• KIISE Transactions on Computing Practices
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• v.20 no.12
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• pp.658-663
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• 2014

Due to the rapid development of open source based Cloud management platforms such as OpenStack, many and various individuals and institutions are trying to construct Cloud computing systems based on open source software projects. It is essential, for those who install, administrate or operate a Cloud, to validate the integrity of the constructed Cloud system. This paper, therefore, proposes the design of a testing tool for validating the integrity of a constructed OpenStack-based Cloud system. Especially, the proposed testing tool utilizes the Tempest project which is an open source project that consists of OpenStack test cases. The proposed testing tool is expected to help developing Cloud technology and Cloud users.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2434-2454
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• 2021

This paper developed the software testing factory-cloud testing platform (STF-CTP) to address the software compatible issues in various smart devices. Software developers who only require uploading the application under test (AUT) and test script can test plenty of smart devices in STF-CTP. The challenge for the cloud test platform is how to optimize the resource and increase the performance in the limited resource. This paper proposed a new scheduling mechanism and a new process of the system operation which is based on the OpenStack platform. We decrease about 40% memory usage of OpenStack server, increase 3% to 10% Android device usage of STF-CTP, enhance about 80% test job throughput and reduces about 40% test job average waiting time.

• Korean Journal of Remote Sensing
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• v.28 no.4
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• pp.467-475
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• 2012

Cloud computing services are known that they have many advantages from the point of view in economic saving, scalability, security, sharing and accessibility. So their applications are extending from simple office systems to the expert system for scientific computing. However, research or computing technology development in the geo-spatial fields including remote sensing applications are the beginning stage. In this work, the previously implemented smartphone app for image processing was first migrated to mobile cloud computing linked to Amazon web services. As well, parallel programming was applied for improving operation performance. Industrial needs and technology development cases in terms of mobile cloud computing services are being increased. Thus, a performance testing on a satellite image processing module was carried out as the main purpose of this study. Types of implementation or services for mobile cloud varies. As the result of this testing study in a given condition, the performance of cloud computing server was higher than that of the single server without cloud service. This work is a preliminary case study for the further linkage approach for mobile cloud and satellite image processing.

• Journal of Information Technology Applications and Management
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• v.20 no.2
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• pp.57-68
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• 2013

Recently, cloud computing has been viewed as a technology of great influence. Despite the importance of personal cloud services, the majority of cloud computing studies has been focused on an enterprise level. Therefore, in this research, we list variables of personal cloud computing characteristics (accessibility, storage, trust, perceived ease of use, availability) from preceding research done on an enterprise level. So, through pilot testing, we analyze whether examined characteristic variables affect the intention to use personal cloud computing. Next, we investigate the relevancy between user characteristics, including gender, age, and occupation, and the usage rate of personal cloud computing. Lastly, we implement an analysis of variance in order to speculate whether there is any difference between the five characteristic variables in using personal cloud computing between cloud service enterprises. For empirical testing people with experience in personal cloud computing are surveyed. Frequency analysis, exploratory factor analysis, internal consistency analysis, T-test, ANOVA, and multiple regression analysis are implemented with 325 valid samples. The analysis results are as follows: First, among user characteristic variables, 'occupation' shows a significant relationship with cloud service usage, Second, between cloud service enterprises, all five characteristic variables of 'Naver N Drive' are thoroughly evaluated and 'Google Drive' receives a high evaluation in availability and accessibility. Third and finally, all of the selected characteristic variables affect the intention to use personal cloud computing. However, availability is identified to be the most important factor. This research will serve as a reference to enterprises for the practical implementation of personal cloud computing service.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2682-2700
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• 2023

To cope with the risks of climate change and promote the realization of carbon peaking and carbon neutrality, this paper first comprehensively considers the policy background, technical trends and carbon reduction paths of energy conservation and emission reduction in data center server industry. Second, we propose a computing power carbon efficiency of data center server, and constructs the carbon emission per performance of server (CEPS) model. According to the model, this paper selects the mainstream data center servers for testing. The result shows that with the improvement of server performance, the total carbon emissions are rising. However, the speed of performance improvement is faster than that of carbon emission, hence the relative carbon emission per unit computing power shows a continuous decreasing trend. Moreover, there are some differences between different products, and it is calculated that the carbon emission per unit performance is 20-60KG when the service life of the server is five years.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.136-141
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• 2023

Artificial intelligence is becoming a global hot topic and is being actively applied in various industrial fields. Not only is artificial intelligence being applied to industrial sites in an on-premises method, but cloud-based artificial intelligence platforms are expanding into "as a service" type. The purpose of this study is to develop and verify a measurement tool for an evaluation framework for the adoption of a cloud-based artificial intelligence platform and test the interrelationships of evaluation variables. To achieve this purpose, empirical testing was conducted to verify the hypothesis using an expanded technology acceptance model, and factors affecting the intention to adopt a cloud-based artificial intelligence platform were analyzed. The results of this study are intended to increase user awareness of cloud-based artificial intelligence platforms and help various industries adopt them through the evaluation framework.

• The Journal of Korea Robotics Society
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• v.16 no.3
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• pp.189-198
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• 2021

In this paper, we introduce the software/hardware system that can reliably calculate the distance from sensor to the model regardless of point cloud density. As the 3d point cloud map is widely adopted for SLAM and computer vision, the accuracy of point cloud map is of great importance. However, the 3D point cloud map obtained from Lidar may reveal different point cloud density depending on the choice of sensor, measurement distance and the object shape. Currently, when measuring map accuracy, high reflective bands are used to generate specific points in point cloud map where distances are measured manually. This manual process is time and labor consuming being highly affected by Lidar sparsity level. To overcome these problems, this paper presents a hardware design that leverage high intensity point from three planar surface. Furthermore, by calculating distance from sensor to the device, we verified that the automated method is much faster than the manual procedure and robust to sparsity by testing with RGB-D camera and Lidar. As will be shown, the system performance is not limited to indoor environment by progressing the experiment using Lidar sensor at outdoor environment.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.2
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• pp.173-178
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• 2015

Cloud systems are efficient models that can utilize various infrastructures, platforms, and applications regardless of the type of clients. This paper proposes a cloud-based integrated development environment (IDE) for robot software development which would make software development easier. The proposed system provides robot simulation to test the robot HW modules or robot systems for development and testing of software operating in a robot system with two or more different operating systems (OS) such as Windows, Linux, and real-time OS. This paper implements and evaluates the proposed system using OPRoS [33].

• International Journal of Contents
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• v.13 no.2
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• pp.29-34
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• 2017

Cloud computing is becoming an effective and efficient way of computing resources and computing service integration. Through centralized management of resources and services, cloud computing delivers hosted services over the internet, such that access to shared hardware, software, applications, information, and all resources is elastically provided to the consumer on-demand. The main enabling technology for cloud computing is virtualization. Virtualization software creates a temporarily simulated or extended version of computing and network resources. The objectives of virtualization are as follows: first, to fully utilize the shared resources by applying partitioning and time-sharing; second, to centralize resource management; third, to enhance cloud data center agility and provide the required scalability and elasticity for on-demand capabilities; fourth, to improve testing and running software diagnostics on different operating platforms; and fifth, to improve the portability of applications and workload migration capabilities. One of the key features of cloud computing is elasticity. It enables users to create and remove virtual computing resources dynamically according to the changing demand, but it is not easy to make a decision regarding the right amount of resources. Indeed, proper provisioning of the resources to applications is an important issue in IaaS cloud computing. Most web applications encounter large and fluctuating task requests. In predictable situations, the resources can be provisioned in advance through capacity planning techniques. But in case of unplanned and spike requests, it would be desirable to automatically scale the resources, called auto-scaling, which adjusts the resources allocated to applications based on its need at any given time. This would free the user from the burden of deciding how many resources are necessary each time. In this work, we propose an analytical and efficient VM-level scaling scheme by modeling each VM in a data center as an M/M/1 processor sharing queue. Our proposed VM-level scaling scheme is validated via a numerical experiment.