1
shows these layers in detail.
FIGURE 1. Communal structural design of e-learning systems in cloud
computing [7].
B. IMPLEMENTING E-LEARNING-BASED CLOUD
COMPUTING CHALLENGES
Implementing e-learning-based cloud computing presents
several challenges that organizations and educational
institutions must address to transition to this technology-
driven approach to education successfully.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3339250
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

VOLUME XX, 2017 9
Firstly, security issues are a concern in academic institutions,
and secondly, the bandwidth could be improved if the internet
bandwidth is available. We will not provide educational
services. Acceptance is the difficulty of convincing officials to
switch to another environment. Third, the rules of learning
management. There are differences between traditional and
cloud learning management, such as learning management,
teaching, content, courses, exams, and learners . []
Educational institutions, students, and teachers must be
educated to transition to an e-learning-based cloud computing
environment. In addition to choosing the transition mechanism
through paying for service or relying on resources within the
institution, creating a cloud computing infrastructure, and
adapting the e-learning structure based on the cloud [9]. The
study [10] highlights the growing interest in leveraging
technology, especially the Internet, for learning purposes.
However, they show that e-learning systems often require
significant hardware and software resources, which may
require more work for some institutions to afford. Cloud
computing is a potential solution for these institutions as it
offers a cost-effective way to access necessary resources. The
study emphasizes that cloud computing is the future platform
for e-learning, and the paper focuses on the application of
cloud computing in the e-learning environment. Another study
[11] discusses the impact of COVID-19 on education and the
need for educational institutions to become more efficient in
the virtual delivery of quality teaching services. Cloud
computing can be a valuable platform for educators to improve
their teaching practices and productivity. The study explores
the applicability of cloud computing in educational settings
and describes various applications such as cloud rendering,
gamification, and collaborative e-learning technologies. The
study also highlights some challenges associated with using
cloud computing in education. The study [12] aims to
understand the significance of adopting cloud computing (CC)
in higher education institutions (HEIs). The paper discusses
the benefits of CC adoption in HEIs and analyzes the
challenges that may arise due to its adoption. The study
proposes early steps toward adopting CC while mitigating the
associated risks. The study is based on a systematic review of
various sources from different backgrounds and contexts. The
study identifies several factors that impact CC adoption in
HEIs, including administrative bodies and governments,
internal stakeholders, cloud suppliers, firm attributes, socio-
political changes, and IT infrastructure. The study suggests
opportunities for future research and offers insights for cloud
suppliers, advisors, governments, and academics to improve
their services in HEIs. [13] Focuses on the usability and
effectiveness of e-learning systems in education. The authors
used a systematic review of 99 articles from 2010 to 2018. The
results were analyzed using qualitative software, identifying
four dominant themes: education systems, learning issues,
student behaviors, and online learning tools. The study
provides research propositions that can be used in a theoretical
framework and proposes a new definition of e-learning. The
findings suggest that e-learning has the potential to bring new
opportunities for learning and teaching. Still, more research is
needed to address interoperability issues and assess the
usability of e-learning systems. [14] This study discusses
using cloud computing tools for collaborative learning in a
blended classroom. A review of 29 relevant studies
categorized the tools into synchronized, LMS, and social
networking tools and identified specific activities supported
by each type of tool. The review also highlighted the
opportunities and challenges of using these tools in a blended
learning context. The findings suggest that cloud computing
tools have the potential to enhance collaborative learning in
education and offer insights for educators and researchers
seeking to integrate technology into their teaching practices.
[15] Presents a systematic literature review and classification
of research related to applying multi-criteria decision-making
(MCDM) methods in evaluating the effectiveness of E-
learning. The review includes 42 papers published between
2001 and 2015 in 33 academic journals and international
conferences. The studies were classified according to the year
of publication, MCDM techniques used, and the journals and
conferences in which they appeared. The study identifies
significant criteria for evaluating E-learning. It provides
insights into the state-of-the-art MCDM application for E-
learning evaluation, which could be helpful for researchers
and practitioners in the field. The study [16] explores the
adoption of cloud computing in e-learning within universities
and institutes of higher education. Using a systematic
literature review, the paper identifies critical success factors
for implementing cloud-based e-learning, categorized into
four dimensions: cloud service resilience, university
technological maturity, university organizational readiness,
and cloud-based e-learning imperatives. The findings aim to
be helpful for policymakers and practitioners of e-learning in
implementing cloud-based e-learning platforms. Study [17]
evaluates the development of research on cloud computing for
education (CCE) and analyzes the empirical validation of the
literature. The study finds that the empirical investigations in
CCE are weak. The necessary scientific development of CCE
requires extending its scope of interest and involving scholars
synergistically to create and maintain a "common research
agenda." The systematic mapping study review identifies
research gaps. It suggests more effective research on the
production and use of content in CCE to support better
pedagogical developments and processes for better-quality
studies. The study [18] conducts a systematic literature review
to explore the current level of adoption of cloud computing in
the education systems of universities and higher education
institutions. The review identified seven empirical studies,
which found that many universities are interested in using
different cloud computing service models. However, there
needs to be more empirical research on using cloud computing
within educational institutions. The paper highlights the need
for more empirical studies in this research area. [19] It aims
to review and analyze the literature on cloud-based learning
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3339250
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

VOLUME XX, 2017 9
adoption in educational institutions and identify the most
frequent factors affecting its adoption. The study found 32
suitable articles from sources such as Science Direct, Emerald,
and IEEE and identified ease of use, usefulness, and security
as the most frequent factors. TAM, TOE, and UTAUT were
the most commonly used theories for adoption, and students
were the majority of respondents. The study recommends
future research to investigate adoption using UTAUT and to
incorporate security and trust.
These studies collectively provide a comprehensive overview
of the challenges, benefits, and opportunities associated with
the intersection of e-learning and cloud computing in
education, offering valuable insights for readers and
researchers.
Table 1 provides an overview of recent review studies on e-
learning and cloud computing, which have been discussed in
this section. It presents information on the main topics,
publication year, and the years covered in each study. The
varying perspectives in these reviews suggest that a more
thorough and methodical literature review is needed to address
some of the common shortcomings.
TABLE 1
A COMPARISON BETWEEN THE CURRENT STUDY AND PREVIOUS STUDIES
III. SYSTEMATICAL REVIEW METHODOLOGY
For a broader understanding of the interaction of e-learning
with cloud computing and to clarify the gap in this field, we
have provided the literature survey to be a guide for
researchers in this field to offer new additions and give a larger
picture to educational institutions, including the advantages
that cloud computing technology provides. Therefore, the
systematic review study was used to define, analyze, and
synthesize the evidence related to explicit research questions.
It is divided into three phases: planning, conducting, and
reporting the review [10]. Fig. 2 describes the general phases.
FIGURE 2. General Phases of Systematic Literature Review
The activities in the planning phase are to collect the necessary
documentation for the study, develop a study protocol, define
study questions, and evaluate the review protocol. The review
phase activities include primary research, data extraction,
quality assessment and monitoring, and data synthesis.
Finally, the reporting phase involves identifying dissemination
mechanisms, structuring the main report, and assessing the
information.
A. PLANNING THE REVIEW
Planning the review step is an essential stage in the systematic
review methodology, which involves developing a
comprehensive plan for the review process. The planning of
the review stage contains basics and activities for
summarizing the subject of study, defining objectives,
selecting appropriate search terms and databases to search for
primary information, inclusion/exclusion criteria, screening
and selecting studies, extracting data, and analyzing and
synthesizing the data.
1) IDENTIFYING THE NEED FOR A REVIEW
Context
[3]

[10]
2021
[11]

[12]

[13]
8
[14]

[15]

[16]

[17]

[18]
2016
Current study
Comparison based year
X
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Cloud development model
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Cloud services
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Taxonomy
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Affected area in e-learning
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Graphic representation
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Summarizing previous studies
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Open issue
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Futuretrends
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Limitations
X
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√
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This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3339250
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

VOLUME XX, 2017 9
Identifying the need for a review is the first step in the
systematic review methodology. It involves assessing the
existing literature to determine if a review is necessary,
defining the research question, and conducting a preliminary
search to determine the feasibility of the evaluation. This step
explores the importance of education and the search for ways
to spread it. Cloud computing offers additional benefits for e-
learning and systematic review publications in the field to
examine the limitations of this track. This step is essential in
the systematic review methodology.
2) REVIEW PROTOCOL
The review protocol step provides a detailed plan for the
research question, search strategy, inclusion and exclusion
criteria, data extraction and analysis, and dissemination of
results. It ensures the review's transparency, rigor, and
reliability, beginning with defining the research question,
developing a search strategy, establishing inclusion and
exclusion criteria, and creating a data extraction and analysis
plan. Ultimately, the review protocol ensures a thorough and
transparent systematic review, which others can replicate.
Therefore, we need to understand current cloud computing in
e-learning environments and define a classification for all
research in this field to analyze, compare, and discuss the
results.
3) RESEARCH QUESTIONS
Formulating research questions is crucial to a research study's
success as it provides direction and focus. Research questions
create a structure for the study and help the researcher develop
the research design and methodology. Poorly crafted research
questions can lead to a lack of focus and unclear objectives in
the study. Moreover, research questions are essential for
assessing the study's success, serving as the basis for the
research hypothesis, which is the initial explanation of the
research question. The researcher can determine whether the
survey accomplished its goals by answering the research
question and testing the hypothesis. This research gives the
ability to set the edge for aims review, allowing for the
reference inclusion and exclusion measures to be followed in
this research. The research questions of this study are as
follows:
 RQ 1: What is the distribution of the selected
studies regarding the year of publications,
publication source, type of papers, deployment,
service type, and e-learning elements?
Description: Definition of publication distribution
for the papers included in this study regarding
years, publication source, cloud environment, and
the impact on e-learning.
 RQ 2: What is the current research on e-learning-
based cloud computing environments?
Description: Focus on previously published
studies on e-learning in the cloud computing
environment.
 RQ 3: Which areas of e-learning are most
commonly used in cloud computing environments?
Description: Defining the impact of cloud
computing in terms of services and development
models and which one is more appropriate or used
in e-learning.
 RQ 4: Which cloud computing models
(deployment, service type, e-learning element) are
suitable for use in e-learning areas?
Description: More clarification of the interaction
of the cloud environment in terms of services and
development models with e-learning and the
suitability of the cloud environment for e-learning.
 RQ 5: What cloud computing service models (IaaS,
PaaS, and SaaS) are most regularly used in e-
learning?
Description: The services and development models
are mainly used and reflect their effects on e-
learning.
 RQ 6: What are the present study's potential future
research directions and limitations?
Description: Identification of research gaps, areas
of future research, and limitations of this study.
4) STUDY SELECTION CRITERIA
We prioritize quality assessment and precise article selection
criteria. Our selection process involves carefully examining
titles, keywords, and abstracts to identify relevant articles in e-
learning-based cloud computing. We apply various
publication types for comprehensive coverage and tailor
search queries to digital libraries' guidelines. Inclusion criteria
demand reliability, focus on e-learning elements, and
relevance to e-learning and cloud computing. Exclusion
criteria filter out unrelated, non-English, and short papers.
These rigorous criteria ensure our review relies on credible,
pertinent, and methodologically sound sources, enhancing the
credibility and validity of our findings in e-learning-based
based-cloud computing. Table 2 presents the origins and
search strings for primary studies. A broad query is created by
joining the terms. We have developed unique strings for each
digital library as the string formation guidelines of different
libraries vary. In this process, we followed the recent study
[19], which aimed to analyze software development practices
in cloud computing systematically. This research explores
perceptions and insights about the software development
process within cloud computing environments.
- INCLUSION CRITERIA
 Papers must be reliable in the field.
 Papers must be based on e-learning environment
elements.
 Papers must be in e-learning and cloud computing.
- EXCLUSION CRITERIA
 Papers must not relate to "cloud" and "e-learning."
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3339250
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

VOLUME XX, 2017 9
 Papers that must not be in English are excluded.
 Papers with less than five pages.
TABLE 2
SOURCES AND FORMULATED SEARCHES STRING
B. CONDUCTING THE REVIEW
First, define a set of keywords to be retrieved and begin the
search in the IEEE, Scopus, Springer, A.C.M., and Science
Direct databases. All papers related to cloud computing and e-
learning environments are retrieved. For full query, we use "e-
learning environment" or "framework" AND "cloud
computing."
1) SELECTION OF PRIMARY RESEARCH
After the initial search that collects a set of papers, an
automatic search from four sources, and a manual search of
the mentioned sources, we stop giving 50 unrelated results.
To improve the quality of the study, a set of tools is used to
analyze the survey according to a standard method. It consists
of three stages: the first is the definition and extraction of
articles; the articles are explored; and the last stage is the
extraction of reports. Fig. 3 describes the phases of conducting
the methodology.
The tools include EndNote, NVIVO, and Microsoft Excel.
These tools were pivotal in facilitating the research process,
notably enhancing our ability to manage, organize, and
analyze data effectively. By leveraging these tools, we aimed
to elevate the overall quality of our study, ensuring that it met
rigorous academic standards.
FIGURE 3. Summarizing the steps of conducting the methodology [20]
For the subject of the study, as shown in Table 3, after the
manual search, the studies are sorted based on the title and
abstract. The investigation is conducted according to the
criteria previously clarified in Fig.3 and Fig.4, showing the
Study selection process. The number of studies is then defined
based on the three stages. In Table 4, we explain the types of
studies that were included.
FIGURE 4. Process of study selection
TABLE 3
SEARCH QUERY RESULT
Database
Initial hits
By
Title
By
Abstract
Full text
IEEE
1,730
117
98
71
ACM
481
56
50
25
Springer
58,490
69
47
43
Science Direct
15,434
64
48
15
Total
154
2) STUDY QUALITY ASSESSMENT
The study relies on reliable sources to collect scientific papers,
such as Springer, IEEE, A.C.M., and Science Direct. And
filter the documents to ensure their relevance to the subject of
the study and read the full text.
3) DATA EXTRACTION AND MONITORING
We selected one paper after determining the relevant scientific
papers relevant to the topic. The chosen studies contain
information about the articles, such as authors, years,
countries, paper type, and publication source, to answer the
recognized questions. The applicable abstract standard to
conduct a systematic study to supply answers to a stated study
question Fig. 5 illustrates the classification scheme for data
synthesis.
Source
Search strings
URL
Springer
“e-learning based on cloud
computing” /“cloud computing” OR
“platform” OR “environment” AND
“e-learning” OR “lab”
www.springerlink.com
Elsevier
“e-learning based on cloud
computing” / “cloud computing”
AND “e-learning”
www.elsevier.com
A.C.M.
“e-learning in cloud computing
environment” OR “e-learning based
on cloud computing”
/ “cloud computing” AND “e-
learning”
www.acm.org
IEEE
Explore
“e-learning in cloud computing
environment” OR
“e-learning based on cloud
computing” / “cloud computing”
OR “platform” OR “environment”
AND “e-learning” OR “lab”
www.ieee.org
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3339250
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

VOLUME XX, 2017 9
FIGURE 5. Classification Scheme Data synthesis
The paper selection process represented in Fig.5 follows a
systematic approach to curate a relevant and representative
collection of papers for analysis in this study. The main
objective is to identify scholarly publications offering insights
into integrating cloud computing and e-learning
environments. This selection process is critical for ensuring
the quality and validity of the data used for analysis.
The process begins with the central focus on the main item,
"Cloud Computing E-Learning Environment." This represents
the overarching theme of the study, emphasizing the
intersection of cloud computing and e-learning.
The primary item branches into two sub-items (level two),
"Cloud Computing" and "E-Learning Environment," which
serve as the primary categories for paper selection.
Under the "Cloud Computing" sub-item (level three), papers
are sought that provide information and insights into different
aspects of cloud computing relevant to e-learning. This
includes (Level four) cloud models (such as public, private,
hybrid, and all related models) and cloud service (SaaS, PaaS,
IaaS, SOA, and all services) that impact the delivery and
management of e-learning content.
Under the "E-Learning Environment" sub-item, a diverse
range of elements related to the e-learning landscape are
considered. These include the architectural design of e-
learning platforms, software applications used for content
delivery and interaction, hardware infrastructure supporting e-
learning, performance optimization strategies, security
protocols, storage solutions, network connectivity, and
overarching general aspects of the e-learning environment.
C. REPORTING THE REVIEW
At this stage, the results are presented in graphs, through
which we can answer the questions posed above and discuss
and analyze the results.
Fig.6 shows the distribution number of papers published from
2010 to 2022; we can note that the lowest number of articles
was published in 2022, with only two articles. This is due to
the difficulty of accessing new papers. Most of the articles are
from 2014 to 20202, which is evidence of a recent interest in
this area of research.
FIGURE 6. Publication by year
FIGURE 7. Distribution of articles by database sources.
Fig.7 shows the distribution of the selected articles by database
sources. We find 71% in the IEEE database, 43% in the
Springer database, 25% in the A.C.M. database, and 15% in
the Science Direct database.
4
12 13 13
20
16
9
19 16 15
10
52
0
5
10
15
20
25
PUBLICATION BY YEAR
71, 46%
25, 16%
43, 28%
15, 10%
Articles by database sources
IEEE
ACM
Springer
Elsevier
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3339250
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

VOLUME XX, 2017 9
FIGURE 8. Distribution publication source
Fig.8 shows the distribution of the publication sources. A
variety of research data sources were used, including journals
(24%), conferences (66%), workshops (2%), and book
chapters (12%). Table 4 shows the variety of publications per
specified sources.
It was classified based on a study conducted in collaboration
with IEEE that explained a set of criteria for categorizing
scientific papers related to computer science. Fig. 9 and Table
8 refer to the primary study classification; it was organized
into six types of research:
 Evaluation research: (survey, case study,
experiment, and field study).
 Validation research: (mathematical proof of
properties, experiments, simulation, mathematical
analysis, prototyping).
 A proposal of a solution: discussing a new
technology solution or an improvement to an old
technology.
 Philosophical papers: such as frameworks, new
concepts, etc.).
 Opinion paper: clarifies the author's opinion on
some things, good or bad.
 Personal experience: it discusses a topic of concern without
focusing on research methods and contains the results of
experiments.
FIGURE 9. Distribution of papers by classification
FIGURE 10. Distribution of type of service
Fig.10 shows the distribution service of cloud computing.
The results show that in the percentage of the paper that
discusses all benefits in general (53%), software as a service
(18%), infrastructure as a service (17%), service-oriented
architecture (2%), and platform as a service (10%).
FIGURE 11. Distribution of model of cloud
Fig.11 depicts the outcome of keywords for a research
question: “Cloud computing models used with an e-learning
environment." Public cloud (74%), hybrid cloud (3%),
27
316 26 82
050 100
1
Type of service
ALL
IaaS
PaaS
SOA
SaaS
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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3339250
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VOLUME XX, 2017 9
private cloud (12%), and all (11%) are the most popular
models.
FIGURE 12. Distribution of papers discussing the effect of e-learning
Fig.12 illustrates that most studies focus on architecture
(27%). Most of the studies have reviewed a structure of e-
learning in cloud computing consisting of three layers, and
there are also proposals for five and seven layers. The
general (21%) statement represents some of the studies that
discussed the definition, advantages, and challenges of e-
learning on cloud computing without going into the details
of the environment. Software 19% of many studies have been
applied as a software process for e-learning that works on
cloud computing, such as learning management systems and
others listed under the section Software. Some studies
discussed other factors of the e-learning environment, such
as improving performance, monitoring programs and
increasing the speed of the performance (18%), the virtual (6
%) environment, and less on security (4%) following it in
storage (2%) following it in the network (2%) following it in
Hardware (1%). The results in Tables 3 and 4 showed that
the selected primary studies in Fig.10 showed a map of
focus areas in research on e-learning in cloud computing
environments by e-learning elements and cloud computing
models. Table 7 refers to the primary study classification,
and Table 9 summarizes all included studies.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3339250
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

VOLUME XX, 2017 9
TABLE 4
DISTRIBUTION OF PRIMARY STUDIES BY THE PUBLISHER’S TYPE
Publisher
Number of
Publications
%
Type of publication
Evaluation
Opinion
Philosophical
Validation
Proposal solution
Personal experience
Springer
43
28%
17
11
6
9
-
-
IEEE
71
46%
24
9
16
17
4
1
A.C.M.
25
16%
4
5
10
6
-
-
Elsevier
15
10%
4
2
2
4
3
-
Total
154
100%
49
27
34
36
7
1
TABLE 5
DISTRIBUTION OF DEPLOYMENT CLOUD COMPUTING MODEL
Cloud computing
Deployment model
Acronym
Number of
publications
%
References
Public cloud
Public cloud
114
74%
[21],[22],[23],[24],[25],[8],[26],[27],[28].[29],[30],[31],[32],[33],[5],[34],[35],[36],[
37],[38],[39],[40],[41],[42],[43],[44],[45],[46],[47],[48],[49],[50],[1],[51],[52],[53],
[54],[55],[56],[57],[58],[59],[60],[61],[62],[63],[64],[65],[66],[67],[68],[69],[70],[7
1],[4],[72],[73],[17],[74],[75],[76],[77],[78],[7],[79],[80],[81],[82],[83],[84],[85],[8
6],[87],[88],[89],[9].[90],[91],[92],[93],[94],[95],[96],[97],[98],[99],[100],[101],[10
2],[103],[104],[105],[106],[13],[50],[107],[107],[108],[109],[110],[111],[112],[113]
,[114],[115],[116],[117],[118],[119],[120],[121],[122],[123], [124]
ALL model
ALL
17
11%
[125],[126],[127],[128],[129],[130],[131],[132],[133],[134],[135],[136],[137],[138]
, [139],[140]
Privet cloud
Private cloud
18
12%
[141],[142],[143],[144],[145],[146],[147],[148],[149],[150],[151],[138],[120],[152]
,[153],[154],[50],[155],[156]
Hybrid cloud
Hybrid cloud
5
3%
[157],[158],[159],[160] ,[161]
TABLE 6
DISTRIBUTION OF CLOUD COMPUTING SERVICE TYPE
cloud computing service
Acronym
Number of
publications
%
References
ALL service
ALL
82
53%
[21],[157],[8],[26],[27],[158],[29],[32],[33],[5],[34],[35],[36],[38],[40],[41
],[42],[43],[44],[45],[125],[49],[126],[127],[128],[129],[1],[51],[52],[53],[5
4],[55],[56],[58],[59],[60],[61],[62],[63],[64],[65],[65],[68],[70],[4],[72],[7
3],[77],[7],[143],[80],[81],[84],[86],[88],[89],[9],[90],[91],[92],[93],[95],[1
61],[96],[99],[100],[162],[131],[132],[107],[108],[109],[119],[121],[122],[
135],[136],[137],[124],[138],[126],[140],[139]
software as a service
SaaS
27
18%
[24],[28],[30],[37],[47],[57],[66],[69],[74],[75],[76],[79],[83],[145],[146],[
94],[101],[102],[104],[130],[13],[110],[112],[114],[115],[116],[123]
platforms as a service
PaaS
16
10%
[23],[163],[46],[159],[78],[97],[98],[147],[105],[106],[133],[111],[118],[13
4],[151],[153]
infrastructure as a service
IaaS
26
17%
[25],[31],[39],[48],[50],[160],[67],[142],[82],[85],[87],[144],[103],[50],[11
3],[117],[120],[148],[149],[150],[138],[120],[152],[154],[50],[155],[156],
Service Oriented
Architecture
S.O.A.
3
2%
[141],[22],[71],
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3339250
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VOLUME XX, 2017 9
TABLE 7
DISTRIBUTION OF E-LEARNING ELEMENT ENVIRONMENT
TABLE 8
DISTRIBUTION STUDIES BY THE PUBLISHER’S TYPE
IV. DISCUSSION
In this section, we will discuss e-learning and its interaction
with its cloud computing environment, review the services
provided and their impact on e-learning, and present and
discuss the results of the studies. In this study, we clarify the
interaction between e-learning and cloud computing through
a survey of studies from 2010 to 2022, including e-learning
and cloud computing. The summary of all selected studies is
shown in Table 9.
A. CLOUD COMPUTING SERVICE
In answer to the question of cloud service, in terms of
services, it was found that most of the studies dealt with
all the services in the study because there was an overlap
between the services. The studies included proposals or
a framework that consists of all services with the highest
percentage (53%), followed by software as a service
(18%), then infrastructure as a service (17%), Platforms
as a Service (10%), and finally, Service Architecture
(2%) as shown in Fig. 10.
E-learning effect
Number of publications
%
References
Architecture
42
27%
[21],[22],[157],[25],[33],[35],[37],[163],[41],[45],[1],[51],[52],[55],[59],[60],[61],[62],[63],[68
],[70],[71],[4],[72],[73],[142],[7],[81],[89],[90],[161],[96],[97],[131],[148],[135],[136],[124],[1
38],[156],[140]
General
33
21%
[8],[26],[158],[29],[32],[5],[36],[43],[44],[125],[126],[127],[128],[53],[56],[17],[77],[80],[84],[
86],[88],[9],[91],[92],[93],[95],[99],[100],[13],[108],[109],[121],[126],[139]
Software
30
19%
[24],[28],[30],[42],[46],[47],[57],[58],[66],[69],[74],[76],[79],[143],[83],[144],[145],[146],[94],
[98],[101],[102],[104],[162],[130],[110],[112],[113],[114],[115],[116],[119]
Performance
27
18%
[34],[38],[40],[49],[50],[129],[160],[64],[65],[65],[75],[82],[105],[106],[132],[133],[107],[111],
[117],[122],[123], [149],[137],[150],[151],[138],[120]
Virtual
10
7%
[23],[159],[147],[50],[118],[120],[152],[153],[154],[50],[155]
Security
5
3%
[39],[54],[78],[87],[134]
Network
3
2%
[48],[67],[103]
Storage
3
2%
[27],[31],[85]
Hardware
1
1%
[141]
Type of
publication
Number of
publications
%
References
Evaluation
research
49
32%
[141],[23],[24],[32],[163],[39],[40],[46],[47],[159],[127],[129],[56],[57],[63],[66],[69],[73],[17],[142],
[75],[76],[143],[80],[83],[85],[88],[9],[144],[145],[146],[164],[162],[106],[13],[133],[110],[112],[114],
[115],[116],[117],[119],[121],[123],[153],[154],[155],[156]
Opinion paper
27
17%
[8],[30],[5],[43],[44],[125],[1],[52],[53],[55],[58],[70],[72],[7],[79],[84],[90],[91],[95],[161],[96],[99],
[100],[108] ,[113],[124]
Philosophical
paper
34
22%
[21],[22],[25],[26],[28],[158],[33],[35],[37],[45],[48],[50],[128],[160],[51],[59],[60],[61],[62],[64],[65
],[68],[4],[77],[81],[89],[94],[97],[130],[50],[148],[135],[136],[138]
Proposal of
solution
7
5%
[157],[126],[147],[103],[131],[132],[107]
Personal
experience
1
1%
[49]
Validation
research
36
23%
[27],[29],[31],[34],[36],[38],[41],[42],[54],[71],[74],[78],[82],[86],[87],[92],[93],[98],[101],[102],[105
],[109],[111],[118],[120],[122],[149],[134],[137],[150],[151],[120],[152],[50],[140] ,[139]
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3339250
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

VOLUME XX, 2017 9
B. CLOUD COMPUTING DEVELOPMENT MODEL
In answer to the question of cloud development models,
the public cloud was the most common (74%), and the
private cloud (12%), followed by all the models (11%),
and then the hybrid cloud (3%), as shown in Fig. 11.
C. IMPACT ON E-LEARNING
To answer the third question, which is about the impact
of e-learning, we discovered that most studies
challenged the architecture or structure (27%), followed
by a study that was published in general about e-learning
in cloud computing (21%), followed by studies of
software development in education (19%), and another
on performance (18%), followed by the virtual with
(6%), then security with (4%), then storage and
networks with (2%) studies for each, and the last in hard
disk storage.
D. INTERACTION OF CLOUD SERVICES AND THE
IMPACT ON E-LEARNING
The interaction of cloud services and the impact on e-
learning is shown in Fig. 13, and Table 6 refers to the
primary study. The highest percentage of cloud
computing services was about architecture, which
includes all services, followed by performance, and
there needs to be more practical and interactive
applications of e-learning, recommendation systems,
artificial intelligence, and business intelligence.
Platforms as a Service reported the highest performance
among others, followed by virtual infrastructure, labs,
virtual machines, and programs for development on
them, such as programming languages and others, have
a dearth of matching software that does not run on the
cloud, enhancing security, measuring quality, and
evaluating performance. Infrastructure as a service has
the highest performance percentage, followed by
networks, security, and storage. There needs to be a
more practical experience to measure service stability
with increased demand and migration of virtual
machines and green computing.
FIGURE 13. The interaction of cloud service with e-learning
FIGURE 14. The interaction of the cloud model with e-learning
E. INTERACTION OF CLOUD COMPUTING
MODELS AND IMPACT ON E-LEARNING
In the interaction of cloud computing models and their
impact on e-learning, the highest percentage was for
software and infrastructure in the public cloud. There
needs to be more private and hybrid clouds, as shown in
Fig. 14 and Table 5, referring to the primary study  We
rely heavily on the public cloud in most studies in order
to reduce costs and the ability to connect with distributed
and remote places, but the public cloud is less secure
than the hybrid and private clouds. The hybrid cloud
represents a solution to reduce costs and enhance
security where the educational institution is linked from
the inside with a private cloud, connects with other
institutions, and provides services through the public
cloud.
V. APPLICATIONS AND SYSTEMS USED IN CLOUD
COMPUTING BASED ON E-LEARNING
In the world of e-learning based on cloud computing,
several typical applications and systems are widely used
to facilitate online education and training. These
applications and systems allow institutions and
educators to deliver content, engage with learners, and
effectively manage various aspects of e-learning.
Learning Management Systems: Many companies
and e-learning platforms have moved to provide their
services through cloud computing, such as Google
Educational Cloud, Microsoft Education Cloud,
Moodle, Blackboard, Sakai, Cisco Collaborative
Knowledge, Braidio Collaborative Learning Platform,
Echo 360 Active Learning Platform, N2N Illuminate
Services Student Engagement Platform, rSmart
OneCampus, WizIQ, Engrade, Wiggio, Edmodo,
Wikispaces, Classdojo, Snapwiz, and DocentEDU and
GoClass [165].
Mobile Learning Applications: Popular mobile
learning applications like Coursera, edX, Khan
Academy, and Duolingo enable learners to access
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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3339250
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VOLUME XX, 2017 9
educational content on smartphones and tablets,
promoting flexible and on-the-go learning. In addition,
there are educational game apps for children,
professional development apps like LinkedIn Learning,
and university-specific apps for course management and
resources.
Video Conferencing Tools: Many tools can be used for
synchronous learning system applications, including
Zoom, Microsoft Teams, Google Meet, and Adobe
Connect, to facilitate real-time video conferencing,
allowing educators to conduct virtual lectures, webinars,
and interactive sessions.
Cloud-Based Learning Analytics: Some analytics
platforms can help educators make data-driven
decisions to improve courses. These include Learning
Locker and Watershed, which collect and analyze
learner performance, engagement, and behavior data.
Open Educational Resources Repositories: Platforms
like MERLOT, OER Commons, and OpenStax host a
vast collection of free and open educational resources
that educators can incorporate into their courses. The
analysis revealed significant variations among these
repositories in terms of metrics. These platforms attract
millions of users monthly, with visitors worldwide,
while others have a more localized audience [166].
Cloud Storage and File Sharing: Cloud storage and
file sharing are essential components of modern digital
life, offering convenient and secure ways to store,
access, and share files and data over the internet. Cloud
storage services, including Google Drive, Dropbox, and
Microsoft OneDrive, store, share, and collaborate on
documents, presentations, and other course materials.
They ensure easy access to resources from anywhere.
E-learning Recommender System: A cloud-based e-
learning platform aims to implement a recommender
system utilizing Google Cloud services. This system's
primary function is to suggest suitable courses to
students based on their individual needs and
preferences, ultimately motivating them to make
informed course selections [167].
VI. OPPORTUNITIES, OPEN ISSUES, AND
CHALLENGES
With the increasing sources of information, the Internet of
Things (IoT) is attaining its scope daily in the near future.
Data generated by such an extensive set of dissimilar
machines stored in massive data, the appearance of fog
Computing goals to less data that needs for conveyed in a
cloud for the procedure plus storage on fog computing, for
generally improved system efficiency a resource is
transferred to the network border. Technological evolution
supports several learning organizations. 2018 University of
Parma, Riccardo Pecori showed a new e-learning structure
that joined Cloud Computing over fog computing plus big
data. It develops the cloud on the e-learning stage by the fog
computing competencies over inner APIs wherever the cloud
appearance a primary function in sequential back up with a
big storage operator in adding the mining mechanism
requiring broad time.
The fog mechanism emerges light dole out NoSQL storage
services, which are used to realize interim predictions above
the light mining mechanism.
Utilized the outer APIs to compute architecture and give
layout with the learning cloud users such as educators,
teachers, and learners to attain the rise interactivity and give
beneficial proposal [33].
Despite the effectiveness of cloud computing in education,
its application has some challenges. The most important of
these challenges are technical, bandwidth, and security. And
non-technical, such as Charge, user concept, educational
forms and methods, management rules for education, and
resource development [124]. Organizations face some
challenges when converting from a traditional e-learning
system to a system that works in a cloud environment, and
the challenge is how to convert. Is it to go to a payment
service provider or switch through the available resources?
Also, students and users may suffer from problems of delays
in accessing cloud education systems, and therefore, the
dimension of cloud centers and problems of delays in the
network. Among the most critical challenges are some
lectures and laboratory materials unsuitable for the cloud
computing environment, which is challenging to implement.
The cloud is inappropriate for some programs that need
special devices or tools, such as robots, digital forensics, and
other programs. The presence of packages and programs that
do not fit the cloud environment and the fact that there are
no versions of them suitable for the cloud environment could
be improved in some of the interaction tools in the cloud.
Vendor lock-in problems are a concern for everyone who
deals with cloud computing.
VII. FUTURE TRENDS
Cloud computing has contributed to finding solutions to
many e-learning problems. For example, there were
limitations in practical application and access to remote
laboratories and a need to adapt some materials in practical
application. During the survey, many studies provided
feasible and practical solutions.
A. BUSINESS INTELLIGENCE AND ARTIFICIAL
INTELLIGENCE
Business intelligence in e-learning can be through
dashboards for reports to help decision support and manage
the data in the cloud, recommendation systems, and artificial
intelligence techniques such as facial recognition and natural
language processing that support more significant interaction
with learning management systems.
Artificial intelligence techniques can help improve the online
course content design because the course design is difficult
and time-consuming and does not adapt to every employee's
specific requirements. To overcome the limitations of
educational systems, business intelligence and artificial
intelligence techniques can be excellent choices to support e-
learning systems.
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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3339250
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VOLUME XX, 2017 9
B. PRIVATE CLOUD
Here, we discuss cloud computing as a specialization or
subject within the field. Private clouds in educational
institutions provide students with an environment for
applying research and projects related to cloud computing as
a path in information technology. A private cloud enables the
actual application in the infrastructure and prepares
containers for platforms as a service. The private cloud can
provide and attain scalability, persistent storage, distributed
access, effective resource utilization and management, and
interoperability of the e-learning system. In addition, it can
support maximizing the resource with more efficiency and
control by avoiding bottlenecks and downtime, ensuring the
stability of the e-learning system for a long time with
reasonable control of the system. In addition, using a private
cloud can offer a high-security environment instead of an
unsecured internet connection.
C. PERFORMANCE
Improving performance in cloud computing would enhance
the learning services provided through the cloud. Using
machine learning algorithms to schedule tasks and distribute
the load balance improves performance, response speed, and
processing, whether parallel or multiple processing. This is a
fertile field for study, research, and development
D. QUALITY SERVICE
Service providers compete to provide quality service to keep
customers and attract more trust in service level agreements
(S.L.A.s). Conduct experiments to evaluate trials, service
stability, and change factors. The quality of e-learning can
focus on several aspects of quality service that lead to the
satisfaction of the students, e-learning system quality, and
loyalty of students to the e-learning system, e-learning
instructor, and course materials. Quality and e-learning were
administrative. These factors can increase and directly affect
e-learning service quality and student satisfaction.
E. DEVELOPMENT
Harmonization of software and open-source cloud operating
systems such as OpenStack competes with commercial
companies in performance and is considered one of the best
cloud operating systems. However, there are some areas for
improvement, such as Internet of Things sensors and
artificial intelligence.
F. GREEN INTERNET OF THINGS
IoT devices need massive Power for tremendous and
effective performance to support a sophisticated
environment for e-learning organizations. This primary
concern has earned enormous attention in the forthcoming
investigation research.
Green computing can help reduce Power and energy, which
are the main points in designing and implementing the future
computing system. Green computing solutions can provide
the organization's e-learning system with friendly and less
energy consumption.
The Green Internet of Things (GIoT) is a solution for the
protection and sustainability of such issues. GIoT represents
the framework of connecting smart sensors and devices and
creating automation by enabling energy conservation
methods. Sensors monitor students and control virtual
classrooms and other tasks that help in education.
Monitoring energy depletion and energy use variability is
one of the priorities for the next stage. Integrating green
Internet of Things (GIoT), technologies would be performed
in the best potential manner and improve the e-learning
system environment to be friendly and less energy-
consuming.
G. BLOCKCHAIN AND METAVERSE
Blockchain is a distributed, unchanging record that enables
recording transactions and tracking resources. On the other
hand, the metaverse is a massive structure that has many
digital aspects. There are several advantages to the
Metaverse globe, such as interaction, authenticity, and
portability. Using blockchain and the metaverse can benefit
e-learning's future development, such as guaranteeing data
security and privacy in the e-learning environment. Another
thing can be ensuring the quality of the e-learning process
and offering data Integrity in different backgrounds. This
integration can promote a new feature to e-learning progress.
VIII. LIMITATIONS
In this study, we analyzed an e-Learning cloud computing
environment. The study has some limitations, such as the
selection of database sources. There are many sources for
publishing scientific papers. However, this study focused on
only four reliable sources to collect articles (Springer, IEEE
Xplore, A.C.M., and Elsevier).
Many terms related to e-learning, such as mobile learning,
massive open online courses (MOOC), continuing learning,
micro-learning, and types of e-learning, are added to the
techniques used with e-learning and are focused on the e-
learning environment. Several factors may impact the
findings, such as whether the contents were only partially
analyzed or the study's researchers.
The study's research questions focused on the impact of e-
learning and cloud computing services and models in
scientific papers related to the subject. This limited scope
may have excluded other potential research questions that
provided additional insights into the topic.
The study only analyzed papers written in English, which
may exclude relevant research published in other languages.
This could limit the generalizability of the findings or miss
essential insights. While the study analyzed a range of topics
related to e-learning and cloud computing, it may have
overlooked specific topics or issues relevant to particular
contexts or stakeholders. For example, the study did not
mention the role of teachers or instructors in e-learning
environments or the impact of e-learning on student
outcomes.
We can give some suggestions to address the limitations:
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3339250
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

VOLUME XX, 2017 9
To enhance the study's comprehensiveness, consider
expanding the selection of database sources beyond the four
mentioned (Springer, IEEE Xplore, A.C.M., and Elsevier).
Additional reputable databases can help capture more
relevant scientific papers and provide a more holistic view of
the topic.
We are incorporating a broader spectrum of e-learning-
related terms beyond those analyzed in the study, such as
mobile learning, MOOCs, micro-learning, and continuing
education. This expansion can offer a more comprehensive
analysis of the subject matter.
To expand the scope of the study, explore additional research
questions beyond the impact of e-learning and cloud
computing services and models. This can uncover new
dimensions and perspectives within the field and provide a
more nuanced understanding of the subject.
Acknowledge that the impact of e-learning and cloud
computing can vary across different educational contexts,
and consider investigating specific issues or stakeholders,
such as the role of teachers or instructors and the effects on
student outcomes.
TABLE 9
SUMMARY OF INCLUDED STUDIES
NO
Ref no
Summarized
year
type of paper
1. \
[21]
It provides the
integration of e-learning
reality with cloud
computing and the
design of a framework
consisting of the learner,
mixed reality, learning
tools, and methods, and
finally, management
using easy tools
2017
Philosophica
l paper
2.
[141]
We are implementing
cloud computing
technology for four
universities upon request
and providing many
services, such as
laboratory resources and
a dedicated work
environment.
2012
Evaluation
research
3.
[22]
Explore the benefits of
cloud computing for
Improving the learning
environment through a
set of integrated virtual
technologies
2012
Philosophica
l paper
4.
[157]
A proposal to use
electronic materials in
the educational
community through
cloud computing in an
effective manner and at a
lower cost
2014
Proposal of
solution
5.
[23]
It discusses the
implementation of
simulation-based
education for students of
engineering, energy, and
technology who face
2019
Evaluation
research
problems in practical
application over the
Internet
6.
[24]
Proposal for integrating
e-learning standards with
the cloud computing
platform
2011
Evaluation
research
7.
[25]
A directed model based
on cloud computing and
electronic learning
systems based on smart
engine management and
Scrum standards
2017
Philosophica
l paper
8.
[8]
Present the challenges
and benefits of cloud
computing in e-learning
2014
Opinion
paper
9.
[26]
A model for ensuring the
success of using the
cloud computing
environment in
education and defining
the most critical
standards in the
education system
2013
Philosophica
l paper
10.
[27]
Provides a storage
service for educational
platforms via cloud
computing
2014
Validation
research
11.
[28]
A proposal for a scrum
standard-compliant
model offering
consistent content
management and lower
storage cost
2011
Philosophica
l paper
12.
[158]
An e-learning model
based on a hybrid cloud
with the integration of
educational, technical,
and general requirements
2013
Philosophica
l paper
13.
[29]
It reviews an experiment
to convert a web-based
educational system to
cloud computing. The
experiment involved 500
participants
2017
Validation
research
14.
[30]
Developing a model for
providing educational
programming services
through cloud computing
2013
Opinion
paper
15.
[31]
model suggests cloud
computing for semantic-
based content storing of
E-Learning materials
2013
Validation
research
16.
[32]
Use the collaborative
Google platform as a
case study for applying
cloud computing in an e-
learning environment
2010
Evaluation
research
17.
[33]
An illustration of the
current state of e-
learning in cloud
computing and existing
technologies, such as fog
computing
2018
Philosophica
l paper
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3339250
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

VOLUME XX, 2017 9
18.
[5]
Explain some of the
solutions provided by
cloud computing in
terms of scalability and
security for e-learning
advantages and
disadvantages
2013
Opinion
paper
19.
[34]
Clarify the current state
of e-learning in the cloud
computing environment
and provide
recommendations for
improvement
2016
Validation
research
20.
[35]
Analyze essential
requirements and
technologies and build a
standard architecture for
educational cloud
computing
2014
Philosophica
l paper
21.
[36]
A model using cloud
computing that allows
universities and schools
to share educational
resources at the lowest
cost
2016
Validation
research
22.
[37]
The importance of using
cloud computing for
organizations and
learners illustrates the
benefits and design
offerings
2016
Philosophica
l paper
23.
[38]
Clarify and solve e-
learning problems using
cloud computing to
participate in lifelong
learning
2012
Validation
research
24.
[163]
Demonstrates a range of
innovations that can be
applied to current cloud
computing to
accommodate cloud-
based education
platforms
2012
Evaluation
research
25.
[39]
Use simulation software
such as Cloudsim. This
is for students to gain
practical experience and
understanding of aspects
of cloud computing
2016
Evaluation
research
26.
[40]
It demonstrates the
success of the open
learning experience
based on cloud
computing in terms of
file sharing and ease of
access
2014
Evaluation
research
27.
[41]
The Education
Everywhere Proposal
The study included all
previous studies in
Indonesia and then
proposed an open
learning environment
based on cloud
computing
2016
Validation
research
28.
[42]
Submit a proposal for
open education in
Indonesia that relies on
cloud computing to
reduce educational
2014
Validation
research
disparities and bridge the
technological gap
29.
[43]
The possibility of
adopting cloud
computing services in
education is discussed
and called "Teaching and
Learning as a Service."
2012
Opinion
paper
30.
[44]
It studies an e-learning
ecosystem that supports
the modern era, the
integration of web
technologies and cloud
computing
2011
Opinion
paper
31.
[45]
It studies an e-learning
ecosystem that supports
the modern era, the
integration of web
technologies and cloud
computing
2015
Philosophica
l paper
32.
[46]
Providing a cloud
desktop for engineering
students that supports
their requirements
2014
Evaluation
research
33.
[47]
Providing software as a
service for education and
supporting the economy
through service pricing
2010
Evaluation
research
34.
[48]
Providing a virtual
environment for
universities through
cloud computing,
providing mobile and
decentralized
2014
Philosophica
l paper
35.
[125]
A descriptive analysis to
determine the
importance of e-learning
and its factors in cloud
computing
2020
Opinion
paper
36.
[49]
The use of cloud
computing in co-
education through an
educational platform and
performance evaluation
2017
Personal
experience
37.
[50]
A framework for
improving performance
in virtual laboratories
and quality of services
2015
Philosophica
l paper
38.
[159]
Hybrid cloud design in
e-learning and the use of
programs such as
C.M.S., WordPress, and
programming languages
such as PHP
2019
Evaluation
research
39.
[126]
A proposal for higher
education in Nigeria
based on a cloud
computing infrastructure
using the theory of DOI
and T.O.E.
2019
Proposal of
solution
40.
[127]
The results of a
questionnaire for
classroom learners on
the Google platform,
which included 255
learners, are discussed
2020
Evaluation
research
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3339250
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

VOLUME XX, 2017 9
and analyzed using
S.P.S.S.
41.
[128]
A proposal that allows
increased cooperation
creates a basis for
building an intelligent
system and contributes
to innovation
2020
Philosophica
l paper
42.
[129]
A proposal for intelligent
modeling of cloud
computing resources in
e-learning systems
through the MS2CRAE
algorithm
2020
Evaluation
research
43.
[160]
Experimenting with a
hybrid cloud application
in e-learning and costing,
where 30% of the cost is
saved
2011
Philosophica
l paper
44.
[1]
The study presents the
pros and cons of cloud
computing in education,
health, and agriculture
2018
Opinion
paper
45.
[51]
Integration of an
educational web
environment with cloud
computing using a tool
called JBraindead
2013
Philosophica
l paper
46.
[52]
A partial learning
platform designed to
support lifelong learning
that uses mass cloud
storage
2011
Opinion
paper
47.
[53]
The benefits and
limitations of Cloud
Computing in the
education sector are
presented
2014
Opinion
paper
48.
[54]
A survey of historical
studies and a
questionnaire on the
factors of confidence in
cloud-based e-learning
2017
Validation
research
49.
[55]
the implementation of
new technologies for
education, such as cloud
computing with teacher
training to contribute to
improving education
2017
Opinion
paper
50.
[56]
Obstacles and solutions
for full implementation
of cloud computing
2016
Evaluation
research
51.
[57]
A collaborative
intelligent learning
management system
called a smart cloud to
support resource
management
2018
Evaluation
research
52.
[58]
Transitioning from
traditional learning
management systems to
learning management
systems operating in the
cloud computing
environment
2019
Opinion
paper
53.
[59]
Concepts for applying
cloud computing in the
education sector in India
2016
Philosophica
l paper
54.
[60]
A proposal to apply
cloud computing and big
data technologies in
education, discovering
the advantages and
disadvantages
2017
Philosophica
l paper
55.
[61]
Clarify the steps needed
to create a quality virtual
learning environment
and use Moodle to study
the case
2014
Philosophica
l paper
56.
[62]
essential factors
affecting users in
accepting classroom
computing and use of
cloud computing in
Thailand
2018
Philosophica
l paper
57.
[63]
Building a Bridge
Connecting Learning
Analytics
Dashboards (L.A.D.s)
and Open Learner
Models (OLMs) to
conduct a study and
present the results
2018
Evaluation
research
58.
[64]
An online platform for
the Malaysian
government (MOOC)
called ArmadaNet uses
the Moodle model
2011
Philosophica
l paper
59.
[65]
it reviews the G.O.D.
project for managing
educational data in a
cloud computing
environment
2013
Philosophica
l paper
60.
[66]
I put forward a system
called Protus that works
on cloud learning
systems that identify
learners' behaviors and
learning styles using
classification
mechanisms and make
use of them in
recommendations
2017
Evaluation
research
61.
[67]
Provide a model for the
application of cloud
computing in the
university system in
terms of structure and
environment for
implementation
2020
Opinion
paper
62.
[68]
It offers a cloud service
provider architecture that
enables the efficient and
effective provision of
educational services to
improve education
2014
Philosophica
l paper
63.
[69]
A proposal that supports
the exchange of
knowledge and the
creation of a community
to exchange information
and educational
experiences in the cloud
environment
2014
Evaluation
research
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3339250
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

VOLUME XX, 2017 9
64.
[70]
It raises some of the
advantages and
challenges of cloud
computing in the field of
education and solutions
for its application in
higher education
institutions
2019
Opinion
paper
65.
[71]
A training model that
aims to provide
educational materials in
short periods that suit
workers through micro-
education and integrating
them with learning
management systems
2020
Validation
research
66.
[4]
A framework of six
layers that can be
implemented in
educational institutions
located in developing
countries
2019
Philosophica
l paper
67.
[72]
A detailed illustration of
the shared infrastructure
for e-learning systems in
the cloud environment
with actual examples
2012
Opinion
paper
68.
[73]
Applying the virtual
learning environment in
three stages and
displaying the results for
each stage
2015
Evaluation
research
69.
[17]
Clarify the actual
experiences of applying
cloud computing in
education. The study
included 27 studies
2015
Evaluation
research
70.
[74]
Implementing a SaaS
presentation platform
called E.D.I.S.O.N. that
provides computerized
design software
2018
Validation
research
71.
[142]
Developing a private
cloud that allows sharing
the university's resources
and linking the various
systems with each other
2017
Evaluation
research
72.
[75]
Directing cloud
computing in education
to solve some
companies' problems,
such as lack of resources
and knowledge gaps
2014
Evaluation
research
73.
[76]
Courses covering cloud
computing concepts ten
years ago explored the
existence of
shortcomings compared
to the impact of cloud
computing
2017
Evaluation
research
74.
[77]
It demonstrates the
importance of integrating
and combining
educational components
with cloud computing
technology to reduce
costs and increase
storage and security
2015
Philosophica
l paper
75.
[78]
This study presents
descriptive census results
to clarify the advantages
and limitations of using
cloud computing
services as a commodity
in educational platforms.
2019
Validation
research
76.
[7]
It provides an analysis of
the limitations facing e-
learning and provides
some solutions to suit the
cloud computing
environment
2017
Opinion
paper
77.
[79]
It explains cloud
computing in universities
and how information and
resources are managed
2012
Opinion
paper
78.
[143]
Providing a private cloud
that meets the needs of
universities and the
requirements of e-
learning
2011
Evaluation
research
79.
[80]
The use of cloud
computing in higher
education in developing
countries such as
Thailand. The study
included collecting data
from two universities
and analyzing and
presenting the results.
2015
Evaluation
research
80.
[81]
Showcasing online
educational platforms
and explaining how they
can contribute to
educational efficiency
and sustainability
2012
Philosophica
l paper
81.
[82]
Enhance security in the
cloud by encrypting
messages before storing
them and saving
encrypted messages in
the storage center for e-
learning systems
2018
Validation
research
82.
[83]
A study about
Armangarayan Company
in Iran is providing e-
learning services: cloud
computing was used in
Ayandeh Bank. The
study aimed to find out
the results of this
experiment.
2017
Evaluation
research
83.
[84]
Providing the
infrastructure for cloud
computing and using it
in the educational field,
specifically in
universities
2013
Opinion
paper
84.
[85]
Focuses on using cloud
computing in developing
countries in the field of
education and making
use of cloud storage and
storing educational
materials
2015
Evaluation
research
85.
[86]
A survey of 282
participants in cloud-
based education systems
was analyzed by
2014
Validation
research
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3339250
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

VOLUME XX, 2017 9
structural equation
modeling (S.E.M.)
86.
[87]
The study aimed to
clarify the security issues
in the field of e-learning
in the cloud, to clarify
the gaps, and to find
some solutions
2013
Validation
research
87.
[88]
The effect of cloud
computing on education
and the importance of
cloud computing
services in the field of
education
2011
Evaluation
research
88.
[89]
Development of a model
to support e-learning in
cloud computing and
model analysis using
C.S.P. and evaluation
2012
Philosophica
l paper
89.
[9]
Includes a set of studies
in cloud computing in
the field of education,
determining what has
been implemented,
analyzing, and clarifying
future challenges.
2019
Evaluation
research
90.
[90]
Clarify the basic
concepts of cloud
computing in the field of
e-learning and suggest a
five-layer model for
implementation
2013
Opinion
paper
91.
[91]
Using cloud computing
platforms to increase the
effectiveness of e-
learning
2019
Opinion
paper
92.
[92]
Encouraging the
transition to cloud
computing in the
education sector and
analyzing using SWOT
to illustrate strengths and
weaknesses
2013
Validation
research
93.
[93]
Examine the factors
affecting the adoption of
cloud computing in
higher education using
SmartPLS and present
the results
2015
Validation
research
94.
[144]
Development of an IaaS
e-learning system
infrastructure to equip
the private cloud and
operate the learning
management system
2014
Evaluation
research
95.
[145]
A prototype for building
an e-learning
environment in cloud
computing with
emergency and disaster
alert
2017
Evaluation
research
96.
[146]
A prototype for building
an e-learning
environment in cloud
computing, a content
management system, and
using the phone to
display an emergency
and disaster alert
2015
Evaluation
research
97.
[94]
Explain how cloud
computing can be used
in the educational
process and its essential
characteristics
2012
Philosophica
l paper
98.
[95]
Moving to cloud
computing in universities
through six stages and
discussing policy
change, security, and
reliability issues
2010
Opinion
paper
99.
[161]
A mixed private and
public cloud to
implement the proposed
five-unit learning system
2012
Opinion
paper
100.
[96]
Discuss the concept of
cloud learning by
explaining the idea of the
virtual environment and
providing a platform for
learning from the ground
up
2014
Opinion
paper
101.
[97]
Developing a framework
based on A.R. for
collaborative product
design and Cloud
platform assistance for
file exchange and
storage
2020
Philosophica
l paper
102.
[98]
It aims to provide a
virtual learning lab that
uses a set of cloud-based
learning models
2014
Validation
research
103.
[99]
A study on the barriers
to using cloud
computing in higher
education included 69
universities in Kenya,
and the results were
statistically
2018
Opinion
paper
104.
[100]
E-learning standards and
their contribution to
cloud computing and the
most important current
issues: a comparison
between e-learning
before the transition to
the cloud environment
2015
Opinion
paper
105.
[147]
Improving the
educational process
through cloud computing
and reducing
complexity. It also aims
to develop virtual
laboratories
2014
Proposal of
solution
106.
[101]
Design and evaluation of
educational activities in
a cloud computing
environment that rely on
inquiry and social
networks and support
critical thinking skills
2014
Validation
research
107.
[102]
Using artificial
intelligence mechanisms
in cloud learning
platforms to evaluate the
user and recommend
additional education for
him
2014
Validation
research
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3339250
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

VOLUME XX, 2017 9
108.
[103]
Using two models of
SaaS and PaaS cloud
computing to teach
networking to graduate
students, this experience
affected the extent of
students' learning and
participation.
2011
Proposal of
solution
109.
[104]
A software-as-a-service
application in the
education field with a
user interface
2015
Evaluation
research
110.
[162]
survey gamification
techniques in cloud
computing to increase
the effectiveness of
education with
clarification of current
and future requirements
2019
Evaluation
research
111.
[130]
Framework It contains
services such as
recording activities,
exercises, and others,
and providing C ++
Workbook as a software
service in the cloud
2013
Philosophica
l paper
112.
[131]
It discusses e-learning in
the cloud computing
architecture divided into
five layers, as well as the
advantages and
disadvantages
2015
Proposal of
solution
113.
[105]
Analysis of student
activity data in the
English language
through a classroom
built in the cloud
environment
2021
Validation
research
114.
[106]
The CLEM cloud
learning project for
mechatronic students
enables access and use of
their virtual laboratories
and interact with them
via the cloud
2015
Evaluation
research
115.
[13]
A survey of learning
management systems
and interaction with
social networking
platforms, including 29
studies
2018
Evaluation
research
116.
[132]
A proposal to reduce
complexity in the cloud
computing environment
through agent and virtual
resource management
tools
2020
Proposal of
solution
117.
[133]
It aims at cooperative
and interactive education
through behavioral
analysis
2015
Evaluation
research
118.
[50]
A framework that aims
to ensure the quality of
services, specifically
infrastructure as a
service, by measuring
services and
performance with what
has been agreed upon by
the service provider.
2015
Philosophica
l paper
119.
[107]
A proposal for a smart
system that aims to share
content such as photos
and videos and access
them via smart devices
2017
Proposal of
solution
120.
[108]
Clarification of cloud
computing in e-learning,
the benefit, and the cost-
effectiveness
2018
Opinion
paper
121.
[109]
A comprehensive survey
of cloud computing in
the field of e-learning
and the most important
factors affecting its
success for stakeholders
2019
Validation
research
122.
[110]
The use of cloud services
in teaching engineering
students: the study
included 10 Cloud
Services from Amazon
and Google Firebase
2021
Evaluation
research
123.
[111]
Experience using the
Google platform to
collaborate and teach
subjects in the College of
Engineering, such as
industrial neural
networks, that need
expensive devices and
high specifications
2020
Validation
research
124.
[112]
Discusses cooperation
between students in the
development of a
software engineering
project and remote
interaction between
students
2020
Evaluation
research
125.
[113]
Adaptation of the cloud
computing environment
for courses distributed
through the T.A.M.
model and the use of
open-source software
2018
Opinion
paper
126.
[114]
Design and
implementation of a
collaborative educational
system based on cloud
computing. The
experiment includes two
groups of students
2018
Evaluation
research
127.
[115]
A proposal to develop a
cloud-based educational
system using educational
media to deliver lessons
focused on writing code,
storage size, and server
simulation
2019
Evaluation
research
128.
[116]
A cloud-based learning
management system with
new features that support
interaction between
teacher and learner
2021
Evaluation
research
129.
[117]
Cloud performance
monitoring in case of
increased demand from
beneficiaries and load
distribution
2018
Evaluation
research
130.
[118],
A survey of a group of
students and their views
on the use of cloud-
based virtual laboratories
2018
Validation
research
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3339250
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

VOLUME XX, 2017 9
to apply information
technology courses
131.
[119]
Develop a cloud-based
education system, data
science techniques, and
recommendation systems
that work in real-time
2021
Evaluation
research
132.
[120]
Designing a virtual lab
and applying the
networking course to it
using the OpenNebula
cloud operating system
and evaluating the
performance of the lab
2017
Validation
research
133.
[121]
A survey of e-learning in
Malawi during the
Corona period and a
proposal for some
technologies that support
e-learning
2022
Evaluation
research
134.
[122]
A cloud computing
architecture that
integrates with
physiological systems
that analyzes images and
behavior of students
during distance learning
2022
Validation
research
135.
[123]
Development of
ingredient management
software in pharmacy by
developing a cooperation
program to support
resource sharing and
communication
2011
Evaluation
research
136.
[148]
A proposal for a
prototype for private
cloud infrastructure in
universities, colleges,
and research centers
2010
Philosophica
l paper
137.
[149]
Improving performance
in scheduling using the
fairy algorithm by
applying to a private
cloud designed for e-
learning
2012
Validation
research
138.
[134]
Improving security in
platforms as a service by
isolating devices and
using more than one
algorithm to determine
which devices are close
to each other in terms of
location and scheduling
tasks, and then the
algorithm was compared
with three different
algorithms to evaluate
performance
2017
Validation
research
139.
[135]
A proposal for a national
framework for e-learning
that includes a set of
layers of applications,
services, databases, and
networks
2016
Philosophica
l paper
140.
[136]
A proposal to move
academic institutions to
rely on cloud computing
in education
2013
Philosophica
l paper
141.
[137]
Analysis of e-learning in
the previous period by
collecting data from
2019
Validation
research
different education
platforms to know the
current situation and
future requirements
142.
[150]
Analysis of e-learning in
the previous period by
collecting data from
different education
platforms to know the
current situation and
future requirements
2017
Validation
research
143.
[151]
Developing an
educational platform
using and relying on
Hadoop to analyze data
to improve performance
through feedback and
student assessment
2018
Validation
research
144.
[124]
Discussing e-learning in
cloud computing in
terms of structure and
challenges
2011
Opinion
paper
145.
[138]
A framework for e-
learning in ways that suit
the educational
community, including
individuals and
organizations, in an
effective manner with
cloud computing
2012
Philosophica
l paper
146.
[120]
Developing a cloud-
based virtual lab to study
networking through the
Open Nebula program
2017
Validation
research
147.
[139]
Test strategies that help
students with dyslexia in
the e-learning
environment on the
Amazon platform
2019
Validation
research
148.
[152]
A virtual lab experience
for a distributed I.T.
course application using
OpenStack
2017
Validation
research
149.
[153]
A virtual lab to apply
some information
technology materials,
such as big data, which
we find shortcomings in
using in the cloud
2021
Evaluation
research
150.
[154]
A virtual laboratory for
studying communication
and networking courses
2016
Evaluation
research
151.
[50]
A framework to improve
the quality of virtual
laboratories and reduce
time and delays in
performance
2015
Validation
research
152.
[155]
An open-source private
cloud for higher
education
2019
Evaluation
research
153.
[156]
implementing the
infrastructure for higher
education in Oman on
cloud computing using
OpenStack
2018
Evaluation
research
154.
[140]
Evaluation of computer
courses through cloud
computing in platforms
as a service and
infrastructure as a
service
2011
Validation
research
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3339250
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

VOLUME XX, 2017 9
IX. CONCLUSION
Since the direction is now for online learning as the
development of the technology, on the other hand, with its
on-demand, metered access to computing resources
(Process, Memory, Storage, etc.), cloud computing is a new
paradigm that is fostering technical advancement and
enabling dispersed applications across different geographies.
Therefore, this study reviewed an e-learning-based cloud
computing environment. The study extensively examined the
integration of e-learning and cloud computing from 2010 to
2022, analyzing 154 scholarly works. The potential for
remote engagement in education and employment is
emphasized. The reliance on literature reviews signals a need
for practical implementations and comprehensive integration
of hardware, software, security, and other facets. While
public cloud computing offers cost efficiency, data security
remains pivotal, particularly for sensitive information like
student grades. Cloud computing's role in shaping e-learning
is recognized, yet challenges necessitate ongoing innovation
for a comprehensive educational environment.
The primary finding of this study underscores the substantial
role of cloud computing in enhancing the integration and
effectiveness of e-learning, particularly concerning
architecture, software, performance, and the potential of
diverse cloud computing service models. Furthermore, this
study offers recommendations for both researchers and
readers. For researchers, it suggests a shift towards empirical
validation to bridge theoretical concepts with practical
applications, emphasizing the comprehensive integration of
all e-learning elements, prioritizing practical
implementations, and advancing research in data security. As
for readers, the study advises them to seek studies that
provide practical relevance through empirical insights,
promote integrated approaches in e-learning, consider case
studies, and maintain awareness of security measures within
cloud-based educational systems.
ACKNOWLEDGMENTS
The authors thank the Deanship of Scientific Research at
King Khalid University for funding this work through a large
group Research Project under grant number (RGP2/175/44).
Author contribution:
Conceptualization: H.E, A.O.I and F.S; Methodology: H.E,
A.O.I and F.S; Validation: I.H, A.A and H.J; Formal
analysis: A.A, M.A.I and A.E; Visualization: I.H, A.W.A
and A.E; Writing-original draft: H.E and A.O.I; Writing-
review and editing: I.H, H.J, M.A.I, and A.W.A;
Supervision: A.O.I.
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This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3339250
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/

VOLUME XX, 2017 9
HANA ELJAK received the B.Sc. information
systems and M.Sc. and Ph.D. degrees in information
technology from the Faculty of computer Sciences
and information technology, University of
Elneelain, Khartoum, Sudan. She is currently a
Lecturer with the information technology
department, Faculty of computer Science and
information technology, University of Elneelain.
Her current research interests include cloud
computing, social commerce, e-learning and e-commerce.
ASHRAF OSMAN IBRAHIM (Senior Member,
IEEE) received the B.Sc. degree in computer
science from Al-Neelain University, the M.Sc
degree in computer science from University of
Khartoum, and a Ph.D. in computer science from the
Universiti Teknologi Malaysia (UTM). Currently,
he is member of Advanced Machine Intelligence
Research Group University Malaysia Sabah. Dr.
Ibrahim has extensive experience in supervising and
co-supervising postgraduate students, with over 20 postgraduate scholars
successfully graduating under his guidance. In addition, Dr. Ibrahim serves
as an external and internal examiner/evaluator for Ph.D. and Master's theses
at numerous universities. His research interests include applications of
intelligence system, machine learning, artificial neural networks, deep
learning in cyber security and different areas, evolutionary computation,
data science, big data analytics in different applications and multi-objective
optimization. He is currently serving as a journal reviewer for many
reputation journals such as IEEE Transactions on Neural Networks and
Learning Systems, IEEE ACCESS, Expert systems with applications,
Engineering Applications of Artificial Intelligence, Biomedical signal
processing and control, and Journal of King Saud University Computer and
Information Sciences.
FAKHRELDIN SAEED is a Lecturer in Data
Science, University of Roehampton, and
computing departmen. Dr. Saeed experienced as
associate professor with a demonstrated history of
working in the higher education industry. Skilled
in Python (Programming Language), Data
Science, Apache Spark, Hadoop, and TensorFlow.
Dr. Saeed has a strong education professional with a Doctor of Philosophy
- Ph.D. focused on Computer Science from Al-neelain University,
supervised M.s.c and P.h.D students.
IBRAHIM ABAKER TARGIO HASHEM has
received the master’s degree in computer science
from the University of Wales, Newport, and the
Ph.D. degree in computer science from the
University of Malaya. He obtained professional
certificates from CISCO (CCNP, CCNA, and
CCNA Security) and the APMG Group (PRINCE2
Foundation, ITIL v3 Foundation, and OBASHI
Foundation). He is currently working as an assistant professor with the
Faculty of Computing and Informatics, University of Sharjah, United Arab
Emirates. He has published a number of research articles in refereed
international journals and magazines. His areas of interest include big data,
cloud computing, distributed computing, and machine learning. He is an
Active Member of the Center for Mobile Cloud Computing Research
(C4MCCR), University of Malaya Kula Lumpur, Malaysia. His numerous
research articles are very famous and among the most downloaded in top
journals.
ABDELZAHIR ABDELMABOUD received the
M.Sc. degree in computer science and information
from Gezira University, Sudan, and the Ph.D. degree
in software engineering from Universiti Teknologi
Malaysia (UTM), Malaysia. He is currently an
Assistant Professor with the Department Information
System, College of Science and Arts, King Khalid
University, Mahayil, Asir, Saudi Arabia. Previously
he was worked as the IT Manager, the Quality Manager, and the Database
Administrator. His research interests include the integration of blockchain
technology with the Internet of Things and cloud computing. He is a
member of the Software Engineering Research Group (SERG), UTM.
HASSAN JAMIL SYED received his Ph.D.
degree in computer science from the University of
Malaya, Malaysia in 2019. He completed his
master’s degree in personal mobile and satellite
communication from the University of Bradford,
England, U.K in 2008, He received his bachelor’s
degree in electrical engineering from the Quaide-
Awam University of Engineering, Science, and
Technology, Nawabshah, Pakistan, in 2003. He was with WorldCall
Telecom Ltd., Karachi, Pakistan, as a Network Engineer for two years, and
after that he was with the Faculty of Engineering Science and Technology,
Iqra University, Karachi, Pakistan for four years. After completing his Ph.D.
he worked as an Assistant Professor with the Department of Computer
Sciences, National University of Computer and Emerging Sciences,
Karachi, Pakistan from August 2019 to June 2022. During his career, he
supervised several MS Thesis/ final year projects and taught courses in the
Electronics, Telecommunication, and Computer Science Departments.
Currently, he is working as Senior Lecturer at the Faculty of Computing and
Informatics, Universiti Malaysia Sabah (UMS), Sabah, Malaysia. His
research interests include; cloud computing, Cybersecurity, SDN, NFV,
eBPF, and the Internet of Things.
MOHD ARFIAN BIN ISMAIL received the B.Sc.,
M.Sc., and Ph.D. degrees in computer science from
Universiti Teknologi Malaysia (UTM), in 2008, 2011,
and 2016, respectively. He is a Senior Lecturer with
the Faculty of Computing, University Malaysia
Pahang. His current research interests include machine
learning method and soft-computing algorithms.
ABUBAKAR ELSAFI received B.Sc. degree in
Computer Science from International University
of Africa (IUA), Sudan, M.Sc. degree in
Computer Science (Software Engineering)
from Sudan University of Science and
Technology (SUST), Ph.D. degree in Computer
Science (Software Engineering) from Universiti
Teknologi Malaysia (UTM), Malaysia. Dr. Elsafi
working as an academic staff since 2008, as
teaching assistant (in 2008-2010), lecturer (in
2010-2017), and assistant professor department of Software Engineering,
College of Computer Science & Engineering, University of Jeddah, Saudi
Arabia (in 2017-Now). Besides teaching, Dr. Elsafi given responsibilities as
academic supervisor to undergraduate students and member of several
academic and administrative committees. His research interests lie in
Computer Science with particular interests in Software Engineering such as
software testing, reliability and quality, component-based software
engineering, agile methodologies, and cloud computing.
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2023.3339250
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/