• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 학술대회지
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• pp.703-706
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• 2004

Grid computing represents the fundamental computing shift from a localized resource computing model to a fully-distributed virtual organization with shared resources. Accounting is one of the main obstacles to widespread adoption of the grid. Accounting has until recently, been a sparsely-addressed problem, particularly in practice. In this paper, we design and implement the accounting information gathering system. Implemented system is based on OGSA, following GSAX framework of RUS-WG in GGF. And the schema of gathered and serviced accounting information is following Usage Record Fields of UR-WG in GGF. Also, the accounting information integrating and monitoring tool for system management in the grid environment are implemented.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2018년도 제30회 한글 및 한국어 정보처리 학술대회
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• pp.636-638
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• 2018

본 논문에서는 영어권에 비해 상대적으로 부족한 한국어 언어자원을 지속적으로 구축함으로써 한국어 문서로 구성된 시간정보 주석 말뭉치를 확보하고 이를 바탕으로 한국어 시간정보추출 시스템에 대한 연구를 수행한다. 말뭉치 구축 과정에서의 시간정보 주석 작업은 가이드라인을 숙지한 주석자들이 수작업으로 기록하고, 어떤 주석 결과에 대해 의견이 다른 경우에는 중재자가 주석자들과 함께 검토하며 합의점을 도출한다. 시간정보추출 시스템은 자연어 문장에 대한 형태소 분석결과를 이용하여 시간표현(TIMEX3), 시간관계와 연관된 사건(EVENT), 시간표현 및 사건들 간의 시간관계(TLINK)를 추출하는 단계로 이루어진다. 추출된 한국어 시간정보는 문서 내 공통된 개체에 대한 공간정보와 결합함으로써 시공간정보가 모두 반영된 SPOTL을 생성한다. 추후 실험을 통하여 제안시스템의 구체적인 시간정보추출 성능을 파악할 것이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권12호
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• pp.5694-5711
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• 2017

The technology of cloud computing is growing very quickly, thus it is required to manage the process of resource allocation. In this paper, load balancing algorithm based on honey bee behavior (LBA_HB) is proposed. Its main goal is distribute workload of multiple network links in the way that avoid underutilization and over utilization of the resources. This can be achieved by allocating the incoming task to a virtual machine (VM) which meets two conditions; number of tasks currently processing by this VM is less than number of tasks currently processing by other VMs and the deviation of this VM processing time from average processing time of all VMs is less than a threshold value. The proposed algorithm is compared with different scheduling algorithms; honey bee, ant colony, modified throttled and round robin algorithms. The results of experiments show the efficiency of the proposed algorithm in terms of execution time, response time, makespan, standard deviation of load, and degree of imbalance.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2012년도 추계학술대회
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• pp.351-354
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• 2012

클라우드 컴퓨팅의 핵심은 대규모 컴퓨팅 리소스를 유기적으로 연결시켜 효율적으로 사용하게 하는 것이다. 본 논문에서는 오픈 소스 기반의 클라우드 컴퓨팅 가상화 기법을 이용한 가상 컴퓨터 실습실을 설계하고 구현하여 교육현장에서 실습 장비의 노후나 소프트웨어의 잦은 업그레이드로 인한 문제점을 개선할 수 있도록 하였다.

• 한국IT서비스학회지
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• 제5권3호
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• pp.109-119
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• 2006

The company cannot optimize its computing environment just with individual solutions such as ERP(enterprise resource planning), SCM(supply chain management), CRM(customer relationship management), and KM(knowledge management). EAI(enterprise application integration) has emerged as an alternative that can optimize computing environment of the company through integrating such solutions and systems of distributed computing and mainframe environment. This paper reports the case study of Deakyo Co. in successfully implementing EAI. It describes project goal, project organization, project plan, the implemented EAI configuration and its features, the EAI effectiveness to the firm, and the critical success factors of the EAI project. This case study is thought to be useful as a practical guideline in carrying out EAI project of the company and to provide significant basis for constructing the theoretical framework of EAI project methodology.

• 전자통신동향분석
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• 제37권1호
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• pp.32-41
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• 2022

With the development of computing technology, the convergence of ICT with existing traditional industries is being attempted. In particular, with the recent advent of 5G, connectivity with numerous AuT (autonomous Things) in the real world as well as simple mobile terminals has increased. As more devices are deployed in the real world, the need for technology for devices to learn and act autonomously to communicate with humans has begun to emerge. This article introduces "Device to the Edge," a new computing paradigm that enables various devices in smart spaces (e.g., factories, metaverse, shipyards, and city centers) to perform ultra-reliable, low-latency and high-speed processing regardless of the limitations of capability and performance. The proposed technology, referred to as EdgeCPS, can link devices to augmented virtual resources of edge servers to support complex artificial intelligence tasks and ultra-proximity services from low-specification/low-resource devices to high-performance devices.

• 전자통신동향분석
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• 제37권5호
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• pp.44-53
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• 2022

The Internet of Things (IoT) is growing exponentially, with the number of IoT devices multiplying annually. Accordingly, the paradigm is changing from cloud computing to edge computing and even tiny edge computing because of the low latency and cost reduction. Machine learning is also shifting its role from the cloud to edge or tiny edge according to the paradigm shift. However, the fragmented and resource-constrained features of IoT devices have limited the development of artificial intelligence applications. Edge MLaaS (Machine Learning as-a-Service) has been studied to easily and quickly adopt machine learning to products and overcome the device limitations. This paper briefly summarizes what Edge MLaaS is and what element of research it requires.

• International journal of advanced smart convergence
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• 제13권2호
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• pp.16-24
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• 2024

With an increase in the relevance of next-generation integrated networking environments, the need to effectively utilize advanced networking techniques also increases. Specifically, integrating Software-Defined Networking (SDN) with Multi-access Edge Computing (MEC) is critical for enhancing network flexibility and addressing challenges such as security vulnerabilities and complex network management. SDN enhances operational flexibility by separating the control and data planes, introducing management complexities. This paper proposes a reinforcement learning-based network path optimization strategy within SDN environments to maximize performance, minimize latency, and optimize resource usage in MEC settings. The proposed Enhanced Proximal Policy Optimization (PPO)-based scheme effectively selects optimal routing paths in dynamic conditions, reducing average delay times to about 60 ms and lowering energy consumption. As the proposed method outperforms conventional schemes, it poses significant practical applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권2호
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• pp.327-347
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• 2024

Edge computing is frequently employed in the Internet of Vehicles, although the computation and communication capabilities of roadside units with edge servers are limited. As a result, to perform distributed machine learning on resource-limited MEC systems, resources have to be allocated sensibly. This paper presents an Improved MADDPG algorithm to overcome the current IoV concerns of high delay and limited offloading utility. Firstly, we employ the MADDPG algorithm for task offloading. Secondly, the edge server aggregates the updated model and modifies the aggregation model parameters to achieve optimal policy learning. Finally, the new approach is contrasted with current reinforcement learning techniques. The simulation results show that compared with MADDPG and MAA2C algorithms, our algorithm improves offloading utility by 2% and 9%, and reduces delay by 29.6%.

• IEIE Transactions on Smart Processing and Computing
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• 제2권3호
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• pp.160-167
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• 2013

Resource allocation based on quantized feedback plays a critical role in wireless mesh networks with a time division multiple access (TDMA) physical layer. In this study, a resource allocation problem was formulated based on quantized feedback for TDMA wireless mesh networks that minimize the total transmission power. Three steps were taken to solve the optimization problem. In the first step, the codebook of the power, rate and equivalent channel quantization threshold was designed. In the second step, the timeslot allocation criterion was deduced using the primal-dual method. In the third step, a resource allocation scheme was developed based on quantized feedback using the stochastic optimization tool. The simulation results show that the proposed scheme not only reduces the total transmission power, but also has the advantage of quantized feedback.