• 정보처리학회논문지A
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• 제18A권1호
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• pp.11-18
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• 2011

클라우드 컴퓨팅은 자원 공유를 위한 새로운 컴퓨팅 패러다임이다. 클라우드 서비스를 위해 사용 하는 다양한 어플리케이션들은 워크플로우들로 표현된다. 이러한 워크플로우 어플리케이션은 클라우드의 자원 또는 서비스들에 적절하게 할당되어야 한다. 본 논문에서는 클라우드 컴퓨팅 환경을 고려하여 다중 워크플로우 어플리케이션을 위한 새로운 스케줄링 알고리즘을 제안한다. 제안하는 알고리즘은 비용 기반 랭크 스케줄링 알고리즘으로서 다중 워크플로우 어플리케이션을 고려할 뿐만 아니라, 서비스 평가를 위한 다양한 QoS 메트릭을 고려한다. 실험결과에서 제안한 알고리즘은 다른 알고리즘들에 비해 평균 총 처리시간과 평균 가용성에서 향상된 결과를 보였다.

• International Journal of Advanced Culture Technology
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• 제9권3호
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• pp.321-326
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• 2021

In a multi-cloud environment, it is necessary to minimize physical movement for efficient interoperability of distributed source data without building a data warehouse or data lake. And there is a need for a data platform that can easily access data anywhere in a multi-cloud environment. In this paper, we propose a new platform based on data fabric centered on a distributed platform suitable for cloud environments that overcomes the limitations of legacy systems. This platform applies the knowledge graph database technique to the physical linkage of source data for interoperability of distributed data. And by integrating all data into one scalable platform in a multi-cloud environment, it uses the holochain technique so that companies can easily access and move data with security and authority guaranteed regardless of where the data is stored. The knowledge graph database mitigates the problem of heterogeneous conflicts of data interoperability in a decentralized environment, and Holochain accelerates the memory and security processing process on traditional blockchains. In this way, data access and sharing of more distributed data interoperability becomes flexible, and metadata matching flexibility is effectively handled.

• 대한임베디드공학회논문지
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• 제16권3호
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• pp.107-111
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• 2021

A 3D point cloud map is an essential elements in various fields, including precise autonomous navigation system. However, generating a 3D point cloud map using a single sensor has limitations due to the price of expensive sensor. In order to solve this problem, we propose a precise 3D mapping system using low-cost sensor fusion. Generating a point cloud map requires the process of estimating the current position and attitude, and describing the surrounding environment. In this paper, we utilized a commercial visual-inertial odometry sensor to estimate the current position and attitude states. Based on the state value, the 2D LiDAR measurement values describe the surrounding environment to create a point cloud map. To analyze the performance of the proposed algorithm, we compared the performance of the proposed algorithm and the 3D LiDAR-based SLAM (simultaneous localization and mapping) algorithm. As a result, it was confirmed that a precise 3D point cloud map can be generated with the low-cost sensor fusion system proposed in this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권5호
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• pp.2754-2767
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• 2019

Cloud computing is widely used in information spreading and processing, which has provided a easy and quick way for users to access data and retrieve service. Generally, in order to prevent the leakage of the information, the data in cloud is transferred in the encrypted form. As one of the traditional security technologies, access control is an important part for cloud security. However, the current access control schemes are not suitable for cloud, thus, it is a vital problem to design an access control scheme which should take account of complex factors to satisfy the various requirements for cipher text protection. We present a novel access control scheme based on proxy re-encryption(PRE) technology (PreBAC) for cipher text. It will suitable for the protection of data confidently and information privacy. At first, We will give the motivations and related works, and then specify system model for our scheme. Secondly, the algorithms are given and security of our scheme is proved. Finally, the comparisons between other schemes are made to show the advantages of PreBAC.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권3호
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• pp.1141-1163
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• 2019

In an effort to minimize operational expenses and supply users with more scalable services, distributed applications are actually going towards the Cloud. These applications, sent out over multiple environments and machines, are composed by inter-connecting independently developed services and components. The implementation of such programs on the Cloud is difficult and generally carried out either by hand or perhaps by composing personalized scripts. This is extremely error prone plus it has been found that misconfiguration may be the root of huge mistakes. We introduce AutoBot, a flexible platform for modeling, installing and (re)configuring complex distributed cloud-based applications which evolve dynamically in time. AutoBot includes three modules: A simple and new model describing the configuration properties and interdependencies of components; a dynamic protocol for the deployment and configuration ensuring appropriate resolution of these interdependencies; a runtime system that guarantee the proper configuration of the program on many virtual machines and, if necessary, the reconfiguration of the deployed system. This reduces the manual application deployment process that is monotonous and prone to errors. Some validation experiments were conducted on AutoBot in order to ensure that the proposed system works as expected. We also discuss the opportunity of reusing the platform in the transition of applications from Cloud to Fog computing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.2282-2303
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• 2021

To solve the problems of heavy computing load and system transmission pressure in energy internet (EI), we establish a three-tier cloud-edge integrated EI network based on a cloud-edge collaborative computing to achieve the tradeoff between energy consumption and the system delay. A joint optimization problem for resource allocation and task offloading in the threetier cloud-edge integrated EI network is formulated to minimize the total system cost under the constraints of the task scheduling binary variables of each sensor node, the maximum uplink transmit power of each sensor node, the limited computation capability of the sensor node and the maximum computation resource of each edge server, which is a Mixed Integer Non-linear Programming (MINLP) problem. To solve the problem, we propose a joint task offloading and resource allocation algorithm (JTOARA), which is decomposed into three subproblems including the uplink transmission power allocation sub-problem, the computation resource allocation sub-problem, and the offloading scheme selection subproblem. Then, the power allocation of each sensor node is achieved by bisection search algorithm, which has a fast convergence. While the computation resource allocation is derived by line optimization method and convex optimization theory. Finally, to achieve the optimal task offloading, we propose a cloud-edge collaborative computation offloading schemes based on game theory and prove the existence of Nash Equilibrium. The simulation results demonstrate that our proposed algorithm can improve output performance as comparing with the conventional algorithms, and its performance is close to the that of the enumerative algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권6호
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• pp.1877-1891
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• 2022

As a part of cloud computing technology, algorithms for cloud task scheduling place an important influence on the area of cloud computing in data centers. In our earlier work, we proposed DeepEnergyJS, which was designed based on the original version of the policy gradient and reinforcement learning algorithm. We verified its effectiveness through simulation experiments. In this study, we used the Proximal Policy Optimization (PPO) algorithm to update DeepEnergyJS to DeepEnergyJSV2.0. First, we verify the convergence of the PPO algorithm on the dataset of Alibaba Cluster Data V2018. Then we contrast it with reinforcement learning algorithm in terms of convergence rate, converged value, and stability. The results indicate that PPO performed better in training and test data sets compared with reinforcement learning algorithm, as well as other general heuristic algorithms, such as First Fit, Random, and Tetris. DeepEnergyJSV2.0 achieves better energy efficiency than DeepEnergyJS by about 7.814%.

• 한국산업융합학회 논문집
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• 제26권6_3호
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• pp.1325-1332
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• 2023

Recently, much research is being conducted based on point cloud data for the growth of innovations such as construction automation in the transportation field and virtual national space. This data is often measured through remote control in terrain that is difficult for humans to access using devices such as UAVs and UGVs. Drones, one of the UAVs, are mainly used to acquire point cloud data, but photogrammetry using a vision camera, which takes a lot of time to create a point cloud map, is difficult to apply in construction sites where the terrain changes periodically and surveying is difficult. In this paper, we developed a point cloud mapping system by adopting non-repetitive scanning LiDAR and attempted to confirm improvements through field application. For accuracy analysis, a point cloud map was created through a 2 minute 40 second flight and about 30 seconds of software post-processing on a terrain measuring 144.5 × 138.8 m. As a result of comparing the actual measured distance for structures with an average of 4 m, an average error of 4.3 cm was recorded, confirming that the performance was within the error range applicable to the field.

• 한국정보통신학회논문지
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• 제15권9호
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• pp.1910-1917
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• 2011

본 논문에서는 오픈 소스 클라우드 플랫폼인 OpenNebula 기반의 클라우드 컴퓨팅 환경을 구축하고 클러스터 노드에 설치되는 하이퍼바이저로 Xen을 이용하여 클라우드 가상화를 이용한 가상 컴퓨터 실습시스템을 설계하고 구현하였으며, 관리의 편의성을 제공하고자 웹기반 인터페이스를 개발하였다. 학기 중 주별로 정해진 강의 시간표에 따라 미리 준비된 이미지를 사용하여 사용자별 가상머신을 할당하면 사용자는 원격 데스크톱을 이용하여 구동된 가상머신에 접속하여 원하는 운영체제 및 어플리케이션 프로그램을 이용할 수 있게 된다. 본 시스템을 활용하면 기존의 컴퓨터 교육실습실의 업그레이드와 관리 비용 및 시간을 대폭 줄일 수 있다.

• 한국항행학회논문지
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• 제13권5호
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• pp.781-786
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• 2009

클라우드 컴퓨팅 환경은 웹을 기반으로 한 응용분야의 새로운 트랜드이다. 이는 많은 사용자들이 쉽게 인터넷을 통해 자원을 할당받고 서비스를 지원받을 수 있는 IT 비즈니스 모델이라 할 수 있다. 또한 u-러닝은 인터넷 기반 학습의 효율성을 극대화한 모델이다. 따라서 본 연구는 이를 인터넷 기반 학습에 응용하는 u-러닝 교수 학습 모형의 설계를 제시하고자 한다. 제안된 교수 학습 모형은 u-러닝에 맞도록 준비, 계획, 수집, 학습 진행, 분석 및 평가, 피드백의 7단계로 구성하였다. 이는 클라우드 u-러닝 서버와 클라우드 LMS를 두어 처리 및 관리하도록 하였으며, 학습자의 이동형 기기 모델의 인식을 위하여 이동형 기기 메타데이터를 두도록 하였다.