• Spatial Information Research
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• v.19 no.4
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• pp.55-71
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• 2011

Recently, interest in cloud computing which provides IT resources as service form in IT field is increasing. As a result, much research has been done on the distributed data processing that store and manage a large amount of data in many servers. Meanwhile, in order to effectively utilize the spatial data which is rapidly increasing day by day with the growth of GIS technology, distributed processing of spatial data using cloud computing is essential. Therefore, in this paper, we review the representative distributed data processing techniques and we analyze the optimization requirements for performance improvement of the distributed processing techniques for a large amount of data. In addition, we uses the Hadoop and we evaluate the performance of the distributed data processing techniques for their optimization requirements.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.4
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• pp.113-124
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• 2009

A research of grid computing that is the combination of computer resources from multiple distributed administrative domains to get large amount of computing power is underway. This research is an application of grid technology on a urban facilities management system based on u-GIS. The sensors are set up in the urban facilities to make it monitoring. If an amount of sensors and gateways is increased, the server needs more computing resources to process the data. In this study we developed the skills that can distribute jobs to idle gateway, in case of the server capacity had approached threshold. It will be possible to develop of economic and efficient system that will apply to large amount of data processing about u-City.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.4
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• pp.107-113
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• 2012

Cloud computing has brought changes to the IT environment. Due to the spread of LTE, users of cloud services are growing more. This which provides IT resources to meet the needs of users of cloud services are noted as a core industry. But it is not activated because of the security of personal data and the safety of the service. In order to solve this, intrusion detection system is constructed as follows. This protects individual data safely which exists in the cloud and also protects information exhaustively from malicious attack. The cause of most attack risk which exists to cloud computing can find in distributed environment. In this study, we analyzed about necessary property of network-based intrusion detection system that process and analyze large amount of data which occur in cloud computing environment. Also, we studied functions which detect and correspond attack occurred in interior of virtualization.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5669-5684
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• 2018

The new mobile edge network architecture has been required for an increasing amount of traffic, quality requirements, advanced driver assistance system for autonomous driving and new cloud computing demands on highway. This article proposes a hierarchical cloud computing architecture to enhance performance by using adaptive data load distribution for buses that play the role of edge computing server. A vehicular dynamic cloud is based on wireless architecture including Wireless Local Area Network and Long Term Evolution Advanced communication is used for data transmission between moving buses and cars. The main advantages of the proposed architecture include both a reduction of data loading for top layer cloud server and effective data distribution on traffic jam highway where moving vehicles require video on demand (VOD) services from server. Through the description of real environment based on NS-2 network simulation, we conducted experiments to validate the proposed new architecture. Moreover, we show the feasibility and effectiveness for the connected car media service on highway.

• Electronics and Telecommunications Trends
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• v.33 no.1
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• pp.20-33
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• 2018

The calculation speed of quantum computing is expected to outperform that of existing supercomputers with regard to certain problems such as secure computing, optimization problems, searching, and quantum chemistry. Many companies such as Google and IBM have been trying to make 50 superconducting qubits, which is expected to demonstrate quantum supremacy and those quantum computers are more advantageous in computing power than classical computers. However, quantum computers are expected to be applicable to solving real-world problems with superior computing power. This will require large scale quantum computing with many more qubits than the current 50 qubits available. To realize this, first, quantum error correction codes are required to be capable of computing within a sufficient amount of time with tolerable accuracy. Next, a compiler is required for the qubits encoded by quantum error correction codes to perform quantum operations. A large-scale quantum computer is therefore predicted to be composed of three essential components: a programming environment, layout mapping of qubits, and quantum processors. These components analyze how many numbers of qubits are needed, how accurate the qubit operations are, and where they are placed and operated. In this paper, recent progress on large-scale quantum computing and the relation of their components will be introduced.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.211-232
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• 2024

In this paper, we consider the resource allocation and offloading decisions of device-to-device (D2D) cooperative UAV-assisted mobile edge computing (MEC) system, where the device with task request is served by unmanned aerial vehicle (UAV) equipped with MEC server and D2D device with idle resources. On the one hand, to ensure the fairness of time-delay sensitive devices, when UAV computing resources are relatively sufficient, an optimization model is established to minimize the maximum delay of device computing tasks. The original non-convex objective problem is decomposed into two subproblems, and the suboptimal solution of the optimization problem is obtained by alternate iteration of two subproblems. On the other hand, when the device only needs to complete the task within a tolerable delay, we consider the offloading priorities of task to minimize UAV computing resources. Then we build the model of joint offloading decision and power allocation optimization. Through theoretical analysis based on KKT conditions, we elicit the relationship between the amount of computing task data and the optimal resource allocation. The simulation results show that the D2D cooperation scheme proposed in this paper is effective in reducing the completion delay of computing tasks and saving UAV computing resources.

• ETRI Journal
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• v.37 no.2
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• pp.212-221
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• 2015

As the amount of re-sequencing genome data grows, minimizing the execution time of an analysis is required. For this purpose, recent computing systems have been adopting both high-performance coprocessors and host processors. However, there are few applications that efficiently utilize these heterogeneous computing resources. This problem equally refers to the work of single nucleotide polymorphism (SNP) detection, which is one of the bottlenecks in genome data processing. In this paper, we propose a method for speeding up an SNP detection by enhancing the utilization of heterogeneous computing resources often used in recent high-performance computing systems. Through the measurement of workload in the detection procedure, we divide the SNP detection into several task groups suitable for each computing resource. These task groups are scheduled using a window overlapping method. As a result, we improved upon the speedup achieved by previous open source applications by a magnitude of 10.

• Smart Media Journal
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• v.8 no.1
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• pp.35-42
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• 2019

With IoT device technology developments, such devices now can perceive the surrounding environment and operate upon the condition, but a method for efficiently processing an enormous amount of IoT device data is required. The existing cloud computing has a transmission delay problem due to load and distance. Fog Computing, an environment to control IoT devices, therefore, emerged to solve this problem. In Fog Computing, IoT devices are located close to each other to solve the shortcomings of the cloud system. While many earlier studies on Fog Computing for IoT mainly focus on its structure and framework, we would like to propose an integrated Fog Computing platform that monitors, analyzes, and controls IoT devices.

• Journal of Engineering Education Research
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• v.26 no.1
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• pp.37-44
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• 2023

Recently, the Internet of Things is attracting attention as an important key technology of the 4th Industrial Revolution, and SW education using physical computing is suggested as a good alternative to supplement the problems raised by beginners in programming education. Among the many teaching tools that can be used for physical computing education, MODI is a modular manufacturing tool that anyone can easily assemble like Lego. MODI is a teaching tool that can improve learners' achievement by linking a self-linked block-type code editor called MODI Studio to lay the foundation for programming in a relatively small amount of time and immediately check the results in person. In this paper, a physical computing education method using MODI was designed to be applied to basic programming courses for programming beginners and applied to after-school classes for middle school students. As a result, it was found that students' interest and satisfaction were much higher in physical computing classes using MODI than in text-based programming classes. It can be seen that physical computing education that allows beginners to see and feel the results in person is more effective than grammar-oriented text programming, and it can have a positive effect on improving basic programming skills by increasing students' participation.

• The Journal of the Korea Contents Association
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• v.14 no.5
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• pp.8-17
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• 2014

Petascale systems(so called supercomputers) have been mainly used for supporting communication-intensive and tightly-coupled parallel computations based on message passing interfaces such as MPI(HPC: High-Performance Computing). On the other hand, computing paradigms such as High-Throughput Computing(HTC) mainly target compute-intensive (relatively low I/O requirements) applications consisting of many loosely-coupled tasks(there is no communication needed between them). In Korea, recently emerging applications from various scientific fields such as pharmaceutical domain, high-energy physics, and nuclear physics require a very large amount of computing power that cannot be supported by a single type of computing resources. In this paper, we present our HTCaaS(High-Throughput Computing as a Service) which can leverage national distributed computing resources in Korea to support these challenging HTC applications and describe the details of our system architecture, job execution scenario and case studies of various scientific applications.