• The Transactions of the Korea Information Processing Society
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• v.7 no.4
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• pp.1227-1235
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• 2000

In this paper, we propose new concepts that the distributed counter value with the distributed EC system identify each user who users the distributed system. The web user should register his/her own user ID in the cyber shopping mall system. Instead of registration, this paper proposes the proprietary mechanism that is distributing counter. The counter assigns the distinguished number to each client. The distributed lock algorithm is used for mutual assignment of the counter to each client. The proposed algorithm is the best solution in the distributed environment system such as cyber shopping mall. If a user should register his/her own ID in every EC system, he/she may not try to use these uncomfortable systems. The mutual counter is used to identify each client. All of these features are designed and implemented on Windows NT web server. Also these features were experiments with 5 clients for 300 times. According to the experiments, clients have their own mutual counter value. The proposed algorithm will be more efficient in internet application environment. Moreover, it will improve the number of internet users.

• Structural Engineering and Mechanics
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• v.37 no.1
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• pp.17-37
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• 2011

This paper presents a new Internet-based computational framework for the realistic simulation of multi-scale response of structural systems. Two levels of parallel processing are involved in this frame work: multiple local distributed computing environments connected by the Internet to form a cluster-to-cluster distributed computing environment. To utilize such a computing environment for a realistic simulation, the simulation task of a structural system has been separated into a simulation of a simplified global model in association with several detailed component models using various scales. These related multi-scale simulation tasks are distributed amongst clusters and connected to form a multi-level hierarchy. The Internet is used to coordinate geographically distributed simulation tasks. This paper also presents the development of a software framework that can support the multi-level hierarchical simulation approach, in a cluster-to-cluster distributed computing environment. The architectural design of the program also allows the integration of several multi-scale models to be clients and servers under a single platform. Such integration can combine geographically distributed computing resources to produce realistic simulations of structural systems.

• ETRI Journal
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• v.39 no.2
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• pp.202-212
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• 2017

With the advent of the Internet-of-Things paradigm, the amount of data production has grown exponentially and the user demand for responsive consumption of data has increased significantly. Herein, we present DART, a fast and lightweight stream processing framework for the IoT environment. Because the DART framework targets a geospatially distributed environment of heterogeneous devices, the framework provides (1) an end-user tool for device registration and application authoring, (2) automatic worker node monitoring and task allocations, and (3) runtime management of user applications with fault tolerance. To maximize performance, the DART framework adopts an actor model in which applications are segmented into microtasks and assigned to an actor following a single responsibility. To prove the feasibility of the proposed framework, we implemented the DART system. We also conducted experiments to show that the system can significantly reduce computing burdens and alleviate network load by utilizing the idle resources of intermediate edge devices.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1284-1286
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• 2003

There is a growing demand on remotely sensed and GIS data services in modern society. However, conventional WEB applications based on client/server pattern can not meet the criteria in the future . Grid computing provides a promising resolution for establishing spatial information system toward future applications. Here, a new architecture of the distributed environment for remotely sensed data processing based on the middleware technology was proposed. In addition, in order to utilize the new environment, a problem had to be algorithmically expressed as comprising a set of concurrently executing sub-problems or tasks. Experiment of the algorithm was implemented, and the results show that the new environmental can achieve high speedups for applications compared with conventional implementation.

• The Transactions of the Korea Information Processing Society
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• v.6 no.1
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• pp.57-65
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• 1999

The simulation which is larger scale, complex and interactive with clients treat a lot of messages. It can be thinking more efficient distributed simulation than sequential one. The training simulation with multi-users is geographically distributed, and required high cost to operate and maintain system as increasing user requirements. The adaptation of web technology to the simulation can be a way to solves it without cost added. But dynamic web environment can causes causality error of events. This paper is concerned with client-server model, which supports interaction between distributed simulation server and web browser, and it is implemented by Java distributed object model. the result have shown that the distributed simulation is performed correctly on dynamic environment.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.1
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• pp.70-75
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• 2018

Smart Factory is an intelligent factory that can enhance productivity, quality, customer satisfaction, etc. by applying information and communications technology to the entire production process including design & development, manufacture, and distribution & logistics. The precise amount of data generated in a smart factory varies depending on the factory's size and state of facilities. Regardless, it would be difficult to apply traditional production management systems to a smart factory environment, as it generates vast amounts of data. For this reason, the need for a distributed big-data processing system has risen, which can process a large amount of data. Therefore, this article has designed a Gluster File System (GlusterFS)-based distributed big-data processing system that can be used in a smart factory environment. Compared to existing distributed processing systems, the proposed distributed big-data processing system reduces the system load and the risk of data loss through the distribution and management of network traffic.

• Journal of Digital Contents Society
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• v.15 no.4
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• pp.533-545
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• 2014

According to the evolving of IT technologies, the amount of data we are facing increasing exponentially. Thus, the technique for managing and analyzing these vast data that has emerged is a distributed processing system of big data. A quality evaluation for the existing distributed processing systems has been proceeded by the structured data environment. Thus, if we apply this to the evaluation of distributed processing systems of big data which has to focus on the analysis of the unstructured data, a precise quality assessment cannot be made. Therefore, a study of the quality evaluation model for the distributed processing systems is needed, which considers the environment of the analysis of big data. In this paper, we propose a new quality evaluation model by deriving the quality evaluation elements based on the ISO/IEC9126 which is the international standard on software quality, and defining metrics for validating the elements.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.3
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• pp.251-260
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• 2002

The parallel/distributed processing with a lot of the idle hosts on the web has the high coot-performance ratio for large-scale applications. It's processing has to show the solutions for unpredictable status such as heterogeneity of hosts, variability of hosts, autonomy of hosts, the supporting performance continuously, and the number of hosts which are participated in computation and so on. In this paper, we propose the strategy of adaptive tack reallocation based on performance the host job processing, spread out geographically Also, It shows the scheme of dynamic host management with dynamic environment, which is changed by lots of hosts on the web during parallel processing for large-scale applications. This paper implements the PDSWeb (Parallel/Distributed Scheme on Web) system, evaluates and applies It to the generation of rendering image with highly intensive computation. The results are showed that the adaptive task reallocation with the variation of hosts has been increased up to maximum 90% and the improvement in performance according to add/delete of hosts.

• The KIPS Transactions:PartA
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• v.11A no.3
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• pp.181-188
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• 2004

PDF(Parallel/Distributed Processing) is an internet-based parallel/distributed processing system that utilizes resources from hosts on the internet in idle state to perform large scale application through parallel processing, thus decreasing the total execution time. In this paper. do propose an adaptive method to be changed network environment at any time using realtime monitoring of host. It is found from experiments that parallel/distributed processing has better performance than its without monitoring as an adaptive strategy, which copy with task delay factor by overload and fault of network, be applicable to the cockpits of task allocation algorithm in PDP.

• KIISE Transactions on Computing Practices
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• v.22 no.2
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• pp.89-94
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• 2016

Recently, cluster computing environments have been in a process of change toward virtualized cluster environments. The change of the cluster environment has great impact on the performance of large volume distributed processing. Therefore, many domestic and international IT companies have invested heavily in research on cluster environments. In this paper, we show how the hypervisor affects the performance of distributed processing of a large volume of data. We present a performance comparison of MapReduce processing in two virtualized cluster environments, one built using the Xen hypervisor and the other built using the container-based Docker. Our results show that Docker is faster than Xen.