• Proceedings of the Korean Information Science Society Conference
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• 한국정보과학회 2005년도 가을 학술발표논문집 Vol.32 No.2 (1)
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• pp.802-804
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• 2005

유비쿼터스 환경에 대한 관심과 휴대용 컴퓨팅 기기의 보급으로 가까운 미래의 사람들은 언제 어디서나 네트워크를 통해 데이터 접근이 가능하게 될 것이다. 특히 영화나 음악과 같은 멀티미디어 데이터의 폭발적인 증가로 인해 확장성 있는 네트워크 스토리지 시스템의 필요성이 부각되고 있다. SAN(Storage Area Network)은 높은 확장성과 빠른 속도를 제공하여 엔터프라이즈 환경에 적합한 스토리지 네트워크 시스템이다. 최근에 SAN 환경은 SCSI Architecture Model(SAM)의 표준으로 채택된 iSCSI를 이용한 IP기반의 SAN으로 옳겨가고 있다. 본 논문에서는 iSCSI 기반의 IP SAN환경에서 서비스 클라이언트가 증가함에 따라 스토리지 서버의 부하가 커지는 문제를 해결하기 위해 스토리지 디바이스에서 클라이언트로 데이터를 직접 전송하는 방안을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 한국정보처리학회 2008년도 추계학술발표대회
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• pp.1055-1058
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• 2008

URC는 유비쿼터스 환경의 네트워크 기반 로봇이다. URC 로봇은 네트워크를 통해 외부 디바이스에 서비스를 요청하고 기능을 제공 받을 수 있는 환경을 가진다. 이에 따라 URC 로봇을 관리하고 서비스를 제공할 수 있는 외부 디바이스의 중요성이 증가 할 것이다. 본 논문은 유비쿼터스 환경의 URC 로봇 클라이언트의 서비스 요청에 대한 기능을 제공 할 수 있는 외부 디바이스인 URC 서버의 구조에 대해 제안하며, 제안하는 서버는 OSGi 프레임워크를 기반으로 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제5권5호
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• pp.938-958
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• 2011

Ubiquitous web services have been expanding in various business areas with the evolution of wireless Internet technologies, accessible and usable with a variety of mobile Internet devices such as smart phones. Ubiquitous mobile-web information services can be evaluated for accessibility and usability with the mobile Internet devices interacting with mobile-web information servers. In human mobile-web activity, a web server could be a unified center for mobile-web interaction services as well as for real-time estimation and analysis of mobile-web interaction sessions. We present a real-time estimation and analysis scheme for time-based accessibility and usability in ubiquitous mobile-web services. With real-time estimation/analysis of sessions in a mobile-web server, we estimated the time-based accessibility and usability for comparison between different web services as well as for applications in mobile cloud computing services. We present empirical results based on the implementation of the real-time estimation/analysis scheme.

• Journal of KIISE:Computing Practices and Letters
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• 제8권6호
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• pp.619-629
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• 2002

This paper describes the implementation of a RTSP-based MPEG-4 streaming system. The implemented system consists of content authoring tool, streaming server and streaming client. The existing MPEG-4 multiplexer has been modified with improvement to generate a file format with which the server load and streaming delay are reduced. The streaming system uses RTSP, instead of DMIF, as its communication protocol. With RTSP, the server provides extensive control functionalities as in DMIF and its implementation effort is greatly simplified. The implemented system supports the user interactivity for both streaming and local file playback. The effectiveness of the proposed multiplexing scheme has been validated with the various experiments on the fully functional server.

• Journal of Information Technology Services
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• 제11권2호
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• pp.107-118
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• 2012

Recently, as the clouding computing begins to receive a great attention from people all over the world, it became the most popular buzz word in recent IT magazines or journal and heard it in many different services or different fields. However, a notion of the cloud service is defined vaguely compared to increasing attentions from others. Generally the cloud service could be understood as a specific service model base on the clouding computing, but the cloud, the cloud computing, the cloud computing service and cloud service, these four all terms are often used without any distinction of its notions and characteristics so that it's difficult to define the exact nature of the cloud service. To explore and analyze the cloud service systematically, an accurate conception and scope have to be preceded. Therefore this study is to firstly clarify its definition by Delpi method using expert group and then tries to provide the foundation needed to enable relative research such as establishing business model or value chain and policies for its activation to set off. For the Delpi, 16 experts participated in several surveys from different fields such industry, academy and research sector. As a result of the research, Characteristics of the Cloud Service are followings : Pay per use, Scalability, Internet centric Virtualization. And the scope as defined including Grid Computing, Utility Computing, Server Based Computing, Network Computing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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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.

• Journal of the Korea Institute of Information and Communication Engineering
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• 제12권5호
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• pp.942-947
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• 2008

A cluster server usually consists of a front end node and multiple backend nodes. Though increasing the number of bookend nodes can result in the more QoS(Quality of Service) streams for clients, the possibility of failures in backend nodes is proportionally increased. The failure causes not only the stop of all streaming service but also the loss of the current playing positions. In this paper, when a backend node becomes a failed state, the recovery mechanisms are studied to support the unceasing streaming service. The basic techniques are hewn as providing very high speed data transfer rates suitable for the video streaming. However, without considering the architecture of cluster-based VOD server, the application of these basic techniques causes the performance bottleneck of the internal network for recovery and also results in the inefficiency CPU usage of backend nodes. To resolve these problems, we propose a new failure recovery mechanism based on the pipeline computing concept.

• The Journal of Korean Institute of Communications and Information Sciences
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• 제37C권11호
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• pp.1027-1035
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• 2012

Recently, the smart phone including web and mobile service based on the reliability and extendability of cloud computing is receiving huge attention. However, most of current cloud services provide just an application service for synchronizing data between mobile entity and server. Business model developed by communication companies have problems with interoperability. This paper proposes a new service security authentication model to efficiently manage smart phone users using different business models between smartphones and to keep the reliability and extendability of cloud computing. Proposed model authenticates for smart phone users to stay with in the unified communication with smart phone user's identity and access control to effectively use the current cloud computing system.

• Journal of Information Processing Systems
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• 제17권3호
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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.

• International Journal of Internet, Broadcasting and Communication
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• 제12권1호
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• pp.144-150
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• 2020

Reinforcement learning is a technology that can present successful and creative solutions in many areas. This reinforcement learning technology was used to deploy containers from cloud servers to fog servers to help them learn the maximization of rewards due to reduced traffic. Leveraging reinforcement learning is aimed at predicting traffic in the network and optimizing traffic-based fog computing network environment for cloud, fog and clients. The reinforcement learning system collects network traffic data from the fog server and IoT. Reinforcement learning neural networks, which use collected traffic data as input values, can consist of Long Short-Term Memory (LSTM) neural networks in network environments that support fog computing, to learn time series data and to predict optimized traffic. Description of the input and output values of the traffic-based reinforcement learning LSTM neural network, the composition of the node, the activation function and error function of the hidden layer, the overfitting method, and the optimization algorithm.