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Cloud and Edge Computing Integration into Software Engineering and Design (A Case of Health Information System)

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Patient care and quality of care have always been difficult to achieve despite the abundance of healthcare facilities because there is no coordination of the delivery process and no data sharing between healthcare facilities, leaving the patient alone within the delivery chain. This study is to incorporate cloud and edge computing into an existing health information system. In order to manage and retain patient data, it was specifically decided to build a central database using Amazon cloud services. In order to decrease the latency in data processing and retrieval, on-premises edge computing will be implemented using servers, storage, and networking resources. Data stored on each server is shared with the other either in real-time or at a scheduled period. Shared data that is communicated between health centers is encrypted before it is recorded in the database, and it is decrypted using a decryption key to restore its original state after being retrieved.
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International Journal of Science, Mathematics and Technology Learning
ISSN: 2327-7971 (Print) ISSN: 2327-915X (Online)
Volume 30 No. 2, 2022
Page | 824
Cloud and Edge Computing Integration into Software
Engineering and Design (A Case of Health Information
System)
Andrew Quansah
1
, Isaac Kofi Otchere
2
,
Samuel Tweneboah-Koduah
3
, Samuel Akwasi Frimpong
4
1,2,3Department of Computer and Electrical Engineering,
University of Energy and Natural Resources, Ghana
4Department of Computer Engineering,
Ghana Communication Technology University, Ghana
Abstract
Patient care and quality of care have always been difficult to achieve despite the abundance of
healthcare facilities because there is no coordination of the delivery process and no data sharing
between healthcare facilities, leaving the patient alone within the delivery chain. This study is
to incorporate cloud and edge computing into an existing health information system. In order
to manage and retain patient data, it was specifically decided to build a central database using
Amazon cloud services. In order to decrease the latency in data processing and retrieval, on-
premises edge computing will be implemented using servers, storage, and networking
resources. Data stored on each server is shared with the other either in real-time or at a
scheduled period. Shared data that is communicated between health centers is encrypted before
it is recorded in the database, and it is decrypted using a decryption key to restore its original
state after being retrieved.
Keywords
Edge Computing, Cloud Computing, Data synchronization, Cloud Services, Real-time.
1
First Author, email: andrew.quansah@uenr.edu.gh
2
Second Author, email: isaac.otchere@uenr.edu.gh
3
Third Author, email: samuel.tweneboah-koduah@uenr.edu.gh
4
Corresponding Author, email: sfrimpong@gctu.edu.gh
© Common Ground Research Networks, Samuel Akwasi Frimpong, All Rights Reserved.
Acceptance: 22Oct2022, Publication: 11Dec2022
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INTRODUCTION
Cloud and edge computing have recently grown in popularity and become a prominent trend
in IT. The cloud's meteoric rise has transformed the way businesses conduct themselves. It is
showing no signs of abating [1].
Computing used to be a procedure that could only and most usually be performed on a
computer. All computations and programs were run locally based on the data, and processing
capacity available to the computer at the time. However, because these devices could only
retain so much data and had access to restricted processing resources, this sort of computing
was limited. Then came the cloud computing era, which changed everything. Data storage and
processing resources are available in the cloud with cloud computing. This information is
available in real time via your mobile devices, tablets, smart watches, and laptop computers.
Cloud computing provided us with more storage capacity and processing resources. For
example, this allowed us to train machine learning models and save data that would not
otherwise be able to be stored on our devices. Amazon, Microsoft, Google, and IBM are among
the industry leaders that saw an opportunity to provide these storage services and computation
resources to businesses and consumers. However, three major issues arose as a result of cloud
computing. These difficulties included bandwidth constraints, latency issues, and privacy
concerns [2].
Edge computing can be dated back to the 1990s when Akamai launched a Content Delivery
Network (CDN), which introduced nodes in geographic locations closer to the end-user. These
nodes store cached static content such as photos and videos. Edge computing takes this concept
even further by allowing nodes to perform basic computational tasks [3]. Edge computing is
not the same as standard cloud computing. A new computing paradigm performs computation
at the network's edge. Its core concept is to bring computation closer to the source of the data
[4]. Edge computing is a new computing style of network edge execution. The edge computing
downlink data represents cloud service, the edge computing uplink data represents the Internet
of Everything, and the edge computing edge refers to the arbitrary computing and network
resources between the data source and the path of cloud computing [5]. In other words, edge
computing is to provide services and performs computations at the edge of the network and
data generation. Edge computing is to migrate the cloud's network, computing, storage
capabilities, and resources to the edge of the network, and provide intelligent services at the
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edge of the network to meet the requirements of low latency and high bandwidth on the network
[6].
There are about 5,517 health facilities in the country, categorized in CHPS through to
Hospitals [7]. Despite the abundance of healthcare facilities, patient care and quality of care
have always been a challenge because there is no coordination of the delivery process and data
sharing between healthcare centres, leaving the patient isolated within the delivery chain. One
of the most perplexing parts of healthcare is patient-related data. Storing the patient's data is a
difficult task. Data overload or mismanagement may result in an erroneous diagnosis, poor
treatment, lapsed appointments, and failure to keep track of changes in the patient's condition
developed [8].
Because of low-key technology, inefficiencies and healthcare blunders occur. One
particular source of worry is the exchange of patient data when patients are transferred from
one institution to another. Patient record sharing is not only wasteful and time-consuming, but
it also puts patients in danger. It can be too risky if the patient requires immediate or extensive
treatment [8]. In traditional health care delivery, data loss is a regular issue. In the majority of
Ghanaian health institutions, patient data is still kept in paper files. Patient data may be lost in
the event of a disaster. Paper folders, which are the only way of keeping patients' data, might
become old and eventually wear out, resulting in data loss.
The main objective of this study is to integrate cloud and edge computing into health
information systems. The specific objectives are to create a centralized database utilizing
Amazon cloud services (RDS and S3) to maintain and manage patient information and to use
cloud message brokers and storage to enable data sharing and increase access to data and to
utilize edge computing to reduce latency in data processing and retrieval and to test and
evaluate the system.
LITERATIVE REVIEW
2.1 Cloud Computing
Cloud computing refers to the on-demand availability of computer system resources,
particularly data storage (cloud storage) and computational power, without the user's direct
active supervision. Cloud computing providers such as Amazon Web Service (AWS), Server
Space, Microsoft Azure, Google Cloud Platform, and IBM Cloud Services provide their
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services in a variety of models, the three standard models according to NIST being
Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service
(SaaS) [9].
2.2.1 Cloud Computing Models
Infrastructure as a service (IaaS) is a sort of cloud computing service that provides on-demand
computation, storage, and networking resources on a pay-as-you-go basis [10]. A cloud
provider hosts the infrastructure components that would typically be found in an on-premises
data centre under an IaaS service model. This covers the virtualization or hypervisor layer, as
well as servers, storage, and networking hardware [11].
Platform as a service (PaaS) is a complete cloud development and deployment environment
with resources that allow you to offer everything from simple cloud-based applications to
sophisticated, cloud-enabled business systems [12]. The hardware and software are hosted on
the PaaS providers infrastructure. As a result, PaaS relieves developers of the need to install
on-premises hardware and software to develop or execute a new application.
Software-as-a-Service (SaaS) is a software licensing model in which users pay a monthly
fee for access to software that is hosted on external servers rather than in-house servers [13].
You rent the use of an app for your business and connect to it via the Internet, typically through
a web browser. The service provider's data center houses all of the underlying infrastructure,
middle ware, app software, and app data. The service provider is in charge of the hardware and
software, and with the right service agreement, they will ensure the app's availability and
security, as well as the protection of your data. With SaaS, your company can quickly get an
application up and operating for a low upfront cost [14].
2.3 Edge Computing
Edge computing is a distributed information technology architecture in which client data is
handled at the network's perimeter, as near to the source as feasible. The traditional computer
architecture, based on a centralized data centre and the internet as we know it (cloud
computing), is not well adapted to transferring continuously expanding rivers of real-world
data. Bandwidth constraints, latency concerns, and unpredictability in network outages can all
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work against such attempts. Edge computing architecture is being used by businesses to address
these data concerns [15].
2.4 Review of Related Works
IBM proposed a cloud-based platform that assists organizations in supporting wellness
programs and making medical information available to individuals. IBM cited some of the
reasons why firms, in its opinion, are moving their services into the cloud, claiming that
healthcare organizations are currently lured to cloud computing due to lower IT expenses,
faster service, and infrastructure availability. IBM developed safe delivery models and
deployed secure by design systems for industry clouds. The results achieved were security and
access to unlimited infrastructure. When healthcare organizations or communication services
providers use these secure models, they may be confident that their services will not be
compromised. The system is great as it unlocks access to computing resources and promises
the security of services on the platform. The system does not support multiple health
organizations communicating or sharing the same database, which makes it unable to create a
network of health organizations [16].
Zhai et al. hosted a management platform using cloud services to give hospitals centralized
access. The deployment and building of the centralized management system were less
expensive overall thanks to the implementation, which also gave health administrations'
interests more variety and flexibility. Additionally, the innovation enhanced server utilization
while reducing energy use by 30%. This adoption led to cost savings of up to 60% when
compared to conventional, non-cloud solutions. Despite the system's many benefits, there were
drawbacks as well, such as latency and security risks. [17]
C.H. Hong et al. proposed a cloud computing system that provided a new model for
remotely delivering a variety of ICT services that can be offered in a metered pay-as-you-go
manner. Storage space, administration systems, version control platforms, and collaborative
tools were among the services provided. Users pay based on the resources they consume when
they subscribe to the services they need. Numerous advantages were offered to end users as a
result. Versatility and ease of usage were advantageous to end users. On the cloud, end users
get access to services that would be challenging to compute on a local computer and offer
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access to limitless storage. But the biggest downsides were latency and security difficulties.
[18].
Nabil Sultan proposed a model that classifies edge computing resources into three types:
compute resources, storage resources, and network resources. These resources, unlike cloud
resources, are offered to consumers in a diverse, constrained, and distributed manner. These
resources are also more dynamic than cloud resources and are distributed competitively among
users in some circumstances. In edge computing, resource management refers to a collection
of control procedures for allocating and retaking resources to user tasks or requests based on
various variables such as latency, cost, and energy. The following are the most important
research sub-fields on this topic: resource allocation and scheduling; migration and placement;
load balancing; resource estimation and use; and resource sharing. A local server was setup to
manage resources such as documents, media files, and databases. This system was open to
cyber-attacks, which can lead to the integrity of the resources being breached.
Satyanarayanan M. utilised edge computing to develop attendance tracking system for
hospitals. On a server located on hospital vicinity, the system was installed and is currently
being hosted there. Without an internet connection, several devices on the same network might
connect to and communicate with the system. Because the source of the data is close to the
server, there are less latency issues, which aids personnel clocking in and out smoothly. Shifts
and absenteeism might be easily tracked by the administrators. Reduced processing and
response times are beneficial, but the system falls short when it comes to cloud services and
data backup. Data generated at one center cannot be shared with external parties since the
system operates offline. [19].
2.4 Conceptual Framework
The proposed solution is to incorporate cloud and edge computing into an existing health
information system or to create one if none exists. To reduce latencies during data processing
and retrieval, the system will use edge computing for the majority of its on-premise processes.
On-premises edge computing will be deployed using servers, storage, and networking
resources. This will help hospitals and health facilities to carry out their everyday operations
with little or no complications, as the system will conduct time-sensitive and life- saving duties.
When it is necessary to share patient data held on one on-premise server with another in a
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remote location, cloud computing comes into play. Cloud services (Amazon RDS for relational
databases and Amazon S3 for storing media files) from cloud computing provider AWS will
be used to construct a centralized database to store patients' and all vital data from all health
centers to communicate and share data among themselves. This will assist in aggregating all
created digital data from all sources and storing consistent data that represents the activity of
all connected health centers. The system on each on-premise server will attempt, within a
reasonable time frame, to synchronize the data they have access to with the data in the cloud
database by uploading newly processed and stored data and downloading data they do not have.
This guarantees that health centers automatically share data among themselves rather than
having to do so manually.
METHODOLOGY
3.1 Web-Based Hospital Management System with Cloud and Edge Computing
Integration
A Web application (Web app) is software that is stored on a remote server and delivered over
the Internet using a browser interface. Web services are, by definition, Web applications, and
many, but not all, websites feature Web applications. Earlier computing paradigms, such as
client-server, split the processing load for the program between code on the server and code
stored locally on each client. The portion that operates on the client's side is referred to as the
front-end, while the portion that runs on the server is referred to as the back-end. The client-
server architecture is used, which necessitates an always-on server to receive, process, and
respond to client application requests.
Figure 3.1: Client-server architecture
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3.2 Message Broker And Data synchronization
A message broker is an architectural pattern used to validate, transform, and route messages. It
mediates communication between applications, reducing the mutual knowledge that
applications must have in order to exchange messages. A broker's principal function is to accept
incoming messages from applications and conduct some action on them. After hours of work,
data collected at each centre is purged and stored in the database hosted on the on-premise
server. is uploaded to the centralized database, and a message is sent to the other servers to
download the uploaded data in order to ensure that the data on all on-premise servers are
synchronized. The message broker used is Amazon MQ.
3.3 Integration of Cloud and Edge Computing (Deployment)
One of the most critical aspects of the software development process is software deployment.
Deployment is the process by which developers deliver applications, modules, updates, and
patches to users. In the case of this study, deployment will necessitate both the preparation of
cloud services, such as the centralized cloud database and the installation of actual software on
on-premise servers. The software will include background tasks that will handle data upload
and download to and from the centralized database, as well as notify the other on-premise
server when data is uploaded. Because the on-premise server will be on the same network as
the computer devices or host devices that will be used (computers on which the HMU will be
accessed), no internet connection will be required to connect to or access the software. This
reduces latency and ensures that productivity is not hampered by internet speed or connectivity.
The cloud version of the deployment will include the database, which will serve as a backup
and also allow data to be shared (AWS RDS), the cloud broker, which will assist with cloud
messaging during data upload (AWS MQ), and media storage to store media files such as scan
images, patient images, and other media files that will be generated and collected (AWS S3).
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Figure 3.2: System Architecture Overview
Figure 3.3: System Architecture Overview
TESTS, RESULTS AND DISCUSSION
4.1 Real-time Data Synchronization and Backup
Data processed and saved at one centre (on-premise server) must be instantly accessible at the
other centres in order for real-time data synchronization to work. It was discovered that a
dependable and always active internet connection is necessary to enable real-time data
synchronization across numerous remote systems. As long as the linked host devices or
computers were on the same network as the server, communications and operations within one
specific centre could be conducted without an internet connection. The information system
included an interface that made it simple and quick to retrieve, process, and store patient data.
The data has to be encrypted before being saved in the database since health-related
information is private and must not be disclosed to a third party. Through Amazon Web
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Services' cloud message broker (publisher/subscriber) solution, AWS MQ, the shared data was
delivered to the other centers. The information is restored to its original form after retrieval
using a decryption key. Since no one can access or change the data while it is being transmitted,
it is safe to send data to other centers with increased trust in data integrity. Even if one of the
center’s databases is breached, the encrypted data will be useless to the hackers because they
won't be able to decipher it. After being successfully transferred to the remote servers, the data
is saved as a backup to the cloud database service, AWS RDS, which also offers centralized
access to the data when needed. When the transmission to the remote servers is unsuccessful,
the data is still transferred to the cloud database.
Alternatively, list of the database commands used to process or store data was transmitted
to the other servers to carry out the same actions, which would also guarantee that they all had
the same data each time. This method just sends the portion of the data that the other servers
do not have, which is the only distinction between it and the method explained in the preceding
paragraph. To protect its integrity, the list of commands or actions was likewise encrypted.
4.3 Scheduled Data Synchronization and Backup
Instability of networks and the internet in our communities, especially in rural areas, makes
real-time data synchronization nearly impossible; data is rarely transmitted, and receiving data
or retrieving updates from the cloud database becomes problematic. Since a request is sent
every time a new dataset is created, this process was also expensive in terms of computer
resources and internet fees. It necessitated the implementation of a solution that need not be
real-time. Since the on-premise servers can serve and provide services to all connected hosts
without the need for an internet connection, the health centers are able to interact with the
system without any issues and with minimal latency.
As solutions to the real-time data synchronization issues caused by the instability of the
internet, time-based scheduling of the data synchronization process and manual data
synchronization were implemented. Instead of attempting to notify the other remote servers
each time new data is stored, the system keeps track of the data that has been stored since the
last synchronization and assumes that the internet is stable at the scheduled time, at which point
it performs the synchronization process. In addition, manual data synchronization is a system
feature that requires the system administrator to click a button at any time to initiate the data
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synchronization process. Since the data is encrypted prior to storage, it is secure for
transmission over the internet. The system utilizes cron jobs to schedule tasks. Cron jobs enable
you to automate commands or scripts on your server to perform repetitive tasks automatically.
Python was used to create and save the file containing the script to handle and execute the
synchronization. This file is executed by the cron job whenever the scheduled time for
synchronization arrives. The same was true for the manual process; when the administrator
initiated the process, the same script was executed.
Figure 4.1: Script that publishes data to other remote servers
4.4 Key Findings
The setting up of an on-premise server to handle the day-to-day management of the information
system in order to integrate edge and cloud computing into health information systems.
Employing cloud services as well, such as, message brokers to let nodes to share data among
themselves and a cloud database to offer centralized access to the data and act as a backup. The
implementation decreased response time for data retrieval to an average of 22 milliseconds and
up to 93 milliseconds for data processing and storage. As a result, the information system
operates more smoothly because everything is handled quickly.
Figure 4.2: Average retrieval response time
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Figure 4.3: Maximum data processing and saving response time
CONCLUSION
Health information systems are finding usage in cloud and edge computing strategies. One of
the most promising methods for data synchronization, processing, and storage is frequently
referred to as cloud computing. We have more processing power and storage thanks to cloud
computing. Edge computing, On the other hand, involves moving the cloud's network,
computing, storage, and resource capabilities to the network's edge and offering intelligent
services there to satisfy the needs of low latency and high bandwidth on the network. In this
thesis, we examined the various methods for ensuring data synchronization and facilitating
easy data access, including scheduled and real-time data synchronization. Real-time data
synchronization requires that data produced and saved at one centre be immediately available
at the other centres. It was found that real-time data synchronization across several remote
systems requires a dependable and always active internet connection. A solution that did not
necessarily need to be real-time due to scheduled data synchronization was implemented. The
health centres are able to communicate with the system without any problems and with very
little latency because the on-premise servers can serve and give services to all linked hosts
without the requirement for an internet connection. Since health-related information is private
and shouldn't be shared with other parties, shared data is transmitted from one health centre to
another using AWS MQ, a cloud message broker service, and AWS. This data is encrypted
before being saved in the database and after retrieval, it is decrypted using a decryption key to
return to its original state. The implementations in this research are limited by the need for
access to a reliable internet connection, at least for a predetermined amount of time. These
implementations may produce inconsistent data and will noet function well in remote locations
with poor internet access. The synchronization of data could be slow. Future work might
involve deploying the management system or the back-end service to a Virtual Private Server
as a centralized version of the system, allowing it to be accessed from outside the vicinity of
the health centres while the host inside the premises connects to a on-premise server to reduce
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latency. This will improve system accessibility and pave the way for other developments like
scheduling appointments and online consultations.
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