• Journal of KIISE:Computer Systems and Theory
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• v.31 no.1_2
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• pp.95-101
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• 2004

The scheduling problem that maps independent tasks to heterogeneous resources in distributed computing systems is known as NP-complete［1］. GRID［2］ is an example of distributed systems that consisted of heterogeneous resources. Many algorithms to solve this problem have been presented［1,3,4,5］. The scheduling algorithm can be classified into static scheduling algorithms and dynmic scheduling algorithms. A dynamic scheduling algorithm can be used when we can not predict the priority of tasks. Moreover, a dynamic scheduling algorithm can be divided into on-line mode algorithm and batch mode algorithm according to the scheduling time［1,6］. In this paper, we propose a new on-line mode scheduling algorithm. By extensive simulation, we can see that our scheduling algorithm outperforms previous scheduling algorithms.

• The Transactions of the Korea Information Processing Society
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• v.7 no.12
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• pp.3896-3909
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• 2000

The MPEG Video Service of hilving il property of continuity and large cilPilcity needs lilrge network capilcity. Because most of users have a heterogeneous network environment, it's not efficient way that all users have same size of video data to provide them with real time video service. Therefore, for the pUl1Xlse of an efficient and appropriate utilization of network resources, it requires to develop and deploy a new scalable transmission technique in consideration of respective network environment and individual clients computing power. The purpose of this paper is to develop a technology that can adjust the amount of dilta transmitted as an M1'EG video stream according to its gi yen communication bandwidth, and a technique that can reflect dynamic bilndwidth while playing a video stream. For this purpose, we propose a TFS (Temporal-Fidelity Scaling) technique that splits the MPEG video stream into various substream according to picture type or resolution. Those methods proposed her can filcilitilte an effective use of network resources, and provide multimedia MPEG video services in real- time with respect to individual client computing environment

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.3
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• pp.60-71
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• 2017

Smart devices such as smart phones, smart TVs, and smart pads have become essential devices in recent years. As the popularity and demand grows, the performance of smart devices is also getting better and users are dealing with a lot of things such as education and business using smart devices instead of desktop. However, smart devices that still have poor performance compared to desktop, even with improved performance, have difficulty running high performance applications due to limited resources. In this paper, we propose a load balancing algorithm applying the characteristics of smart devices to overcome the resource limitations of devices. in order to verify the algorithm, we implemented the algorithm after adding the distributed processing system service in Android platform. After constructing the cluster on the smart device, various experiments were conducted. Through the analysis of the test results, it is confirmed that the proposed algorithm efficiently improves the overall distributed processing performance by effectively aggregating different amounts of computing resources in heterogeneous smart devices.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.107-110
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• 2002

A grid computing environment is one in which applications can be built over multiple resource nodes at widespread geographic locations. Grid environments seek to integrate and enable access to widely distributed compute resources. The compute resources in a grid environment are typically heterogeneous, with varying qualify and availability Hence, how computations are allocated to individual resources is extremely important in the design of a grid. This paper j\ulcorners concerned with resource management for metacomputing. We describe a resource management architecture using the two-level resource broker.

• 한국전산유체공학회:학술대회논문집
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• 2002.10a
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• pp.78-93
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• 2002

통신 기술의 비약적 발전에 힘입어 기존의 Parallel Computing만으로는 해석할 수 없었던 거대 규모의 문제를 원격지에 있는 고성능의 자원간 연동을 통해 해석하고자 하는 연구가 활발히 진행중이며, 이와 같은 개념을 Grid Computing이라 한다. 본 연구에서는 Grid 환경을 이용한 CFD 해석 방안에 초점을 맞추고 있으며, 이를 위하여 분산된 컴퓨팅 자원을 다양하게 조합하여 Grid 환경에서의 Load Balancing을 위한 병렬 효율에 대한 연구를 수행하였다. 그리고, 다양한 성능 테스트의 결과를 기반으로 Grid 환경에서 두 개의 보조 부스터를 가지는 병렬형 다단 로켓에 대한 유동 해석을 수행하였다.

• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.987-992
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• 2016

This study introduces an example environment where wireless devices are mobile, devices use dynamic voltage scaling, devices and tasks are heterogeneous, tasks have deadline, and the computation and communication power is dynamically changed for energy saving. For this type of environment, the efficient system-level energy management and resource management for task completion can be an essential part of the operation and design of such systems. Therefore, the resources are assigned to tasks and the tasks may be scheduled to maximize a goal which is to minimize energy usage while trying to complete as many tasks as possible by their deadlines. This paper also introduces mobility of nodes and variable transmission power for communication which complicates the resource management/task scheduling problem further.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.670-686
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• 2017

MapReduce (MRV1), a popular programming model, proposed by Google, has been well used to process large datasets in Hadoop, an open source cloud platform. Its new version MapReduce 2.0 (MRV2) developed along with the emerging of Yarn has achieved obvious improvement over MRV1. However, MRV2 suffers from long finishing time on certain types of jobs. Speculative Execution (SE) has been presented as an approach to the problem above by backing up those delayed jobs from low-performance machines to higher ones. In this paper, an adaptive SE strategy (ASE) is presented in Hadoop-2.6.0. Experiment results have depicted that the ASE duplicates tasks according to real-time resources usage among work nodes in a cloud. In addition, the performance of MRV2 is largely improved using the ASE strategy on job execution time and resource consumption, whether in a multi-job environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1913-1934
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• 2022

The emergence of technologies like Big data analytics, Industrial Internet of Things, Internet of Things, and applicability of these technologies in various domains leads to increased demand in the underlying execution environment. The demand may be for compute, storage, and network resources. These demands cannot be effectively catered by the conventional cloud environment, which requires an integrated environment. The task of finding an appropriate service provider is tedious for a service consumer as the number of service providers drastically increases and the services provided are heterogeneous in the specification. A service broker is essential to find the service provider for varying service consumer requests. Also, the service broker should be smart enough to make the service providers best fit for consumer requests, ensuring that both service consumer and provider are mutually beneficial. A service broker in an integrated environment named Global Manager is proposed in the paper, which can find an appropriate service provider for every varying service consumer request. The proposed Global Manager is capable of identification of parameters for service negotiation with the service providers thereby making the providers the best fit to the maximum possible extent for every consumer request. The paper describes the architecture of the proposed Global Manager, workflow through the proposed algorithms followed by the pilot implementation with sample datasets retrieved from literature and synthetic data. The experimental results are presented with a few of the future work to be carried out to make the Manager more sustainable and serviceable.

• The KIPS Transactions:PartA
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• v.14A no.2
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• pp.99-106
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• 2007

Grid computing, a mechanism which uses heterogeneous systems that are geographically distributed, draws attention as a new paradigm for the next generation operation of parallel and distributed computing. The importance of grid computing concerning communication cost is very huge because grid computing furnishes uses with integrated virtual computing service, in which a number of computer systems are connected by a high-speed network. Therefore, to reduce the execution time, the scheduling algorithm in grid environment should take communication cost into consideration as well as computing ability of resources. However, most scheduling algorithms have not only ignored the communication cost by assuming that all tasks were dealt in one cluster, but also did not consider the overhead of communication cost when the tasks were processed in a number of clusters. In this paper, the functions of original scheduling algorithms are analyzed. More importantly, the functions of algorithms are compared and analyzed with consideration of communication cost within the co allocation environment, in which a task is performed separately in many clusters.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.11_12
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• pp.639-645
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• 2005

GRID environments have been developed in distributed heterogeneous computing infrastructure for advanced science and engineering Therefore efficient scheduling algorithms for allocating user job to resources in the GRID environment are required. Many scheduling algorithms have been proposed, but these algorithms are not suitable for the GRID environment. That is the previous scheduling algorithm does not consider network bandwidth between multiple resources. In this paper, we propose a new scheduling algorithm for Global GRID environment and show that our algorithm has better performance than other scheduling algorithms through extensive simulation.