• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5357-5381
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• 2018

Cloud computing offers a wide range of on-demand resources over the internet. Utility-based resource allocation in cloud data centers significantly increases the number of cloud users. Heavy usage of cloud data center encounters many problems such as sacrificing system performance, increasing operational cost and high-energy consumption. Therefore, the result of the system damages the environment extremely due to heavy carbon (CO2) emission. However, dynamic allocation of energy-efficient resources in cloud data centers overcomes these problems. In this paper, we have proposed Energy and Service Level Agreement (SLA) Aware Resource Allocation Heuristic Algorithms. These algorithms are essential for reducing power consumption and SLA violation without diminishing the performance and Quality-of-Service (QoS) in cloud data centers. Our proposed model is organized as follows: a) SLA violation detection model is used to prevent Virtual Machines (VMs) from overloaded and underloaded host usage; b) for reducing power consumption of VMs, we have introduced Enhanced minPower and maxUtilization (EMPMU) VM migration policy; and c) efficient utilization of cloud resources and VM placement are achieved using SLA-aware Modified Best Fit Decreasing (MBFD) algorithm. We have validated our test results using CloudSim toolkit 3.0.3. Finally, experimental results have shown better resource utilization, reduced energy consumption and SLA violation in heterogeneous dynamic cloud environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1520-1542
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• 2020

Traditional software testing typically uses many physical resources to manually build various test environments, resulting in high resource costs and long test time due to limited resources, especially for small enterprises. Cloud computing can provide sufficient low-cost virtual resources to alleviate these problems through the virtualization of physical resources. However, the provision of various test environments and services for implementing software testing rapidly and conveniently based on cloud computing is challenging. This paper proposes a multilayer cloud testing model based on cloud computing and implements a hybrid cloud testing system based on virtual machines (VMs) and networks. This system realizes the automatic and rapid creation of test environments and the remote use of test tools and test services. We conduct experiments on this system and evaluate its applicability in terms of the VM provision time, VM performance and virtual network performance. The experimental results demonstrate that the performance of the VMs and virtual networks is satisfactory and that this system can improve the test efficiency and reduce test costs through rapid virtual resource provision and convenient test services.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.599-612
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• 2019

Cloud networks are used to provide various services. As services are increasingly deployed using cloud networks, there are a number of resources in the cloud that leverage a variety of environments and protocols. However, there is a security intrusion on these resources, and research on cloud network vulnerability detection is required as threats to cloud resources emerge. In this paper, we propose a vulnerability detection scheme using STRIDE and HARM for vulnerability detection of resources utilizing various environments and protocols, and present cloud network vulnerability detection scheme through vulnerability detection scenario composition.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.147-158
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• 2022

Cloud Computing offers flexible, on demand, ubiquitous resources for cloud users. Cloud users are provided computing resources in a virtualized environment. In order to meet the growing demands for computing resources, data centres contain a large number of physical machines accommodating multiple virtual machines. However, cloud data centres cannot utilize their computing resources to their total capacity. Several policies have been proposed for improving energy proficiency and computing resource utilization in cloud data centres. Virtual machine placement is an effective method involving efficient mapping of virtual machines to physical machines. However, the availability of many physical machines accommodating multiple virtual machines in a data centre has made the virtual machine placement problem a non deterministic polynomial time hard (NP hard) problem. Metaheuristic algorithms have been widely used to solve the NP hard problems of multiple and conflicting objectives, such as the virtual machine placement problem. In this context, we presented essential concepts regarding virtual machine placement and objective functions for optimizing different parameters. This paper provides a taxonomy of metaheuristic algorithms for the virtual machine placement method. It is followed by a review of prominent research of virtual machine placement methods using meta heuristic algorithms and comparing them. Finally, this paper provides a conclusion and future research directions in virtual machine placement of cloud computing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.5780-5802
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• 2017

Cloud computing system consists of distributed resources in a dynamic and decentralized environment. Therefore, using cloud computing resources efficiently and getting the maximum profits are still challenging problems to the cloud service providers and cloud service users. It is important to provide the efficient scheduling. To schedule cloud resources, numerous heuristic algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Cuckoo Search (CS) algorithms have been adopted. The paper proposes a Modified Particle Swarm Optimization (MPSO) algorithm to solve the above mentioned issues. We first formulate an optimization problem and propose a Modified PSO optimization technique. The performance of MPSO was evaluated against PSO, and GA. Our experimental results show that the proposed MPSO minimizes the task execution time, and maximizes the resource utilization rate.

• Electronics and Telecommunications Trends
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• v.35 no.3
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• pp.45-54
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• 2020

The cloud computing industry has focused on establishing a cloud-based business environment for enterprises with efforts to convert using their own on-premise computing infrastructures to using cloud services. With these efforts, using cloud services has become natural, especially for the IT industry. The cloud computing industry is moving toward proliferation of the cloud computing environment into various evolving industries. Along with industrial trends, new technical trends such as edge computing and multi-cloud are emerging. These trends are expected to create new business models and develop related service ecosystems, providing new opportunities for service providers and new experiences for users. A mong those emerging technologies, multi-cloud technology is expected to realize unlimited global cloud computing resources by unifying cloud resources from multiple public cloud service providers. In this paper, we introduce the concept and related trends of multi-cloud technology. Subsequently, we analyze the main functionalities and several use cases of multi-cloud technology. Finally, we summarize the effects and usefulness of multi-cloud technology in the domestic cloud industry.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2649-2666
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• 2015

In the dynamic cloud computing environment, to ensure, under the terms of service-level agreements, the maximum efficiency of resource utilization, it is necessary to investigate the online dynamic management of virtual machine resources and their operational application systems/components. In this study, the feasibility and properties of the division of virtual machine resources on the cloud platform, using the virtual machine cluster as the management unit, are investigated. First, the definitions of virtual machine clusters are compared, and our own definitions are presented. Then, the feasibility of division using the virtual machine cluster as the management unit is described, and the isomorphism and reconfigurability of the clusters are proven. Lastly, from the perspectives of clustering and cluster segmentation, the dynamics of virtual machines are described and experimentally compared. This study aims to provide novel methods and approaches to the optimization management of virtual machine resources and the optimization configuration of the parameters of virtual machine resources and their application systems/components in large-scale cloud computing environments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5404-5424
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• 2018

This work is mainly focused on two major topics in cloud platforms by using OpenStack as a case study: management and provisioning of resources to meet the requirements of a service demanded by remote end-user and relocation of virtual machines (VMs) requests to offload the encumbered compute nodes. The general framework architecture contains two subsystems: 1) An orchestrator that allows to systematize provisioning and resource management in OpenStack, and 2) A resource utilization based subsystem for vibrant VM relocation in OpenStack. The suggested orchestrator provisions and manages resources by: 1) manipulating application program interfaces (APIs) delivered by the cloud supplier in order to allocate/control/manage storage and compute resources; 2) interrelating with software-defined networking (SDN) controller to acquire the details of the accessible resources, and training the variations/rules to manage the network based on the requirements of cloud service. For resource provisioning, an algorithm is suggested, which provisions resources on the basis of unused resources in a pool of VMs. A sub-system is suggested for VM relocation in a cloud computing platform. The framework decides the proposed overload recognition, VM allocation algorithms for VM relocation in clouds and VM selection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.685-703
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• 2024

Cloud computing provides each consumer with a large-scale computing tool. Different Cyber Attacks can potentially target cloud computing systems, as most cloud computing systems offer services to many people who are not known to be trustworthy. Therefore, to protect that Virtual Machine from threats, a cloud computing system must incorporate some security monitoring framework. There is a tradeoff between the security level of the security system and the performance of the system in this scenario. If strong security is needed, then the service of stronger security using more rules or patterns is provided, since it needs much more computing resources. A new way of security system is introduced in this work in cloud environments to the VM on account of resources allocated to customers are ease. The main spike of Fog computing is part of the cloud server's work in the ongoing study tells the step-by-step cloud server to change the tremendous measurement of information because the endeavor apps are relocated to the cloud to keep the framework cost. The cloud server is devouring and changing a huge measure of information step by step to reduce complications. The Medical Data Health-Care (MDHC) records are stored in Cloud datacenters and Fog layer based on the guard intensity and the key is provoked for ingress the file. The monitoring center sustains the Activity Log, Risk Table, and Health Records. Cloud computing and Fog computing were combined in this paper to review data movement and safe information about MDHC.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1265-1278
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• 2017

Dynamic task scheduling is one of the most popular research topics in the cloud computing field. The cloud scheduler dynamically provides VM resources to variable cloud tasks with different scheduling strategies in cloud computing. In this study, we utilized a valid model to describe the dynamic changes of both computing facilities (such as hardware updating) and request task queuing. We built a novel approach called Policy Iteration Scheduling (PIS) to globally optimize the independent task scheduling scheme and minimize the total execution time of priority tasks. We performed experiments with randomly generated cloud task sets and varied the performance of VM resources using Poisson distributions. The results show that PIS outperforms other popular schedulers in a typical cloud computing environment.