• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.3
• /
• pp.1002-1015
• /
• 2018

The advent of Internet of Things (IoT) and the evident inadequacy of Cloud networks concerning management of numerous end nodes have brought about a shift of paradigm giving birth to Fog computing. Fog computing is an extension of Cloud computing that extends Cloud resources at the edge of the network, closer to the user. Cloud computing has become one of the essential needs of people over the Internet but with the emerging concept of IoT, traditional Clouds seem inadequate. IoT entails extremely low latency and for that, the Cloud servers that are distant and unknown to the user appear to be unsuitable. With the help of Fog computing, the Fog devices installed would be closer to the user that will provide an immediate storage for the frequently needed data. This paper discusses data migration between different storage types especially between Cloud devices and then presents a mechanism to migrate data between Cloud and Fog Layer. We call this mechanism Adaptive Deadline-Aware Scheme (ADAS) for Data migration between Cloud and Fog. We will demonstrate that we can access and process latency sensitive "hot" data through the proposed ADAS more efficiently than with a traditional Cloud setup.

• Journal of KIISE
• /
• v.43 no.9
• /
• pp.1052-1059
• /
• 2016

A key role in cloud computing systems that is becoming an issue is implementing a database on untrusted cloud servers requiring the complexity of key management. This study proposes a key management system using Self Proxy Servers to minimize key executions and improve the performance of cloud services by generating Self-Creating Algorithms where the data owner is not directly concerned with related keys when a user sends an encrypted database a query. The Self Proxy Server supports active and autonomous key managements as a distributed server if any trouble should arise from a cloud key server and for an efficient cloud key management. Therefore, the key management system provides secure cloud services by supporting confidentiality of a cloud server database.

• Annual Conference of KIPS
• /
• 2012.04a
• /
• pp.499-502
• /
• 2012

빅 데이터 분석의 시대가 도래하면서 대용량 데이터의 특성과 계산 집약적 연산의 특성을 동시에 가지는 문제 해결에 대한 요구가 늘어나고 있다. 대용량 데이터 처리의 경우 각종 분산 파일 시스템과 분산/병렬 컴퓨팅 기술들이 이미 많이 사용되고 있으며, 계산 집약적 연산 처리의 경우에도 GPGPU 활용 기술의 발달로 보편화되는 추세에 있다. 하지만 대용량 데이터와 계산 집약적 연산 이 두 가지 특성을 모두 가지는 문제를 처리하기 위해서는 많은 제약 사항들을 해결해야 하는데, 본 논문에서는 이에 대한 대안으로 분산 컴퓨팅 프레임워크인 Hadoop MapReduce와 Nvidia의 GPU 병렬 컴퓨팅 아키텍처인 CUDA 흘 연동하는 방안을 제시하고, 이를 밀집행렬(dense matrix) 연산에 적용했을 때 얻을 수 있는 성능 개선 효과에 대해 소개하고자 한다.

• International Journal of Internet, Broadcasting and Communication
• /
• v.13 no.1
• /
• pp.201-209
• /
• 2021

The term, eGovernmen or e-Government, uses technology communications devices such as computers and the Internet to provide public services to citizens and others. The eGovernment or e-government provides citizens with new opportunities to access the government directly and conveniently, while the government provides citizens with directservices. Also, in these days, cloud computing is a feature that enables users to use computer system resources, especially data storage (cloud storage) and on-demand computing power, without having to manage themselves. The term is commonly used to describe data centers that are available to many users over the Internet. Today, the dominant Big Cloud is distributed across multiple central servers. You can designate it as an Edge server if it is relatively close to the user. However, despite the prevalence of e-government and cloud computing, each of these concepts has evolved. Research attempts to combine these two concepts were not being made properly. For this reason, in this work, we aim to produce independent and objective analysis results by separating progress steps for the analysis of e-government cloud service platforms. This work will be done through an analysis of the development process and architectural composition of the e-government development standard framework and the cloud platform PaaS-TA. In addition, this study is expected to derive implications from an analysis perspective on the direction and service composition of the e-government cloud service platform currently being pursued.

• The Bulletin of The Korean Astronomical Society
• /
• v.38 no.2
• /
• pp.49.1-49.1
• /
• 2013

We present a new galaxy classification scheme in the Wide-field Infrared Survey Explorer (WISE) [$3.4{\mu}m$]-[$12{\mu}m$] color versus $12{\mu}m$ luminosity diagram. In this diagram, galaxies can be classified into three groups in different evolutionary stages. Late-type galaxies are distributed linearly along "MIR star-forming sequence" identified by Hwang et al. (2012). Some early-type galaxies show another sequence at [3.4]-[12] $(AB){\simeq}-2.0$, and we call this 'MIR blue sequence'. They are quiescent systems with old stellar population older than 10 Gyr. Between the MIR star-forming sequence and the MIR blue sequence, some early- and late-type galaxies are sparsely distributed, and we call these galaxies 'MIR green cloud galaxies'. Interestingly, both MIR blue sequence galaxies and MIR green cloud ones lie on the red sequence in the optical color-magnitude diagram. However, MIR green cloud galaxies have lower stellar masses and younger stellar populations (smaller $D_n4000$) than MIR blue sequence galaxies, suggesting that MIR green cloud galaxies are in the transition stage from MIR star-forming sequence galaxies to MIR blue sequence ones. We present differences in various galaxy properties between the three MIR classes using a multi-wavelength data, combined with the WISE and Sloan Digital Sky Survey Data Release 10, of local (0.03 < z < 0.07) galaxies.

• International journal of advanced smart convergence
• /
• v.10 no.4
• /
• pp.278-288
• /
• 2021

Recently, IoT systems are cloud-based, so that continuous and large amounts of data collected from sensor nodes are processed in the data server through the cloud. However, in the centralized configuration of large-scale cloud computing, computational processing must be performed at a physical location where data collection and processing take place, and the need for edge computers to reduce the network load of the cloud system is gradually expanding. In this paper, a cluster system consisting of 6 inexpensive Raspberry Pi boards was constructed to perform fast data processing. And we propose "Kubernetes cluster system(KCS)" for processing large data collection and analysis by model distribution and data pipeline method. To compare the performance of this study, an ensemble model of deep learning was built, and the accuracy, processing performance, and processing time through the proposed KCS system and model distribution were compared and analyzed. As a result, the ensemble model was excellent in accuracy, but the KCS implemented as a data pipeline proved to be superior in processing speed..

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.153-155
• /
• 2003

Laser scanning has become a viable technique for the collection of a large amount of accurate 3D point data densely distributed on the scanned object surface. The inherent 3D nature of the sub-randomly distributed point cloud provides abundant spatial information. To explore valuable spatial information from laser scanned data becomes an active research topic, for instance extracting digital elevation model, building models, and vegetation volumes. The sub-randomly distributed point cloud should be segmented and classified before the extraction of spatial information. This paper investigates some exist segmentation methods, and then proposes an octree-based split-and-merge segmentation method to divide lidar data into clusters belonging to 3D planes. Therefore, the classification of lidar data can be performed based on the derived attributes of extracted 3D planes. The test results of both ground and airborne lidar data show the potential of applying this method to extract spatial features from lidar data.

• Journal of Internet Computing and Services
• /
• v.13 no.5
• /
• pp.55-64
• /
• 2012

Nowadays, as Cloud computing becomes popular, a need for a DFS(distributed file system) is increased. But, in the current Cloud computing environments, there is no DFS framework that is sufficient to protect sensitive private information from attackers. Therefore, we designed and proposed a secure scheme for distributed file systems. The scheme provides confidentiality and availability for a distributed file system using a secret sharing method. In this paper, we measured the speed of encryption and decryption for our proposed method, and compared them with that of SEED algorithm which is the most popular algorithm in this field. This comparison showed the computational efficiency of our method. Moreover, the proposed secure read/write model is independent of Hadoop DFS structure so that our modified algorithm can be easily adapted for use in the HDFS. Finally, the proposed model is evaluated theoretically using performance measurement method for distributed secret sharing model.

• International Journal of Internet, Broadcasting and Communication
• /
• v.7 no.1
• /
• pp.42-48
• /
• 2015

Generally, the current commercial cloud system is hosted by the centralization large datacenter. However, the other clouding forms existed and one of them is the P2P cloud. The P2P Cloud is a distributed systems, is freedom to join and leave and is possible to provide the IaaS. Commonly, P2P Cloud System is based on Pure-P2P System. Since a number of connection paths exist, it has a high reliability and easily scalability of the network. However, there are disadvantages of the slow rate of route because of looking up the all peers in order to search for the peer. In this paper, it proposes the P2P cloud system based on super-peer P2P. Super-peer P2P system has fast routing time, because of searching for cluster unit and it also can has high reliability and high scalability. We designs the super Peer cloud service and proposes the system model and Resource Allocation Algorithm for IaaS in Super peer P2P environment.

• Proceedings of Korean Society of Archives and Records Management
• /
• 2019.05a
• /
• pp.51-54
• /
• 2019

The transition of the central government system to the cloud-based infrastructure is being conducted as the National Information Resources Service (NIRS, Ministry of the Interior and Safety) attempts government resource integration. In the early days, it was attempted as Infrastructure as a Service (IaaS) for cloud service for HW, but now it is being converted to Software as a Service (SaaS) for ordinary service. General tasks of the system are document creation for government service as well as public records management. To this end, government cloud systems were deployed to central government agencies from 2015 to 2018 and planned to be gradually deployed in 2018. Nowadays, records management is conducted with cloud RMS, the cloud RMS system is distributed to the central government from 2016 to 2018, and the dissemination to local government is scheduled to be carried out considering the adoption of On-Nara system.