• Journal of Digital Convergence
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• v.15 no.11
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• pp.147-153
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• 2017

The Korean government has implemented various policies to introduce the cloud to the public sector. The objectives of the paper are to develop a decision-making model and to propose the roadmap for cloud introduction in the public sector. To achieve these objectives, we analyze the characteristics of public services and types of cloud service. Then we develope a cloud introduction method using fuzzy AHP based convergence decision-making model. As a result of this study, we decided to prioritize the cloud service candidates and proposed a three-step roadmap. The results are expected to contribute to cloud introduction and transition in the public sector and establishment of the cloud policy. In the future, it will be necessary to develop budget plans as well as additional decision-making factors for cloud adoption.

• Journal of Information Technology and Architecture
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• v.10 no.4
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• pp.509-516
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• 2013

Cloud services are recognized the essential IT infrastructure in the optimal smart society which is changing rapidly as a low-cost and high-efficiency. This service of starting from prominent overseas companies such as Google, Amazon, had influenced on the introduction of the service for the various policies of foreign governments, including the United States and the United Kingdom. Such countries adopting to the cloud computing and make transform to the cloud service of existing public service for the effective management of information resources. In this study, we have proposed the main determining factors of cloud adoption, the model of cloud governance for the adoption of public cloud service.

• The Journal of Information Systems
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• v.31 no.2
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• pp.63-88
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• 2022

Purpose The purpose of this study is to empirically analyze the factors affecting the intention to use cloud computing in the smart work environment. This study is meaningful in that it examines the characteristics of smart work and the fit of cloud computing characteristics, and it is a study that reveals the factors affecting the intention to use cloud computing by applying the integrated model of TTF and TAM. Design/methodology/approach In order to understand the factors affecting the intention to use cloud computing in the smart work environment, a research model that integrates TTF and TAM with the hypothesis was proposed. In order to verify the research hypothesis, this study conducted a survey on individuals with experience in smart work and cloud computing. And with the data 280 collected in the survey, path analysis was performed using the PLS structural equation. Findings As a result, it was found that task characteristics and technology characteristics had a positive (+) effect on task-skill fit, and task-skill fit had a positive (+) effect on perceived ease of use and usefulness. Also, task-skill fit, perceived ease of use, and perceived usefulness were found to have a positive (+) effect on intention to use.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.5
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• pp.80-92
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• 2017

The popularity of cloud computing has led to the emergence of various types of cloud services, and the hybrid cloud, a deployment model that integrates public cloud and private cloud and offset their shortcomings, is in the spotlight recently. However, the complexity of different clouds integration and the lack of related integration solutions have delayed the adoption of hybrid cloud and cloud strategy by companies and organizations. Therefore, in this paper, we propose a cloud integration mechanism to solve the integration complexity problem. The cloud integration mechanism proposed in this paper consists of integration script that solves the cloud integration by the script based on the hybrid cloud function, a process of creating and executing it, and a script creation model applying the software design pattern. By integrating the various cloud services, we can quickly generate scripts that meet the user's needs. It is expected that the introduction of hybrid cloud and the acquisition of cloud strategy can be accelerated through this proposed integration mechanism.

• 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.9
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• pp.3663-3679
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• 2020

Scheduling plays a dynamic role in cloud computing in generating as well as in efficient distribution of the resources of each task. The principle goal of scheduling is to limit resource starvation and to guarantee fairness among the parties using the resources. The demand for resources fluctuates dynamically hence the prearranging of resources is a challenging task. Many task-scheduling approaches have been used in the cloud-computing environment. Security in cloud computing environment is one of the core issue in distributed computing. We have designed a deep learning-based security model for scheduling tasks in cloud computing and it has been implemented using CloudSim 3.0 simulator written in Java and verification of the results from different perspectives, such as response time with and without security factors, makespan, cost, CPU utilization, I/O utilization, Memory utilization, and execution time is compared with Round Robin (RR) and Waited Round Robin (WRR) algorithms.

• ETRI Journal
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• v.36 no.6
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• pp.1008-1015
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• 2014

In this paper, we present a method for reconstructing a surface mesh animation sequence from point cloud animation data. We mainly focus on the articulated body of a subject - the motion of which can be roughly described by its internal skeletal structure. The point cloud data is assumed to be captured independently without any inter-frame correspondence information. Using a template model that resembles the given subject, our basic idea for reconstructing the mesh animation is to deform the template model to fit to the point cloud (on a frame-by-frame basis) while maintaining inter-frame coherence. We first estimate the skeletal motion from the point cloud data. After applying the skeletal motion to the template surface, we refine it to fit to the point cloud data. We demonstrate the viability of the method by applying it to reconstruct a fast dancing motion.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.1
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• pp.217-229
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• 2019

The purpose of the research is to verify how task characteristics for business and technology characteristics, economic feasibility, technology readiness, organizational factors, environmental factors of cloud computing affect the performance of cloud computing adoption through Fit and Viability. The research aims to verify the relationship among the success factors for adopting cloud computing based on the Fit-Viability model. Respondents who work for IT companies which is using cloud computing in South Korea were chosen. The data was analyzed by the structural equating model. As a result, Task characteristics and Technology characteristics affected Fit in a positive manner, while Technology readiness, Organizational factors and Environmental factors also positively impacted Viability. Fit and Viability both affected the successful adoption of cloud equally. In particular, Environmental factors were proven to have the biggest impacts on Viability, and affected highly indirect impact on the Performance of cloud computing adoption through Viability. Entering the era of the fourth industrial revolution, corporations have established digital transformation strategies to secure a competitive edge while growing continuously, and are also carrying out various digital transformation initiatives. For the success of adoption of foundational technologies, they need to understand not only the decision-making factors of adopting cloud computing, but also the success factors of adopting cloud computing.

• Atmosphere
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• v.24 no.1
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• pp.111-122
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• 2014

In the Korea Meteorological Administration earth system model (HadGEM2-AO), cloud drop number concentration is determined from aerosol number concentration according to the observed relationship between aerosol and cloud drop number concentrations. However, the observational dataset used for establishing the relationship was obtained from limited regions of the earth and therefore may not be representative of the entire earth. Here we reestablished the relationship between aerosol and cloud drop number concentrations based on a composite of observational dataset obtained from many different regions around the world that includes the original dataset. The new relationship tends to provide lower cloud drop number concentration for aerosol number concentration < 600 $cm^{-3}$ and the opposite for > 600 $cm^{-3}$. This new empirical relationship was applied to the KMA earth system model and the historical run (1861~2005) is made again. Here only the 30 year (1861~1890) averages from the runs with the new and the original relationships between aerosol and cloud drop number concentrations (newHIST and HIST, respectively) were compared. For this early period aerosol number concentrations were generally lower than 600 $cm^{-3}$ and therefore cloud drop number concentrations were generally lower but cloud drop effective radii were larger for newHIST than for HIST. The results from the complete historical run with the new relationship are expected to show more significant differences from the original historical run.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.235-247
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• 2021

Today, the cloud computing has become a major demand of many organizations. The major reason behind this expansion is due to its cloud's sharing infrastructure with higher computing efficiency, lower cost and higher fle3xibility. But, still the security is being a hurdle that blocks the success of the cloud computing platform. Therefore, a novel Multi-tenant Decentralized Information Flow Control (MT-DIFC) model is introduced in this research work. The proposed system will encapsulate four types of entities: (1) The central authority (CA), (2) The encryption proxy (EP), (3) Cloud server CS and (4) Multi-tenant Cloud virtual machines. Our contribution resides within the encryption proxy (EP). Initially, the trust level of all the users within each of the cloud is computed using the proposed two-stage trust computational model, wherein the user is categorized bas primary and secondary users. The primary and secondary users vary based on the application and data owner's preference. Based on the computed trust level, the access privilege is provided to the cloud users. In EP, the cipher text information flow security strategy is implemented using the blowfish encryption model. For the data encryption as well as decryption, the key generation is the crucial as well as the challenging part. In this research work, a new optimal key generation is carried out within the blowfish encryption Algorithm. In the blowfish encryption Algorithm, both the data encryption as well as decryption is accomplishment using the newly proposed optimal key. The proposed optimal key has been selected using a new Self Improved Cat and Mouse Based Optimizer (SI-CMBO), which has been an advanced version of the standard Cat and Mouse Based Optimizer. The proposed model is validated in terms of encryption time, decryption time, KPA attacks as well.