Performance Comparison of Hyper-V and KVM for Cryptographic Tasks in Cloud Computing

Abstract

As the extensive use of cloud computing raises questions about the security of any personal data stored there, cryptography is being used more frequently as a security tool to protect data confidentiality and privacy in the cloud environment. A hypervisor is a virtualization software used in cloud hosting to divide and allocate resources on various pieces of hardware. The choice of hypervisor can significantly impact the performance of cryptographic operations in the cloud environment. An important issue that must be carefully examined is that no hypervisor is completely superior in terms of performance; Each hypervisor should be examined to meet specific needs. The main objective of this study is to provide accurate results to compare the performance of Hyper-V and Kernel-based Virtual Machine (KVM) while implementing different cryptographic algorithms to guide cloud service providers and end users in choosing the most suitable hypervisor for their cryptographic needs. This study evaluated the efficiency of two hypervisors, Hyper-V and KVM, in implementing six cryptographic algorithms: Rivest, Shamir, Adleman (RSA), Advanced Encryption Standard (AES), Triple Data Encryption Standard (TripleDES), Carlisle Adams and Stafford Tavares (CAST-128), BLOWFISH, and TwoFish. The study’s findings show that KVM outperforms Hyper-V, with 12.2% less Central Processing Unit (CPU) use and 12.95% less time overall for encryption and decryption operations with various file sizes. The study’s findings emphasize how crucial it is to pick a hypervisor that is appropriate for cryptographic needs in a cloud environment, which could assist both cloud service providers and end users. Future research may focus more on how various hypervisors perform while handling cryptographic workloads.

Full text

This work is licensed under a Creative Commons Attribution 4.0 International License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the
original work is properly cited.
ech
T
PressScience
DOI: 10.32604/cmc.2023.044304
ARTICLE
Performance Comparison of Hyper-V and KVM for Cryptographic Tasks
in Cloud Computing
Nader Abdel Karim1,*,OsamaA.Khashan
2,*, Waleed K. Abdulraheem3,MoutazAlazab
1,
Hasan Kanaker4,MahmoudE.Farfoura
5and Mohammad Alshinwan5,6
1Department of Intelligent Systems, Faculty of Artificial Intelligence, Al-Balqa Applied University, Al-Salt, 1705, Jordan
2Research and Innovation Centers, Rabdan Academy, P.O. Box 114646, Abu Dhabi, United Arab Emirates
3Department of Information Systems and Networks, The World Islamic Sciences and Education University, Amman, 11947, Jordan
4Department of Cyber Security, Isra University, Amman, 1162, Jordan
5Faculty of Science and Information Technology, Al-Zaytoonah University of Jordan, Amman, 11733, Jordan
6Middle East University Research Unit, Middle East University, Amman, 11831, Jordan
*Corresponding Authors: Nader Abdel Karim. Email: nader.salameh@bau.edu.jo; Osama A. Khashan. Email: okhashan@ra.ac.ae
Received: 27 July 2023 Accepted: 15 November 2023 Published: 27 February 2024
ABSTRACT
As the extensive use of cloud computing raises questions about the security of any personal data stored there,
cryptography is being used more frequently as a security tool to protect data confidentiality and privacy in the
cloud environment. A hypervisor is a virtualization software used in cloud hosting to divide and allocate resources
on various pieces of hardware. The choice of hypervisor can significantly impact the performance of cryptographic
operations in the cloud environment. An important issue that must be carefully examined is that no hypervisor
is completely superior in terms of performance; Each hypervisor should be examined to meet specific needs. The
main objective of this study is to provide accurate results to compare the performance of Hyper-V and Kernel-based
Virtual Machine (KVM) while implementing different cryptographic algorithms to guide cloud service providers
andendusersinchoosingthemostsuitablehypervisorfor their cryptographic needs. This study evaluated
the efficiency of two hypervisors, Hyper-V and KVM, in implementing six cryptographic algorithms: Rivest,
Shamir, Adleman (RSA), Advanced Encryption Standard (AES), Triple Data Encryption Standard (TripleDES),
Carlisle Adams and Stafford Tavares (CAST-128), BLOWFISH, and TwoFish. The study’s findings show that
KVM outperforms Hyper-V, with 12.2% less Central Processing Unit (CPU) use and 12.95% less time overall for
encryption and decryption operations with various file sizes. The study’s findings emphasize how crucial it is to
pick a hypervisor that is appropriate for cryptographic needs in a cloud environment, which could assist both
cloud service providers and end users. Future research may focus more on how various hypervisors perform while
handling cryptographic workloads.
KEYWORDS
Cloud computing; performance; virtualization; hypervisors; Hyper-V; KVM; cryptographic algorithm

2024 CMC, 2024, vol.78, no.2
1Introduction
Authors are required to adhere to this Microsoft Word template in preparing their manuscripts for
submission. It will speed up the review and typesetting process. In recent years, various sectors have
adopted cloud computing, making it more and more common. These sectors include governments,
financial markets, businesses, and industries. This is primarily because cloud computing offers
cost-effective and scalable solutions for storing, handling, and administering data. It also allows
collaboration and remote access to resources from anywhere. The growing number of smartphone
devices, the Internet of Things, and big data analytics have raised the need for cloud computing to
process and manage enormous amounts of data in a timely and efficient manner. Cloud computing
provides an affordable solution for companies and individuals with varying computing needs by
enabling remote computing devices to collaborate on data processing tasks [1].
Cloud computing is thoroughly described by the National Institute of Standards and Technology
(NIST), including key features, service models, and deployment options. According to [2], cloud
computing is a model that allows access to a shared pool of reconfigurable computing resources from
anywhere and at any time that is most convenient for you. With the idea of cloud computing, users and
companies have access to a model that makes it easier to provision and release a variety of resources,
including networks, servers, storage, applications, and services, without putting a lot of management
work into it or requiring direct contact with service providers. This approach offers several advantages,
including the ability to quickly scale up or down the number of resources and a pay-per-use model
that is effective and affordable [3,4]. The most notable characteristics of cloud computing include
on-demand self-service, widespread network access, resource pooling, quick elasticity, and measured
service. These features all contribute to the scalability, effectiveness, and cost-effectiveness of cloud
computing. Additionally, the NIST lists private, public, community, and hybrid clouds as the top four
cloud computing deployment options [3,5]. Private clouds are only available to one company, whereas
many companies share public, community, and hybrid clouds with similar goals. Users can access
cloud computing resources in a way that satisfies their preferences, needs, and requirements due to the
numerous service models and deployment options available.
Cloud computing has developed to the point where it is an essential component of the computing
infrastructure. Numerous advantages, including adaptability, affordability, and flexibility, come with
cloud computing. With over 90% of global enterprises using cloud computing in some form, these
advantages have helped to accelerate its adoption [6]. A survey conducted in 2020 found that 83% of
business applications were hosted in the cloud, with 41% of enterprise workloads employing public
cloud platforms [7]. In addition, there are 7.5 million active internet users every second, 3.5 million
active smartphone users every second, and more than 2 billion mobile game players who save their
data in the cloud [8].
Governments have also been quick to adopt cloud computing [9]. The United States federal
government is making significant efforts to increase cloud computing adoption through policies and
programs such as the Cloud Smart plan, which aims to accelerate the transition to cloud computing
across all government agencies [10]. Significant advancements in cloud computing usage have also been
undertaken by major countries such as the United Kingdom, Australia, New Zealand, and Canada
[9]. Governments using cloud computing are expected to stimulate innovation and enhance the quality
of services supplied to citizens.
Virtualization is a fundamental concept in cloud computing, Virtualization is creating a virtual
version of something at the same abstraction level, including virtual computer hardware platforms,

CMC, 2024, vol.78, no.2 2025
storage devices, and computer network resources. It allows for more efficient utilization of physical
computer hardware and is the foundation of cloud computing [11,12].
Virtualization is critical to cloud computing since it enhances workloads by making traditional
computing more efficient, flexible, and cost-effective [5]. Cloud computing virtualization implemen-
tations include virtual servers, hardware virtualization, and operating system virtualization [13]. To
reap the full advantages of virtualization, system administrators must select the best hypervisor
for their organization’s needs among various commercial and open-source alternatives based on
diverse underlying technologies. This selection procedure should consider several critical variables:
performance, features, and cost. For instance, Amazon Web Services virtualizes its Elastic Compute
Cloud (EC2) machines using the XEN hypervisor platform. In contrast, Microsoft Azure uses
the Hyper-V hypervisor platform for its Infrastructure as a Service (IaaS) cloud. Google’s recent
entry into the IaaS cloud market is based on the KVM hypervisor platform. Multiple hypervisors’
availability enables customers to select the one that best meets their organization’s needs while using
its services [14].
Hypervisor performance plays a crucial role in cloud computing since it has a vital effect on
the performance of the running virtual machines (VMs) [15,16]. It acts as a bottleneck affecting
performance in the cloud computing environment [17]. Hence, Numerous studies have been conducted
to investigate the impact of hypervisor performance on VMs. Regarding architecture, Kernel-based
Virtual Machine (KVM) is better than Hyper-V regarding VMs performance, such as Central
Processing Unit (CPU), memory, disk, and network, since KVM runs as a Linux kernel, so it gets
the full capabilities of the hardware infrastructure. In addition, KVM is open-source, and its open-
source nature allows the whole community to improve the product regarding software security and
performance [18]. KVM is supported by Linux vendors such as RedHat and the open-source commu-
nity [19]. At the same time, Hyper-V is a Windows-based kernel that requires more abstraction layers
which reduces the overall performance [18–20]. The authors in [19,21] compared the performance of
different hypervisors such as KVM, Hyper-V, Xen, and vSphere. They found that no hypervisor is
superior in terms of performance, and each hypervisor should be examined to meet a particular need.
Moreover, some studies show that performance for VMs, while deploying Hyper-V is better than KVM
in some processes, such as in [22–24]. Studies like [25,26] compare KVM and Hyper-V hypervisors (see
Table 1). Comparing the performance of the VMs processes between KVM and Hyper-V, especially
in the virtualization performance, is related to CPU, time, network, and memory as in [18,24].
Table 1: Comparison between KVM and Hyper-V
KVM Hyper-V
Version and base OS Linux (+QEMU) Windows server
Architecture Bare-Metal; Full, Para and
H/W–Assisted virtualization
Bare-Metal; Full, Para and
H/W–Assisted
virtualization
CPU and memory features Linux schedulers (fair queuing
scheduler, round-robin, fair
queuing, proportionally)
Scheduling control with
VMs reservation, VMs
limit, and relative weigh
License Open-source Commercial
High availability Yes Yes
(Continued)

2026 CMC, 2024, vol.78, no.2
Table 1 (continued)
KVM Hyper-V
Virtual CPUs per VM 64 64
Memory per VM 2 TB 1 TB
Maximum VMs 4000 4000
Active VMs per Host 512 Unlimited
Businesses that utilize cloud computing services must implement privacy and security protocols
as an essential requirement [27,28]. As a result of an increasing amount of cyberattacks and data
breaches, security is a vital part of cloud usage and acceptance for both cloud service providers
and customers [29]. Cloud computing provides a dependable and practical multipurpose solution
that allows businesses to access, store, and manage data from anywhere, anytime, using any device.
However, the advantages of cloud computing are vulnerable to being attacked by the dangers posed
by hacking attempts, which can include unauthorized access, the loss of data, and the compromise of
security systems. Therefore, cloud security measures such as encryption, authentication, and access
control are vital for maintaining the confidentiality, integrity, and availability of sensitive data and
services [24,30–32].
Cryptography is one of the oldest and most widely used methods for protecting sensitive digital
data in computers, and cloud computing environments. In cloud environments, where data is trans-
mitted and stored across multiple servers and networks, cryptography is essential in securing the data
against unauthorized access, tampering, or theft [33]. Employing various cryptographic technologies,
including symmetric and asymmetric encryption, aids in maintaining data confidentiality, integrity,
and authenticity and can be used to lower the danger of data breaches and cybercrimes. Additional
security measures such as access control, monitoring, and audit trails are also necessary to maintain
cloud computing. While Hyper-V and KVM are considered two of the most used hypervisors, this
research examined the capabilities of the Hyper-V and KVM hypervisors while adopting six different
cryptographic algorithms in this research. Rivest-Shamir-Adleman (RSA), Advanced Encryption
Standard (AES), Triple Data Encryption Algorithm (TripleDES), Carlisle Adams and Stafford
Tavares (CAST-128), BLOWFISH, and TwoFish are among these algorithms. Martin et al. [34]
presented comparisons between these six popular algorithms as shown in Table 2.
Table 2: Comparison of six common cryptographic algorithms
Algorithm Type Key size Speed Security
RSA Asymmetric 1024–4096 Slow High
AES Symmetric 128, 192, or 256 Fast High
TripleDES Symmetric 128, 192, or 256 Medium Good
CAST-128 Symmetric 128 Medium Good
BlowFish Symmetric 128, 192, or 256 Fast Good
TwoFish Symmetric 128, 192, or 256 Medium Good

CMC, 2024, vol.78, no.2 2027
In this research, the main objective is to assess the above algorithms’ performance in terms of
cloud security and establish how effectively they work. Furthermore, the study aims to resolve the
debate on which hypervisor performs better for VMs, KVM, or Hyper-V.
This study is expected to provide the following contributions:
•Examine the performance of the two most prominent hypervisors, KVM as an open-source and
Hyper-V, when using six cryptographic algorithms.
•Compare CPU usage and the time required for encryption and decryption methods with
different key sizes, data sizes, and core counts.
•Add substantial new information to virtualization by evaluating the impact of cryptographic
algorithms on KVM and Hyper-V’s overall performance (CPU and Time).
•Present advice for identifying the best hypervisor and cryptographic method for specific use
scenarios based on performance measurements.
The subsequent sections of this manuscript are organized as follows: Section 2 offers related
works; Section 3 demonstrates the evaluation method; Section 4 presents the results and discussion.
Finally, the conclusion with some future recommendations is presented in Section 5.
2Related Works
This section of the research summarizes previous studies that examined diverse types of hyper-
visors, especially KVM and Hyper-V, and their impact on improving the performance of VMs in
the cloud environment. Moreover, studies exploring ways to enhance cloud computing security were
reviewed. Additionally, this section explored the latest encryption techniques used in cloud computing,
as encryption plays a vital role in protecting cloud computing.
Regarding the studies that dealt with hypervisors, authors [24] compared two hypervisors, Xen and
Hyper-V, in terms of the performance of the VMs while using eight different cryptosystems. He found
that Xen is better than Hyper-V regarding CPU and time response in most processes, while Hyper-V
is better in some results and algorithms. di Pietro et al. [35] analyzed the advantages and drawbacks
of using virtualization technologies, including Xen, KVM, VMWare ESX, Hyper-V, and VirtualBox
in cloud environments. The authors discussed the benefits of virtualization technology, including
better resource use, flexibility, scalability, and potential security issues. Studies [15] investigated
the performance of the hypervisor. They stated that it plays an essential role in cloud computing
since it has a vital effect on the performance of the running VMs. Several studies [18] that dealt
with hypervisor performance within cloud environments specifically, KVM and Hyper-V, and their
distinctive characteristics are already discussed in the introduction section.
The existing literature proposed various defense methodologies, such as encryption, access
control, and data obfuscation, to protect sensitive data from unauthorized access and potential threats
to improve the security and privacy of cloud-based data storage and processing. Multiple studies
have offered innovative approaches, including homomorphic encryption, attribute-based encryption,
and secure multi-party computing. Thabit et al. [36] investigated the security and performance
features of a unique lightweight cryptographic algorithm for cloud computing. The findings showed
that the suggested method outperforms standard cryptographic algorithms and provides adequate
protection against various security risks. Bhandari et al. [37] proposed a model approach for creating
a cloud-based client relationship management (CRM) service employing independent encryption and
decryption procedures using the Blowfish algorithm to improve the security and privacy of client

2028 CMC, 2024, vol.78, no.2
data. The study’s findings indicated that the suggested strategy considerably decreases the risks of
data breaches and illegal access, resulting in a safe and dependable CRM solution for enterprises.
The healthcare sector has great hope for the future of cloud computing in terms of improving the
standard and effectiveness of healthcare services. Cloud computing can assist healthcare organizations
in streamlining their operations, reducing expenses, and improving patient outcomes by facilitating
simple access to medical data and fostering collaboration among healthcare providers. However, imple-
menting resilient regulatory and governance frameworks that protect patient information privacy,
confidentiality, and security is necessary to successfully integrate cloud computing in healthcare. In
this regard, various access control and encryption schemes have been proposed to enhance the security
and privacy of patient data in healthcare clouds. Chinnasamy et al. [38] proposed a novel access control
scheme that combines hybrid cryptographic techniques with attribute-based access control to secure
the retrieval of electronic health records in healthcare clouds. On the other hand, Dwivedi et al. [39]
proposed a secure healthcare monitoring sensor cloud system that utilizes attribute-based elliptical
curve cryptography for data protection. Both proposed schemes exhibit better performance than
existing techniques, and their adoption is recommended for enhancing data security and privacy in
healthcare cloud systems.
Other studies have focused on key management systems in cloud cryptography. Vinothkumar
et al. [40] employed a systematic literature review methodology to analyze the current state-of-the-
art in the field of cryptography and key management-based security in cloud computing to support
cloud cryptography client and key management service interoperability. Their results highlight the
importance of key management protocols, encryption algorithms, and the need for secure key storage
mechanisms to ensure data confidentiality and integrity in cloud computing environments.
Several studies have also suggested hybrid cryptographic solutions to the security issues with
cloud computing. A method for improving data protection in cloud computing was put forth by
Suresha et al. [41] using key derivation. This cryptographic technique creates three secret keys
from a single master key, each used for a particular purpose. To protect the confidentiality and
integrity of their data, the authors advise organizations to adopt key derivation techniques as part
of their cloud computing security strategy. To preserve users’ privacy and security in the cloud,
Orobosade et al. [42] examined the effectiveness of hybrid encryption by implementing symmetric and
asymmetric cryptography techniques using elliptic curve cryptography and AES. According to their
research, hybrid encryption can improve the confidentiality and integrity of cloud-based applications,
hence enhancing security.
A research study on enhancing cloud data security with hybrid encryption and steganography
techniques was published by Abbas et al. [43]. The authors in this research combined the AES
symmetric encryption algorithm and the RSA asymmetric encryption algorithm to create hybrid
encryption. The encrypted data was then embedded in an image using the LSB technique, and the
data’s authenticity was verified using the SHA hashing method. Before hiding the data in the image,
they suggested compressing it using the LZW method. They also suggested employing a hybrid
approach in cloud-based systems to increase the security of sensitive data. To evaluate the effectiveness
of cryptographic and hybrid security measures for cloud computing systems, Chaudhary et al. [44]sug-
gested a thorough methodology. The study provides in-depth information and findings demonstrating
how hybrid solutions offer more security than cryptography. By demonstrating the effectiveness of
hybrid solutions compared to cloud computing cryptography security implementation in algorithm
execution, their research study solves the security issues associated with cloud computing.

CMC, 2024, vol.78, no.2 2029
Zaineldeen et al. [45] offered an overview of cloud computing cryptography, comparing various
methods, tools, and conclusions. The authors investigate several cloud computing cryptography
techniques, including homomorphic encryption, symmetric and asymmetric key encryption, and proxy
re-encryption. They discuss issues like key management, data confidentiality, and data integrity arising
when incorporating cryptography with cloud computing. They contend that even though cryptography
might improve cloud computing security, significant challenges must be overcome. The findings give
a thorough picture of the state of cryptography in cloud computing and emphasize the need for more
research, mainly focusing on the difficulty of encryption implementation at the hypervisor layer.
Ogiela [46] developed hybrid CAPTCHA codes employing a hybrid techniques approach to
examine the possibilities of cognitive cryptography. The results of the study indicated that cognitive
cryptography has the potential to improve the security and integrity of cloud data.
Singh et al. [47] proposed a novel authentication approach based on mutual authentication
for secure data-sharing in a federated cloud services environment. The proposed method combines
cryptography and machine learning techniques ensemble voting classifier to achieve an essential level
of security and privacy, as demonstrated by the experimental results.
An innovative AI-based encryption method for cloud computing, known as AI-Enc, was devel-
oped by Wang et al. in a report published in 2021 [48]. AI-Enc uses deep neural networks to encrypt and
decode data safely and effectively. The authors asserted that traditional encryption techniques cannot
match AI-Enc’s ability to deliver more robust security and lower computational overhead, nor can it be
integrated with existing hypervisors. In 2020, Mrbullwinkle et al. introduced the RL-Hyper framework,
which uses reinforcement learning to optimize the performance of hypervisors. Throughput, latency,
and energy consumption can all be significantly improved because of the framework’s ability to
dynamically modify hypervisor parameters based on workload factors and system conditions [49].
Table 3 summarizes some of the collected literature within three sections: reference, description,
and results.
Table 3: Summary of related literature
Ref. Description Results
[15–17] In these papers, the authors attempt
to assess the maturity of different
types of hypervisors and study their
impact on VMs.
The results showed that the
performance of VMs and the cloud
computing environment may be
directly impacted by hypervisor
performance. The VMs and the overall
cloud computing environment may
experience a slowdown if the hypervisor
is underperforming. This may occur if
the hypervisor is not set up correctly or
if it is underpowered to manage the
workload. But in most cases,
virtualization just slightly increases the
workload on the CPU, memory,
storage, and network.
(Continued)

2030 CMC, 2024, vol.78, no.2
Table 3 (continued)
Ref. Description Results
[18–20] In these papers, the authors studied
the properties and implementation
of KVM and Hyper-V hypervisors.
The authors state that KVM is,
theoretically, better than Hyper-V
because of its lightweight kernel and
open-source nature.
[19,21] In these papers, the authors
compared the performance of
different hypervisor types such as
KVM, Hyper-V, Xen, and vSphere.
Researchers have not found any
hypervisor that is superior in terms of
performance, and each hypervisor
should be tested to meet a specific need.
Choosing the right hypervisor will
continue to be an important challenge
for proper virtualization management.
[21,23] In these papers, the authors compare
KVM and Hyper-V as well as other
hypervisors using various criteria,
such as responsiveness to SQL
workloads, file server workloads,
and web server workloads.
The researchers noted that there was a
difference in the performance of
hypervisors for different applications
and workloads under different test
conditions, but that Hyper-V
performed better than KVM during the
tests. Choosing the right hypervisor will
increase energy efficiency during cloud
workload.
[24] In this paper, the author compares
the performance of the Xen and
Hyper-V hypervisors while running
eight cryptographic algorithms:
RSA, AES, RC4, CAST-128,
TripleDES, DES, TwoFish, and
BlowFish. The author used different
key sizes, data sizes, and numbers of
cores to test the performance of the
algorithms.
The results show that Xen outperforms
Hyper-V by 15% in terms of time and
6.1% in terms of CPU utilization,
except for CPU utilization using AES,
where Hyper-V outperforms Xen.
[25,26] In these papers, the authors
compare and describe the properties
of KVM and Hyper-V hypervisors.
Each hypervisor has its properties and
abilities. Both are Type 1 hypervisors;
KVM and Hyper-V are supported on
various platforms. Although they both
provide a comparable set of
functionalities, Hyper-V is a
commercial product, whereas KVM is
open-source and free to use. Compared
to Hyper-V, KVM requires more
configuration and management work.
(Continued)

CMC, 2024, vol.78, no.2 2031
Table 3 (continued)
Ref. Description Results
[30–32] In these papers, the authors
investigate the techniques used to
ensure security and privacy in cloud
environments, such as encryption,
authentication, and access control.
Researchers have shown that there are
different ways to ensure the security of
the cloud, whether it is symmetric (such
as AES), asymmetric (such as RSA), or
hashing algorithms (e.g., SHA 256).
The use of multi-factor authentication
is also vital to ensure the security of the
cloud to achieve a high degree of
security and achieve what is called
defense in depth (layers).
[38,39,41,42] In these papers, the authors
proposed schemes to ensure the
security of the cloud in the health
sector based on cryptography.
Using various encryption algorithms,
the authors proposed a scheme that
ensures data integrity, confidentiality,
and precise control of access. The
proposed scheme also reduces
computational overheads, which
increases system performance.
[43–46] In these papers, the authors
proposed using various hybrid
techniques with cryptography, such
as steganography, homomorphic,
and CAPTCHA code in the cloud
computing environment.
The authors state that encryption can
be combined with other technologies to
build a robust technology that ensures
data security in the cloud. For example,
encryption and steganography can be
combined to provide dual security for
data stored in the cloud.
[47–49] In these papers, the authors propose
using cryptography in the cloud in
combination with machine learning
and AI to offer methods and
schemes for ensuring security.
The authors state that AI and machine
learning can be used to secure the cloud
and provide better performance to the
hypervisor. The use of AI, as well as
machine learning, is vital to ensuring
hypervisor performance and cloud
computing security in the future.
3Evaluation Method
In this research, authors examined the average response time for encryption and decryption of
different encryption algorithms to verify the correlation between time and performance, evaluated the
effect of changes in the number of CPU cores, examined the impact of varying key sizes, and finally,
evaluate the effect of data size on performance.
As shown in Fig. 1, the proposed evaluation method involves deploying two servers, each utilizing
a distinct hypervisor: Hyper-V and KVM. A VM running Windows 7 is instantiated on each server, and
six cryptographic algorithms, including RSA, TripleDES, AES, CAST-128, TwoFish, and Blowfish,

2032 CMC, 2024, vol.78, no.2
are implemented using tools on each VM instance to evaluate the different cryptographic algorithms’
performance.
Figure 1: Evaluation method
3.1 Environment Setup
The proposed environment in this study includes two different data centers (i.e., servers), both of
which have an i7 CPU at 2.7 GHz and 16 GB of DDR RAM. For each server, the VM is implemented
using Windows 7. The VM is allocated 4 GB of DDR RAM and a variable number of cores, depending
on the experiment. Additionally, the VMs were deployed with the following subsequent software
tools:
1. HsCipherSDK v2.1 tool that provides RSA, CAST-128, TwoFish, and Blowfish cryptographic
algorithms while encryption and decryption with different keys using Python programming.
2. Rijndael tool that provides AES cryptographic algorithm.
3. PyCryptodome tool that provides TripleDES cryptosystem using Python programming lan-
guage.
4. Windows 7 Performance Monitor is used to measure CPU usage over a specific period of time
and is measured in percentage %.
5. Time is measured by minutes and seconds m:s using a stopwatch.

CMC, 2024, vol.78, no.2 2033
3.2 Performance Metrics and Dataset
In this research, authors used HsCipherSDK v2.1 as a cryptographic tool to encrypt RSA, DES,
TripleDES, CAST-128, TwoFish, and Blowfish algorithms. In contrast, the Rijndael tool is used to
encrypt and decrypt the AES algorithm. For both tools, a different key can be chosen among various
data. Meanwhile, other file types are selected from among the algorithms, such as PDF files, Images,
and compressed files. The file sizes differ from 1 KB in RSA to 5 GB in AES.
CPU core in cloud computing can be changed among different VMs. So, the authors used two
different cores during experiments 2 and 4.
Based on [50], the following equation (Eq. (1)) is used to calculate the overall ratio performance
of the results:
Rat =




Average (Hyper −V)−Average (KVM)
Average (KVM)




×100% (1)
4Result Analysis
This study investigates the performance of six popular encryption algorithms: RSA, TripleDES,
AES, CAST-128, TwoFish, and Blowfish. Experiments were conducted several times for each algo-
rithm in different contexts to ensure that the results were accurate and reliable. Below is a brief
description of the algorithms used in the study, along with their evaluation results:
4.1 RSA
The RSA algorithm is a cornerstone of modern cryptography and offers a safe way to send
sensitive data over open networks. Because RSA fundamentally depends on the difficulty of factoring
large numbers, it is a vital instrument for data encryption. The algorithm is based on a mathematical
equation involving two large prime numbers, p and q, and their product, n. The selection of public
and private key exponents, e and d, respectively, involves using Euler’s totient function of n. RSA
encryption involves raising the message to the power of e modulo n, while decryption requires raising
the encrypted message to the power of d modulo n. The strength of RSA lies in its asymmetry, which
makes it computationally infeasible to derive the private key from the public key. The recommended
key size for RSA by NIST in 2015 was 2048 bits [51], and it is widely used in cloud computing [52].
Table 4 compares the performance of the Hyper-V and KVM hypervisors in terms of duration
time, and CPU usage among different key sizes during the encryption and decryption operations.
The first experiment indicates that with a key size of 64 and one core with a file size of 606 k, the
Hyper-V hypervisor required 40.3 and 51 s, respectively, for encryption and decryption, and a CPU
use was 61% for both encryption and decryption. The KVM hypervisor required 26.2 and 29.2 s for
encryption and decryption, with CPU use of 59% and 60%, respectively. Since KVM consumes less
time duration and CPU usage while encryption and decryption than Hyper-V, the results indicate that
KVM outperformed Hyper-V regarding both response time and CPU utilization.
Table 4: RSA experimental results
Experiment
no.
Hypervisor
type
Key
size
No. of CPU
cores
File
size
Encryption
time m:s
CPU
encryption %
Decryption
time m:s
CPU
decryption %
1 Hyper-V 64 1 606 k 40.3 61 51 61
KVM 64 1 606 k 26.2 59 29.2 60
(Continued)

2034 CMC, 2024, vol.78, no.2
Table 4 (continued)
Experiment
no.
Hypervisor
type
Key
size
No. of CPU
cores
File
size
Encryption
time m:s
CPU
encryption %
Decryption
time m:s
CPU
decryption %
2 Hyper-V 1024 1 12.2 k 14.5 56 23.1 54
KVM 1024 1 12.2 k 14.4 54 21.5 53
3 Hyper-V 2048 1 1 k 5.4 54 10.3 55
KVM 2048 1 1 k 4.6 53 10.5 53
4 Hyper-V 256 1 12.2 k 2.3 64 3.1 64
KVM 256 1 12.2 k 2 61 2.1 55
5 Hyper-V 1536 1 12.2 k 34 64 60.2 62
KVM 1536 1 12.2 k 35.5 56 60 52
6 Hyper-V 2048 1 12.2 k 67.2 52 106.4 56
KVM 2048 1 12.2 k 77.2 51 120.1 52
7 Hyper-V 1024 2 12.2 k 15.5 100 26.4 89
KVM 1024 2 12.2 k 9.4 95 9.4 87
8 Hyper-V 1024 2 2.25 M 51.5 95 78.2 87
KVM 1024 2 2.25 M 25 85 32.2 82
9 Hyper-V 2048 2 1 K 4.1 96 7.5 96
KVM 2048 2 1 K 3.5 94 7 94
10 Hyper-V 2048 2 2 K 7.6 94 15 93
KVM 2048 2 2 K 7.6 94 13.5 93
Table 5 compares the average response time and CPU utilization of two hypervisors, KVM and
Hyper-V, during the encryption and decryption processes using the RSA cryptographic algorithm. The
results reveal that KVM outperforms Hyper-V regarding time and CPU consumption for encryption
and decryption operations. Specifically, KVM demonstrates a 3.7 and 3.4 difference in time and CPU
utilization for encryption and a 7.6 and 3.6 difference for decryption. These results show that KVM is
a more efficient and effective hypervisor for cryptographic operations using the RSA algorithm, even
when changing the key, data size, or core numbers. It can be helpful for companies and individuals
looking to improve their performance and security.
Table 5: RSA performance
RSA Encryption Decryption
Time CPU Time CPU
Hyper-V 24.2 73.6 38.1 71.7
KVM 20.5 70.2 30.5 68.1
Differences 3.7 3.4 7.6 3.6
4.2 AES
Modern cryptographic systems frequently use the symmetric block cipher known as the AES [53].
The Data Encryption Standard (DES) was chosen as its replacement by the National Institute of
Standards and Technology (NIST) in 2001. AES is developed to be secure against various attacks,
such as side-channel attacks, differential and linear cryptanalysis, and brute-force attacks. AES utilizes
a key that is either 128, 192, or 256 bits long and operates on fixed block sizes of 128 bits [53]. The
algorithm comprises rounds with several smaller steps, including substitution, permutation, and XOR
operations. The key schedule is the same for all rounds except the final round, which differs slightly

CMC, 2024, vol.78, no.2 2035
from the earlier rounds. The overall strength of AES comes from its use of a substitution-permutation
network (SPN) structure and the extensive diffusion of input bits throughout the rounds.
Galois field arithmetic, especially the finite field GF (2 ∧8), is the foundation of the mathematical
formula utilized in AES [54]. This finite field consists of 256 elements and represents bytes in the input
block and elements of the key schedule. The AES algorithm uses a set of standard operations, including
the substitution of bytes, shift rows, mixing columns, and adding round keys. The substitution step
involves replacing each byte of the input block with another byte based on a fixed lookup table called
the S-box. The shift rows step involves cyclically shifting the bytes in each row of the input block
by a fixed amount. The mix columns step involves multiplying each column of the input block by
a fixed matrix, resulting in a diffusion of input bits throughout the block. The add-round key step
involves XORing the input block with a key schedule derived from the encryption key. Together,
these steps create a highly secure cloud security environment and efficient encryption algorithm
widely used in applications ranging from secure communication to data storage. This paper presents
a detailed analysis of the AES algorithm using (128, 192, and 256), CPU core numbers (2 to 4), and
its performance in ten repeated experiments as in Tabl e 6 below.
Table 6: AES experimental results
Experiment
no.
Hypervisor
type
Key
size
No. of CPU
cores
File
size
Encryption
time m:s
CPU
encryption %
Decryption
time m:s
CPU
decryption %
1Hyper-V 128 2 2 G 2.4 67 2.4 69
KVM 128 2 2 G 2.4 60 2.4 55
2Hyper-V 128 4 2 G 3.1 34 2.5 40
KVM 128 4 2 G 2.6 30 2.3 34
3Hyper-V 128 4 5 G 7.2 35 7.1 38
KVM 128 4 5 G 6.3 30 6.1 33
4Hyper-V 128 4 3 G 3.4 35 3.5 36
KVM 128 4 3 G 3.4 30 3.2 32
5Hyper-V 192 2 2 G 2.6 67 2.6 71
KVM 192 2 2 G 2.5 62 2.6 56
6Hyper-V 192 4 2 G 2.5 37 3.2 37
KVM 192 4 2 G 2.5 33 2.4 34
7Hyper-V 256 2 3 G 4.2 70 4.0 69
KVM 256 2 3 G 3.5 63 3.2 64
8Hyper-V 256 2 2 G 3.3 63 3.3 67
KVM 256 2 2 G 2.5 61 2.5 61
9Hyper-V 256 4 2 G 3 37 3.3 38
KVM 256 4 2 G 2.6 34 2.6 34
10 Hyper-V 256 4 1 G 1.1 34 1.1 32
KVM 256 4 1 G 1.1 32 1 32
Table 7 compares the average response time and CPU utilization of two hypervisors, KVM and
Hyper-V, during the encryption and decryption processes using the AES cryptographic algorithm. The
results reveal that KVM outperforms Hyper-V regarding time and CPU consumption for encryption
and decryption operations. Specifically, KVM demonstrates a 0.4 and 4.4 difference in time and
CPU utilization for encryption and a 0.5 and 4.8 difference for decryption. These findings suggest
that KVM is a more efficient and effective hypervisor for performing cryptographic tasks using the
AES algorithm, even when changing the key, data size, or core numbers. It can significantly benefit
organizations and individuals seeking to enhance their security and performance capabilities.

2036 CMC, 2024, vol.78, no.2
Table 7: AES performance
AES Encryption Decryption
Time CPU Time CPU
Hyper-V 3.5 45.4 3.5 47.4
KVM 3.1 41 3 42.6
Differences 0.4 4.4 0.5 4.8
4.3 TripleDES
The Triple Data Encryption Standard (TripleDES) is a cryptographic technique that uses a
symmetric block cipher. Three DES keys comprise a key bundle, also known as a key [55].
The authors have performed ten different experiments for both Hyper-V and KVM hypervisors.
Different CPU core numbers (2 and 4) and file sizes with fixed key size (192-bit) were used.
Table 8 shows the average time and CPU usage while encryption and decryption using TripleDES
cryptographic algorithm.
Table 8: TripleDES performance
TripleDES Encryption Decryption
Time CPU Time CPU
Hyper-V 11.9 48.2 12.1 49.1
KVM 11.6 44.9 11.7 46.4
Differences 0.3 3.3 0.4 2.7
As presented in Table 8, the performance of two hypervisors, KVM and Hyper-V, was evaluated
during the encryption and decryption processes using the TripleDES cryptographic algorithm. The
analysis indicates that KVM has superior CPU performance for encryption compared to Hyper-V, but
Hyper-V exhibits better encryption response time. On the other hand, KVM has better CPU utilization
and time response for decryption than Hyper-V.
4.4 CAST-128
CAST-128, often known as CAST5, is a 1996-designed member of the CAST family. The
technique is based on a traditional Feistel/DES network with 16 rounds, 64-bit block, and up to 128-bit
keys. CAST-128 has 16 subkey pairs, three types of round functions, and eight distinct S-boxes [56].
The authors have performed ten different experiments for both Hyper-V and KVM hypervisors.
The key (128-bit), CPU core numbers (2 and 4), and different file sizes were used. Tabl e 9 shows the
average time and CPU usage while encryption and decryption using the CAST-128 cryptographic
algorithm.

CMC, 2024, vol.78, no.2 2037
Table 9: CAST-128 performance
CAST-128 Encryption Decryption
Time CPU Time CPU
Hyper-V 2.1 46.3 2 48.3
KVM 1.9 40.3 1.9 41
Differences 0.2 6 0.1 7.3
Table 9 compares the average response time and CPU utilization of two hypervisors, KVM
and Hyper-V, during the encryption and decryption processes using the CAST-128 cryptographic
algorithm. The results reveal that KVM outperforms Hyper-V regarding time and CPU consumption
for encryption and decryption operations. Specifically, KVM shows a 0.2 and 6 difference in time and
CPU utilization for encryption and a 0.1 and 7.3 difference for decryption. These findings suggest that
KVM is a more efficient and effective hypervisor for performing cryptographic tasks using the CAST-
128 algorithm, even when changing the key, data size, or core numbers. It can significantly benefit
organizations and individuals seeking to enhance their security and performance capabilities.
4.5 BLOWFISH
Blowfish is a symmetric block cipher designed to provide secure encryption of digital data. Bruce
Schneier created it in 1993, and it gained popularity due to its flexibility and high level of security.
Blowfish is a cipher well-suited for encrypting enormous amounts of data owing to its block length
of 64 bits. It utilizes four substitution boxes, often known as “S-Boxes,” and eighteen permutation
arrays, or “P-Boxes,”. Blowfish is appropriate for applications requiring high-speed encryption since it
supports various keys ranging from 32 bits to 448 bits [56]. The Feistel network, a prominent topology
used in many block ciphers, is the basis for Blowfish’s mathematical formula. Blowfish encrypts data
in rounds or iterations, including a replacement step, a permutation step, and a key mixing step. The
substitution step is performed using the S-Boxes, which replace blocks of data with other blocks
according to a predetermined table. The permutation step is performed using the P-Boxes, which
scramble the order of the data. Finally, the key mixing step involves XORing the data with a portion
of the secret key. This process is repeated for several rounds, resulting in encrypted data. The strength
of Blowfish lies in its complex key schedule, which ensures that each key bit affects many parts of the
encryption process, making it highly resistant to attacks.
The authors have performed ten different experiments for both Hyper-V and KVM hypervisors.
A 64-bit key size, CPU core numbers (2 and 4), and different file sizes were used.
Table 10 compares the average response time and CPU utilization of two hypervisors, KVM
and Hyper-V, during the encryption and decryption processes using the BLOWFISH cryptographic
algorithm. The results reveal that KVM outperforms Hyper-V regarding time and CPU consumption
for encryption and decryption operations. Specifically, KVM demonstrates a 1.1 and 4.8 difference in
time and CPU utilization for encryption and a 1.1 and 4.5 difference for decryption. These findings
suggest that KVM is a more efficient and effective hypervisor for performing cryptographic tasks using
the BLOWFISH algorithm, even when changing the key, data size, or core numbers. It can significantly
benefit organizations and individuals seeking to enhance their security and performance capabilities.

2038 CMC, 2024, vol.78, no.2
Table 10: BLOWFISH performance
BLOWFISH Encryption Decryption
Time CPU Time CPU
Hyper-V 7.4 50.5 7.1 51.5
KVM 6.3 45.7 6 47
Differences 1.1 4.8 1.1 4.5
4.6 TwoFish
Twofish is a symmetric block encryption algorithm developed in 1998 [56,57]. The algorithm
works with fixed-size data blocks with a key length of 128, 192, or 256 bits. Twofish’s design combines
several characteristics to make it secure and efficient. Using the Feistel cipher structure, a popular
method for block ciphers is one such aspect. TwoFish employs four S-boxes, which replace input
bits with output bits based on the key and round number. The S-boxes are developed from extended
Rijndael S-boxes, which depend on the properties of finite fields.
The authors performed ten tests for the Hyper-V and KVM hypervisors using various keys (128,
192, and 256-bit), CPU core counts (2 and 4), and file sizes. Table 11 compares the average response
time and CPU usage of two hypervisors, KVM and Hyper-V, during encryption and decryption. The
findings show that KVM consumes fewer CPU for encryption and takes the same time for Hyper-V.
KVM surpasses Hyper-V regarding both time and CPU use for decryption operations. KVM reveals
a 3.3 difference in CPU use for encryption and a 0.1 in time. These results imply that KVM is a
more efficient and effective hypervisor for completing cryptographic operations using the TWOFISH
technique, even if the key, data size, or core counts are modified.
Table 11: TWOFISH performance
TwoFish Encryption Decryption
Time CPU Time CPU
Hyper-V 5.6 48.1 5.6 48.6
KVM 5.6 44.8 5.5 46.3
Differences 0 3.3 0.1 3.3
4.7 Overall Results
The overall performance time and CPU utilization during encryption and decryption for both
Hyper-V and KVM hypervisors of all cryptosystems and all previous tables are presented in Tabl e 12
and Fig. 2.
After applying Eq. (1) to compare Hyper-V and KVM, the results show that KVM performs
better than Hyper-V in terms of encryption time by 10.5% ((8.1 −7.3)/7.3) and decryption time by
15.4% ((9.7 −8.4)/8.4), resulting in an overall average of 12.95%. Similarly, KVM shows lower CPU
utilization than Hyper-V during encryption with a ratio of 10.5% ((51.4 −46.5)/46.5) and 9.9% ((51.8
−47.1)/47.1) during decryption, resulting in an overall average of 10.2%.

CMC, 2024, vol.78, no.2 2039
Table 12: Overall algorithms
Overall algorithms Time CPU
Encryption Decryption Encryption Decryption
KVM 7.3 8.4 46.5 47.1
Hyper-V 8.1 9.7 51.4 51.8
Figure 2: Performance of all algorithms in terms of time (A), and CPU usage (B)
Moreover, Tab l e 1 3 demonstrates a performance comparison of the six cryptography algorithms
being compared (i.e., RSA, AES, TripleDES, CAST-128, BlowFish, and TwoFish Algorithms) using
KVM and Hyper-V. Fig. 3 shows the overall time performance in m:s for each algorithm while encryp-
tion and decryption, and Fig. 4 shows the CPU utilization in % while encryption and decryption.
Table 13: Performance of cryptographic algorithms on KVM and Hyper-V
Time CPU
Encryption Decryption Encryption Decryption
KVM Hyper-V KVM Hyper-V KVM Hyper-V KVM Hyper-V
RSA 20.5 24.2 30.5 38.1 70.2 73.6 68.1 71.7
AES 3.1 3.5 3 3.5 41 45.4 42.6 47.4
TripleDES 11.6 11.9 11.7 12.1 44.9 48.2 46.4 49.1
CAST-
128
1.9 2.1 1.9 2 40.3 46.3 41 48.3
BlowFish 6.3 7.4 6 7.1 45.7 50.5 47.1 51.5
TwoFish 5.6 5.6 5.5 5.6 44.8 48.1 46.3 48.6
To compare our results with other studies, we choose the paper [24] published in 2022 as a relevant
reference, because it also investigated the performance of different cryptographic algorithms on VMs.
In [24], the authors studied eight different cryptographic algorithms; RSA, AES, RC4, CAST-128,
TripleDES, DES, TwoFish, and BlowFish, while this study excluded the DES and RC4 algorithms
because they are no longer safe. The authors in [24] compared Xen and Hyper-V, while this study
compares Hyper-V and KVM, as they are the most commonly used hypervisors. In [24], they found

2040 CMC, 2024, vol.78, no.2
that Hyper-V outperforms Xen for most results during encryption and decryption operations, while
this study shows that KVM outperforms Hyper-V in terms of time duration and CPU usage for all
encryption and decryption operations. Tabl e 1 4 shows the comparison of this study with [24]:
Figure 3: Overall time performance of cryptography algorithms in m:s during encryption and
decryption
Figure 4: Overall CPU utilization of cryptography algorithms in % during encryption and decryption
Table 14: Comparison with other studies
Ref. Tested hypervisor Tested algorithms Results
[24] Xen and Hyper-V RSA, AES, RC4, CAST-128,
TripleDES, DES, TwoFish,
and BlowFish.
Hyper-VisbetterthanXen
for most results.
(Continued)

CMC, 2024, vol.78, no.2 2041
Table 14 (continued)
Ref. Tested hypervisor Tested algorithms Results
This study Hyper-V and
KVM
RSA, AES, CAST-128,
TripleDES, TwoFish, and
BlowFish.
KVM is better than
Hyper-V for all results.
5Conclusion
Cryptographic methods are essential for preserving data security and privacy in the cloud
environment. This study evaluated the efficiency of two widely used hypervisors, Hyper-V and KVM,
in implementing six cryptographic algorithms: RSA, AES, TripleDES, CAST-128, BLOWFISH, and
TwoFish. The study measured response time and CPU utilization during encryption and decryption
processes. The results show that KVM surpasses Hyper-V in terms of time duration for all the
experiments and CPU utilization for all algorithms, especially with more cores. It is important to
remember that various variables might affect how well virtualization solutions work, including the
hardware and software configurations utilized in the study. The results also support the prevailing
view that using the KVM hypervisor is better than using Hyper-V for VMs since KVM works with
the Linux kernel, making it more lightweight than the Windows kernel. Thus, it is essential to conduct
further research to confirm these findings and explore other factors that may impact the performance
of virtualization technologies. The following future works can also be undertaken:
•Examine the performance of using other hypervisors, such as VMware or VMWare ESX.
•Examine the performance of other cryptographic algorithms, such as Salsa20 and Curve25519,
in cloud environments.
•Study and compare the performance of different versions of Hyper-V and KVM.
•Study the possibility of improving the effectiveness of encryption and decryption on the
hypervisor using artificial intelligence.
The study had some limitations, such as:
•Specific software and hardware configurations were used during the study. Different settings
may produce different results.
•The effects of network congestion, bandwidth, and latency on the encryption and decryption
operations were not taken into account in this investigation. These factors could have an impact
on cloud environments’ quality of service and user experience, particularly for apps that need
data transmission and communication in real-time or almost real-time.
Acknowledgement: Not applicable.
Funding Statement: The authors received no specific funding for this study.
Author Contributions: Study conception and design: Nader Abdel Karim, Waleed K. Abdulra-
heem, Osama A. Khashan; data collection: Hasan Kanaker, Mahmoud Farfoura, Mohammad
Alshinwan; analysis and interpretation of results: Waleed K. Abdulraheem, Nader Abdel Karim,
Osama A. Khashan, Moutaz Alazab; draft manuscript preparation: Nader Abdel Karim, Waleed K.

2042 CMC, 2024, vol.78, no.2
Abdulraheem, Osama A. Khashan, Moutaz Alazab. All authors reviewed the results and approved
the final version of the manuscript.
Availability of Data and Materials: Not applicable.
Conflicts of Interest: The authors declare they have no conflicts of interest to report regarding the
present study.
References
[1] H. Shukur, S. Zeebaree, R. Zebari, D. Zeebaree, O. Ahmed and A. Salih, “Cloud computing virtualization
of resources allocation for distributed systems,” J. Appl. Sci. Technol. Trends, vol. 1, no. 3, pp. 98–105, 2020.
doi: 10.29121/granthaalayah.v8.i8.2020.926.
[2] T. G. Peter Mell, “The NIST definition of cloud computing,” Reco. Natl. Inst. Stand. Technol., pp. 1–3,
2011. Accessed: Aug. 10, 2023. [Online]. Available: http://faculty.winthrop.edu/domanm/csci411/Handouts/
NIST.pdf
[3] N. Tissir, S. E. Kafhali, and N. Aboutabit, “Cybersecurity management in cloud computing: Semantic
literature review and conceptual framework proposal,” J. Reliab. Intell. Environ., vol. 7, no. 2, pp. 69–84,
Jun. 2021. doi: 10.1007/s40860-020-00115-0.
[4] M. Herman et al., “NIST cloud computing forensic science challenges,” US Depart. Commer., Natl. Inst.
Stand. Technol., 2020. doi: 10.6028/NIST.IR.8006.
[5] M. Quraishi, E. Tavakoli, and F. Ren, “A survey of system architectures and techniques for
FPGA virtualization,” IEEE Trans. Parallel Distrib. Syst., vol. 32, no. 9, pp. 2216–2230, 2021. doi:
10.1109/TPDS.2021.3063670.
[6] A. Khayer, M. S. Talukder, Y. Bao, and M. N. Hossain, “Cloud computing adoption and its impact on
SMEs’ performance for cloud supported operations: A dual-stage analytical approach,” Technol. Soc.,vol.
60, pp. 101225, 2020. doi: 10.1016/j.techsoc.2019.101225.
[7] L. Columbus, 83% of Enterprise Workloads Will Be in the Cloud by 2020. Forbes, 2018. Accessed: Aug.
10, 2023. [Online]. Available: https://www.forbes.com/sites/louiscolumbus/2018/01/07/83-of-enterprise-
workloadswill-be-in-the- cloud-by-2020
[8] N. Sreenivasulu, “Cloud computing statistics & trends for 2023,” 2023. Accessed: Oct. 15, 2023. [Online].
Available: https://www.vpnhelpers.com/cloud-computing-statistics/
[9] N. M. Al-Ramahi, M. Odeh, Z. Alrabie, and N. Qozmar, “The TOEQCC framework for sustainable
adoption of cloud computing at higher education institutions in the Kingdom of Jordan,” Sustain.,vol.
14, no. 19, pp. 12744, 2022. doi: 10.3390/su141912744.
[10] S. Kent, “Federal cloud computing strategy (June 24, 2019),” 2019. Accessed: Jul. 12, 2023. [Online].
Available: https://www.whitehouse.gov/wp-content/uploads/2019/06/Cloud-Strategy.pdf
[11] S. Louvros, M. Paraskevas, and T. Chrysikos, “QoS-aware resource management in 5G and 6G cloud-based
architectures with priorities,” Inform., vol. 14, no. 3, pp. 175, Mar. 2023. doi: 10.3390/info14030175.
[12] K. Filippou, G. Aifantis, G. A. Papakostas, and G. E. Tsekouras, “Structure learning and hyperparameter
optimization using an automated machine learning (AutoML) pipeline,” Inform., vol. 14, no. 4, pp. 232,
Apr. 2023. doi: 10.3390/info14040232.
[13] P. Bir, S. V. Karatangi, and A. Rai, “Design and implementation of an elastic processor with hyperthreading
technology and virtualization for elastic server models,”J. Supercomput., vol. 76, no. 9, pp. 7394–7415, 2020.
doi: 10.1007/s11227-020-03174-5.
[14] H. Akbar, M. Zubair, and M. S. Malik, “The security issues and challenges in cloud computing,” Int. J.
Electron. Crime Investig., vol. 7, no. 1, pp. 13–32, 2023. doi: 10.54692/ijeci.2023.0701125.
[15] D. Silva, J. Rafael, and A. Fonte, “Virtualization maturity in creating system VM: An updated performance
evaluation,” J. Electr. Comput. Eng. Res., vol. 3, no. 2, pp. 7–17, 2023. doi: 10.53375/ijecer.2023.341.

CMC, 2024, vol.78, no.2 2043
[16] A. Ullah, N. M. Nawi, and S. Ouhame, “Recent advancement in VM task allocation system for cloud
computing: Review from 2015 to 2021,” Artif. Intell. Rev., vol. 55, no. 3, pp. 2529–2573, 2022. doi:
10.1007/s10462-021-10071-7.
[17] J. Li, S. Xue, W. Zhang, R. Ma, Z. Qi and H. Guan, “When I/O interrupt becomes system bottleneck:
Efficiency and scalability enhancement for SR-IOV network virtualization,” IEEE Trans. Cloud Comput.,
vol. 7, no. 4, pp. 1183–1196, 2017. doi: 10.1109/TCC.2017.2712686.
[18] S. Ali, “Virtualization with KVM,” in Practical Linux Infrastructure, 1st ed. Cham, Switzerland: Springer
Nature, 2015, pp. 53–80.
[19] R. Morabito, J. Kjällman, and M. Komu, “Hypervisors vs. lightweight virtualization: A performance
comparison,” in Proc. IEEE Int. Conf. Cloud Eng. (IC2E), Tempe, Arizona, USA, 2015, pp. 386–393.
[20] Huawei Technologies Co., Ltd., “Virtualization technology,” in Cloud Computing Technology,1sted.Cham,
Switzerland: Springer Nature, 2022, pp. 97–144.
[21] J. Hwang, S. Zeng, F. Y. Wu, and T. Wood, “A component-based performance comparison of four
hypervisors,” in Proc. IFIP/IEEE Int. Symp. Integr. Netw. Manag. (IM 2013), Ghent, Belgium, 2013, pp.
269–276.
[22] B. Djordjevic, V. Timcenko, N. Kraljevic, and N. Macek, “File system performance comparison in full
hardware virtualization with ESXi, KVM, Hyper-V and Xen hypervisors,” Adv. Electr. Comput. Eng.,vol.
21, no. 1, pp. 11–20, 2021. doi: 10.4316/AECE.2021.01002.
[23] C. Jiang et al., “Energy efficiency comparison of hypervisors,” Sustain. Comput.: Inform. Syst., vol. 22, pp.
311–321, 2019. doi: 10.1016/j.suscom.2017.09.005.
[24] W. K. Abdulraheem, “Performance comparison of Xen AND hyper-V in cloud computing while using cryp-
tosystems,”Int. J. Adv. Soft Comput. Appl., vol. 14, no. 3, pp. 17–30, 2022. doi: 10.15849/IJASCA.221128.02.
[25] V. K. Manik and D. Arora, “Performance comparison of commercial VMM: ESXI, XEN, HYPER-V &
KVM,” in Proc. Int. Conf. Comput. Sustain. Global Develop. (INDIACom), Bharati Vidyapeeth, New Delhi,
India, 2016, pp. 1771–1775.
[26] P. K. Das, “Comparative study on XEN, KVM, VSphere, and Hyper-V,” Emerging Research Sur-
rounding Power Consumption and Performance Issues in Utility Computing, pp. 233–261, 2016. doi:
10.4018/978-1-4666-8853-7.ch011.
[27] P. Kumar, G. P. Gupta, R. Tripathi, S. Garg, and M. M. Hassan, “DLTIF: Deep learning-driven cyber threat
intelligence modeling and identification framework in IoT-enabled maritime transportation systems,”IEEE
Trans. Intell. Transp. Syst., vol. 24, no. 2, pp. 2472–2481, 2023. doi: 10.1109/TITS.2021.3122368.
[28] P. Kumar, R. Tripathi, and G. P. Gupta, “P2IDF: A privacy-preserving based intrusion detection frame-
work for software defined Internet of Things-fog (SDIoT-Fog),”in Proc. Int. Conf. Distrib. Comput. Netw.
(ICDCN), Quezon City, Philippines, 2021, pp. 37–42.
[29] T. Liu, B. Di, P. An, and L. Song, “Privacy-preserving incentive mechanism design for feder-
ated cloud-edge learning,” IEEE Trans. Netw. Sci. Eng., vol. 8, no. 3, pp. 2588–2600, 2021. doi:
10.1109/TNSE.2021.3100096.
[30] Z. Yan, Z. Peng, and V. V. Athanasios, “A security and trust framework for virtualized networks
and software-defined networking,” Secur. Commun. Netw., vol. 5, no. 16, pp. 3059–3069, 2016. doi:
10.1002/sec.1243.
[31] N. A. Karim and A. H. Ali, “E-learning virtual meeting applications: A comparative study from a
cybersecurity perspective,” Indones. J. Electr. Eng. Comput. Sci., vol. 24, no. 2, pp. 1121–1129, 2021. doi:
10.11591/ijeecs.v24.i2.
[32] N. A. Karim, H. Kanaker, S. Almasadeh, and J. Zarqou, “A robust user authentication technique in online
examination,” Int. J. Comput., vol. 20, no. 4, pp. 535–542, 2021. doi: 10.47839/ijc.20.4.2441.
[33] S. S. Gaur, H. S. Kalsi, and S. Gautam, “A comparative study and analysis of cryptographic algorithms:
RSA, DES, AES, BLOWFISH, 3-DES, and TWOFISH,” Int. J. Res. Electron. Comput. Eng.,vol.7,no.1,
pp. 996–999, 2019.

2044 CMC, 2024, vol.78, no.2
[34] A. Martín, A. Hernández, M. Alazab, J. Jung, and D. Camacho, “Evolving generative adversarial
networks to improve image steganography,” Expert Syst. Appl., vol. 222, pp. 119841, Jul. 2023. doi:
10.1016/j.eswa.2023.119841.
[35] R. di Pietro and F. Lombardi, “Virtualization technologies and cloud security: Advantages, issues,
and perspectives,” in Lecture Notes in Computer Science, vol. 11170, pp. 166–185, 2018. doi:
10.1007/978-3-030-04834-1_9
[36] F. Thabit, S. Alhomdy, and S. Jagtap, “Security analysis and performance evaluation of a new lightweight
cryptographic algorithm for cloud computing,” Glob. Transit. Proc., vol. 2, no. 1, pp. 100–110, 2021. doi:
10.1016/j.gltp.2021.01.014.
[37] R. R. Bhandari and N. Mishra, “Cloud computing a CRM service based on separate encryption and
decryption using Blowfish algorithm,” Int. J. Recent Innov. Trends Comput. Commun.,vol.1,no.4,pp.
217–223, 2013. doi: 10.17762/ijritcc.v1i4.2766.
[38] P. Chinnasamy and P. Deepalakshmi, “HCAC-EHR: Hybrid cryptographic access control for secure
EHR retrieval in healthcare cloud,” J. Amb. Intel. Hum. Comp., vol. 13, no. 2, pp. 1001–1019, 2021. doi:
10.1007/s12652-021-02942-2.
[39] R. Dwivedi, R. K. Kumar, and R. Buyya, “Secure healthcare monitoring sensor cloud with attribute-based
elliptical curve cryptography,” Int. J. Cloud Appl. Comput. (IJCAC), vol. 11, no. 3, pp. 1–18, 2021. doi:
10.4018/IJCAC.2021070101.
[40] S. Vinothkumar, J. Amutharaj, and S. Jeyabalan, “A comprehensive study of cryptography and key
management based security in cloud computing,” J. Contemp. Issues Bus. Gov., vol. 27, no. 3, pp. 1909–
1923, 2021. doi: 10.31838/jcr.07.14.412.
[41] D. Suresha and K. Karibasappa, “Enhancing data protection in cloud computing using key derivation
based on cryptographic technique,” in Proc. Int. Conf. Comput. Methodol. Commun. (ICCMC), Erode,
India, 2021, pp. 291–299.
[42] A. Orobosade, T. Aderonke, A. Boniface, and A. J. Gabriel, “Cloud application security using hybrid
encryption,” Commun. Appl. Electron., vol. 7, no. 33, pp. 25–31, 2020. doi: 10.5120/cae2020652866.
[43] M. S. Abbas, S. S. Mahdi, and S. A. Hussien, “Security improvement of cloud data using hybrid
cryptography and steganography,” in Proc. Int. Conf. Comput. Sci. Softw. Eng. (CSASE), Duhok, Iraq,
2020, pp. 123–127.
[44] F. S. and N. K. J. Sumit Chaudhary, “Comparative study between cryptographic and hybrid techniques for
implementation of security in cloud computing,” in Performance Management of Integrated Systems and
its Applications in Software Engineering, Cham, Switzerland: Asset Analytics, Springer Nature, 2019, vol.
22, pp. 127–135. doi: 10.1007/978-981-13-8253-6_12.
[45] S. Zaineldeen and A. Ate, “Review of cryptography in cloud computing,” Int. J. Comput. Sci. Mobile
Comput., vol. 9, no. 3, pp. 211–220, 2020. doi: 10.47760/ijcsmc.
[46] U. Ogiela, “Cognitive cryptography for data security in cloud computing,” Concurr. Comput. Pract. Exp.,
vol. 32, no. 18, pp. e5557, 2020. doi: 10.1002/cpe.5557.
[47] A. K. Singh and D. Saxena, “A cryptography and machine learning based authentication for secure data-
sharing in federated cloud services environment,” J. Appl. Secur. Res., vol. 11, no. 3, pp. 1–24, 2021. doi:
10.1080/19361610.2020.1870404.
[48] W. G. V. L. R. W. Gaspar, “Private AI: Machine learning on encrypted data—Ericsson,” Ericsson, 2021.
Accessed: Aug. 19, 2023. [Online]. Available: https://www.ericsson.com/en/blog/2021/9/machine-learning-
on-encrypted-data
[49] M. U. E. Mrbullwinkle, “Azure OpenAI service encryption of data at rest—Azure AI services | Microsoft
Learn,” Microsoft, 2023. Accessed: Aug. 19, 2023. [Online]. Available: https://learn.microsoft.com/en-us/
azure/ai-services/openai/encrypt-data-at-rest
[50] W. K. A. Abdulraheem, “Comparative analysis of the performance for cloud computing hypervisors with
encrypted algorithms,” M.S. thesis, Middle East Univ., Jordan, 2014.
[51] E. Barker and W. Barker, Recommendation for Key Management, Part 2: Best Practices for Key Management
Organization. Gaithersburg, Maryland, USA: National Institute of Standards and Technology, 2018.

CMC, 2024, vol.78, no.2 2045
[52] W. K. AbdulRaheem, S. B. M. Yasin, N. I. B. Udzir, and M. R. B. K. Ariffin, “Improving the performance
of [0, 1, 3]-NAF recoding algorithm for elliptic curve scalar multiplication,” Int. J. Adv. Comput. Sci. Appl.,
vol. 10, no. 4, pp. 275–279, 2019. doi: 10.14569/IJACSA.2019.0100432.
[53] W. K. AbdulRaheem, S. B. M. Yasin, N. I. B. Udzir, and M. R. B. K. Ariffin, “New quintupling point
arithmetic 5P formulas for Lôpez-Dahab coordinate over binary elliptic curve cryptography,” Int. J. Adv.
Comput. Sci. Appl., vol. 10, no. 7, pp. 397–401, 2019. doi: 10.14569/IJACSA.2019.0100754.
[54] Y. M. Kuo, F. Garcia-Herrero, O. Ruano, and J. A. Maestro, “RISC-V galois field ISA extension for non-
binary error-correction codes and classical and post-quantum cryptography,” IEEE Trans. Comput.,vol.
72, no. 3, pp. 682–692, 2023. doi: 10.1109/TC.2022.3174587.
[55] G. Hatzivasilis, K. Fysarakis, I. Papaefstathiou, and C. Manifavas, “A review of lightweight block ciphers,”
J. Cryptogr. Eng., vol. 8, pp. 141–184, 2018. doi: 10.1007/s13389-017-0160-y.
[56] M. E. Haque, S. Zobaed, M. U. Islam, and F. M. Areef, “Performance analysis of cryptographic algorithms
for selecting better utilization on resource constraint devices,” in Proc. Int. Conf. Comput. Inf. Technol.
(ICCIT), Dhaka, Bangladesh, 2018, pp. 1–6.
[57] U. Chibueze Nwamouh, B. O. Sadiq, K. U. John, and S. N. Ndubuisi, “A comparative analysis of symmetric
cryptographic algorithm as a data security tool: A survey,” J. Sci. Technol. Res., vol. 5, no. 3, pp. 2023–2144,
2023. doi: 10.5281/zenodo.8313097.