• Title/Summary/Keyword: network computing

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A Design and Implementation of the Middleware for the Home Network Service (홈 네트워크 서비스 구축을 위한 미들웨어 설계 및 구현)

  • Lee, Seung-Joo
    • The Journal of Information Technology
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    • v.10 no.4
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    • pp.57-67
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    • 2007
  • In this paper, we propose a design and implementation of Home Network Middleware which offer integrated service of electronic products. Most senior nations setting to core paradigm to ubiquitous computing techniques and effort to get the novel information and making more power about information techniques. But, they don't have a skill to Home Network Middleware in electronic products. It is a more important in Home Network Middleware. So, we will try to study about Home Network Middleware skill. And suggest to proposition that is a novel Home Network Middleware.

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A study of network mobility for internet service in railway system (열차에서 이동네트워크 적용 방안)

  • Cho, Bong-Kwan;Jung, Jae-Il
    • Proceedings of the KIEE Conference
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    • 2005.04a
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    • pp.241-243
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    • 2005
  • The study for ubiquitous computing infra is proceeding actively, it make possible to use service and access network anywhere, anytime because of wire/wireless communication technology and progress of hardware. Domestically, study for the network mobility support technology which is the key technology for future ubiquitous computing realization have progressed, but that is insufficient. Especially, there is no study for independent mobility support study about railway wireless network. So, this study propose network mobility management technology for mobile network infra in railway and proper network model in train.

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A cache placement algorithm based on comprehensive utility in big data multi-access edge computing

  • Liu, Yanpei;Huang, Wei;Han, Li;Wang, Liping
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.15 no.11
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    • pp.3892-3912
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    • 2021
  • The recent rapid growth of mobile network traffic places multi-access edge computing in an important position to reduce network load and improve network capacity and service quality. Contrasting with traditional mobile cloud computing, multi-access edge computing includes a base station cooperative cache layer and user cooperative cache layer. Selecting the most appropriate cache content according to actual needs and determining the most appropriate location to optimize the cache performance have emerged as serious issues in multi-access edge computing that must be solved urgently. For this reason, a cache placement algorithm based on comprehensive utility in big data multi-access edge computing (CPBCU) is proposed in this work. Firstly, the cache value generated by cache placement is calculated using the cache capacity, data popularity, and node replacement rate. Secondly, the cache placement problem is then modeled according to the cache value, data object acquisition, and replacement cost. The cache placement model is then transformed into a combinatorial optimization problem and the cache objects are placed on the appropriate data nodes using tabu search algorithm. Finally, to verify the feasibility and effectiveness of the algorithm, a multi-access edge computing experimental environment is built. Experimental results show that CPBCU provides a significant improvement in cache service rate, data response time, and replacement number compared with other cache placement algorithms.

Traffic-based reinforcement learning with neural network algorithm in fog computing environment

  • Jung, Tae-Won;Lee, Jong-Yong;Jung, Kye-Dong
    • International Journal of Internet, Broadcasting and Communication
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    • v.12 no.1
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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.

Torus Network Based Distributed Storage System for Massive Multimedia Contents (토러스 연결망 기반의 대용량 멀티미디어용 분산 스토리지 시스템)

  • Kim, Cheiyol;Kim, Dongoh;Kim, Hongyeon;Kim, Youngkyun;Seo, Daewha
    • Journal of Korea Multimedia Society
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    • v.19 no.8
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    • pp.1487-1497
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    • 2016
  • Explosively growing service of digital multimedia data increases the need for highly scalable low-cost storage. This paper proposes the new storage architecture based on torus network which does not need network switch and erasure coding for efficient storage usage for high scalability and efficient disk utilization. The proposed model has to compensate for the disadvantage of long network latency and network processing overhead of torus network. The proposed storage model was compared to two most popular distributed file system, GlusterFS and Ceph distributed file systems through a prototype implementation. The performance of prototype system shows outstanding results than erasure coding policy of two file systems and mostly even better results than replication policy of them.

Priority-Based Network Interrupt Scheduling for Predictable Real-Time Support

  • Lee, Minsub;Kim, Hyosu;Shin, Insik
    • Journal of Computing Science and Engineering
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    • v.9 no.2
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    • pp.108-117
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    • 2015
  • Interrupt handling is generally separated from process scheduling. This can lead to a scheduling anomaly and priority inversion. The processor can interrupt a higher priority process that is currently executing, in order to handle a network packet reception interruption on behalf of its intended lower priority receiver process. We propose a new network interrupt handling scheme that combines interrupt handling with process scheduling and the priority of the process. The proposed scheme employs techniques to identify the intended receiver process of an incoming packet at an earlier phase. We implement a prototype system of the proposed scheme on Linux 2.6, and our experiment results show that the prototype system supports the predictable real-time behavior of higher priority processes even when excessive traffic is sent to lower priority processes.

A Study on Designing Intelligent Military Decision Aiding System in a Network Computing Environment (네트웍 컴퓨팅 환경하에서의 지능형 군사적 의사결정시스템 구축에 관한 연구)

  • 김용효;박상찬
    • Journal of the military operations research society of Korea
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    • v.24 no.1
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    • pp.18-40
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    • 1998
  • This paper is aimed to design an intelligent military decision aiding system in a network computing environment, especially focusing on designing an intelligent analytic system that has data mining tools and inference engine. Through this study, we concluded that the intelligent analytic system can aid military decision making processes. Highlights of the proposed system are as follows : 1) Decision making time can be reduced by the On-line and Real-time analysis ; 2) Intelligent analysis on military decision problems in network computing environments in enabled; 3) The WWW-based implementation models, which provide a standard user interface with seamless information sharing and integration capability and knowledge repository.

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Study on the Job Execution Time of Mobile Cloud Computing (모바일 클라우드 컴퓨팅의 작업 실행 시간에 대한 연구)

  • Jung, Sung Min;Kim, Tae Kyung
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.8 no.1
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    • pp.99-105
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    • 2012
  • Given the numbers of smartphones, tablets and other mobile devices shipped every day, more and more users are relying on the cloud as the main driver for satisfying their computing needs, whether it is data storage, applications or infrastructure. Mobile cloud computing is simply cloud computing in which at least some of the devices involved are mobile. Each node is owned by a different user and is likely to be mobile. Using mobile hardware for cloud computing has advantages over using traditional hardware. These advantage include computational access to multimedia and sensor data without the need for large network transfer, more efficient access to data stored on other mobile devices and distributed ownership and maintenance of hardware. It is important to predict job execution time in mobile cloud computing because there are many mobile nodes with different capabilities. This paper analyzes the job execution time for mobile cloud computing in terms of network environment and heterogeneous mobile nodes using a mathematical model.

Virtual Network for IPTV Service

  • Song, Biao;Hassan, Mohammad Mehedi;Huh, Eui-Nam
    • Proceedings of the Korean Information Science Society Conference
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    • 2011.06d
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    • pp.315-318
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    • 2011
  • In this work, a VN-based IPTV service delivery network containing a novel VNT was designed, This VN-based IPTV service delivery network utilizes current resources that can be easily obtained by IPTV providers and organizes these resources in a more efficient manner, We also developed a three-stage VN allocation scheme to reduce the complexity of topology design and allocation.

A Computing Method of a Process Coefficient in Prediction Model of Plate Temperature using Neural Network (신경망을 이용한 판온예측모델내 공정상수 설정 방법)

  • Kim, Tae-Eun;Lee, Haiyoung
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.28 no.11
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    • pp.51-57
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    • 2014
  • This paper presents an algorithmic type computing technique of process coefficient in predicting model of temperature for reheating furnace and also suggests a design method of neural network model to find an adequate value of process coefficient for arbitrary operating conditions including test conditons. The proposed neural network use furnace temperature, line speed and slab information as input variables, and process coefficient is output variable. Reasonable process coefficients can be obtained by an algorithmic procedure proposed in this paper using process data gathered at test conditons. Also, neural network model output equal process coefficient under same input conditions. This means that adquate process coefficients can be found by only computing neural network model without additive test even if operating conditions vary.