• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.9B
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• pp.1066-1072
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• 2011

Cloud Computing Environment provides users with a blue print of IT Utopia with virtualization; unbounded computing power and data storage free from the cost and the responsibility of maintenance for the IT resources. But, there are several issues to be addressed for the Cloud Computing Environment to be realized as the blue print because users cannot control the IT resources provided by the Cloud Computing Environment but can only use them. One of the issues is how to secure and to trust data in the Cloud Computing Environment. In this paper, an efficient and practical trust assurance of data with provenance in Cloud Computing Environment.

• Annual Conference of KIPS
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• 2015.04a
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• pp.112-113
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• 2015

최근, IT 기술의 발달로 다양한 스마트 디바이스 및 서비스 증가로 빅 데이터 시대가 도래되었다. 이에 빅 데이터를 저장하기 위한 많은 연구들이 진행되고 있지만 데이터 저장을 위한 측면에 중점을 두어 본질적인 서버 운용에 대한 연구가 도외시 되고 없다. 또한, 기존의 서버 운용은 장애 발생시 페일 오버나 리던던시를 통해 대용을 하고 있지만, 이러한 기법은 연속적인 장애 발생에 대한 대응이 불가하다. 본 논문에서는 빅 데이터를 저장하기 위한 DSV(Desktop Storage Virtualization) 환경에서 서버 장애를 효율적으로 대응하는 M2S2(Metadata Management for Server Sustainability)를 제안한다. M2S2는 독립적으로 분산된 데스크당의 성능, 거리, 저장된 데이터양을 기준으로 최적의 대체 서버가 수행된다. 또한, 대체 서버의 장애 발생시 하위 데스크탑 중에 최적의 대체 서버가 수행 반복됨으로써 지속적인 서버 운용이 가능하다.

• Annual Conference of KIPS
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• 2015.10a
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• pp.197-198
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• 2015

IT 기술의 진보적 발전에 따라 클라우드 컴퓨팅 분야 연구들이 활발히 진행되고 있다. 클라우드 컴퓨팅은 가상화 기술을 이용하여 크게 인프라, 플랫폼, 소프트웨어 관점으로 나뉘어 사용자에게 다양한 서비스를 제공한다. 가상화 기술 중에 Desktop Storage Virtualization (DSV)은 분산된 레거시 데스크탑으로 구성되어 있기 때문에 비가용 상태 시간별 클러스터링 및 사용자 요청에 따른 자동 확장이 매우 중요시된다. 본 논문에서는 GPU의 many-core를 이용하여 분산된 데스크탑의 성능 상태 분석 및 자동 확장을 위해 스레드별로 호스트를 매핑하고 병렬적으로 처리하는 Rapid Auto Scaling Mechanism (RASM)을 제안한다.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.4
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• pp.347-357
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• 2019

The usefulness of processing and analysis systems of GIS-based agricultural climate data is affected by the reliability and availability of computing infrastructures such as cloud, on-premises, and hybrid. Cloud technology has grown in popularity. However, various reference cases accumulated over the years of operational experiences point out important features that make on-premises technology compatible with cloud technology. Both cloud and on-premises technologies have their advantages and disadvantages in terms of operational time and cost, reliability, and security depending on cases of applications. In this study, we have described characteristics of four general computing platforms including cloud, on-premises with hardware-level virtualization, on-premises with operating system-level virtualization and hybrid environments, and compared them in terms of advantages and disadvantages when a huge amount of GIS-based agricultural climate data were stored and processed to provide public services of agro-meteorological and climate information at high spatial and temporal resolutions. It was found that migrating high-resolution agricultural climate data to public cloud would not be reasonable due to high cost for storing a large amount data that may be of no use in the future. Therefore, we recommended hybrid systems that the on-premises and the cloud environments are combined for data storage and backup systems that incur a major cost, and data analysis, processing and presentation that need operational flexibility, respectively.

• Convergence Security Journal
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• v.14 no.7
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• pp.85-90
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• 2014

Cloud computing originated simply to request and execute the desired operation from the network of clouds. It means that an IT resource that provides a service using the Internet technology. It is getting the most attention in today's IT trends. In cloud computing networks, devices and data centers which are composed of the server, storage and application are connected over network. That is, data of computers in different physical locations are integrated using the virtualization technology to provide a service. Therefore cloud computing system is a key information resource, standardized methods and assessment system are required. In this paper, we aims to derive the parameters and information for research of technical standards stability evaluation method associated with various cloud computing equipment.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.19-29
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• 2012

Currently, cloud computing technology is being supplied in various service forms and it is becoming a ground breaking service which provides usage of storage service, data and software while user is not involved in technical background such as physical location of service or system environment. cloud computing technology has advantages that it can use easily as many IT resources as it wants freely regardless of hardware issues required by a variety of systems and service level required by infrastructure. Also, since it has a strength that it can choose usage of resource about business model due to various internet-based technologies, provisioning technology and virtualization technology are being paid attention as main technologies. These technologies are ones of important technology elements which help web-based users approach freely and install according to user environment. Therefore, this thesis introduces software-related technologies and architectures in an aspect of grid for building up high availability cloud computing environment by analysis about cloud computing technology trend.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.489-504
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• 2021

By distributing computing tasks among devices at the edge of networks, edge computing uses virtualization, distributed computing and parallel computing technologies to enable users dynamically obtain computing power, storage space and other services as needed. Applying edge computing architectures to Internet of Vehicles can effectively alleviate the contradiction among the large amount of computing, low delayed vehicle applications, and the limited and uneven resource distribution of vehicles. In this paper, a predictive offloading strategy based on the MEC load state is proposed, which not only considers reducing the delay of calculation results by the RSU multi-hop backhaul, but also reduces the queuing time of tasks at MEC servers. Firstly, the delay factor and the energy consumption factor are introduced according to the characteristics of tasks, and the cost of local execution and offloading to MEC servers for execution are defined. Then, from the perspective of vehicles, the delay preference factor and the energy consumption preference factor are introduced to define the cost of executing a computing task for another computing task. Furthermore, a mathematical optimization model for minimizing the power overhead is constructed with the constraints of time delay and power consumption. Additionally, the simulated annealing algorithm is utilized to solve the optimization model. The simulation results show that this strategy can effectively reduce the system power consumption by shortening the task execution delay. Finally, we can choose whether to offload computing tasks to MEC server for execution according to the size of two costs. This strategy not only meets the requirements of time delay and energy consumption, but also ensures the lowest cost.