• Proceedings of the Korean Society of Computer Information Conference
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• 2014.07a
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• pp.235-236
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• 2014

플래시 메모리 기술이 발전함에 따라 낸드 플래시 기반의 SSD가 상용화 되면서 I/O시간을 줄이기 위한 연구들이 진행되고 있다. 이에 본 논문에서는 기존의 메인 메모리와 저장장치 사이에 확장 버퍼 캐시로써 SSD를 사용하고 메인 메모리에서 방출 된 페이지들을 구분하여 같은 성향의 페이지들을 블록화 하는 모델을 제안한다. 이러한 모델을 통하여 블록 단위로 사용되는 SSD를 효율적으로 이용하여 읽기 및 쓰기 성능을 높이고 I/O에 해당하는 시간들을 줄임으로써 전체적인 성능 향상을 증명하였다.

• Annual Conference of KIPS
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• 2009.11a
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• pp.95-96
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• 2009

클라우드 컴퓨팅은 대규모의 분산 시스템을 기반으로 페타바이트급 이상의 처리가 가능해진 최근에 각광받고 있는 기술이다. 대부분 비 대칭 분산 구조로 객체기반 클러스터 파일 시스템으로 이루어져있다. 이런 환경에서 효율성과 신뢰성 등을 고려하여 많은 연구가 진행 중이다. 본 논문에서는 특정한 상황에서 병목현상과 메타 데이터 서버간의 부하분산을 처리함으로써, 한 특징인 효율적인 측면에서 향상이 가능한 기법을 제안하였다.

• The KIPS Transactions:PartC
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• v.17C no.2
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• pp.189-196
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• 2010

The LEACH does not use flooding method for data transmission and this makes low power consumption. So performance of the WSN is increased. On the other hand, QoS based algorithm which use restricted flooding method in WSN also achieves low power consuming rate by reducing the number of nodes that are participated in routing path selection. But when the data is delivered to the sink node, the LEACH choose a routing path which has a small hop count. And it leads that the performance of the entire network is worse. In the paper we propose a QoS based energy efficient clustering and routing algorithm in WSN. I classify the type of packet with two classes, based on the energy efficiency that is the most important issue in WSN. We provide the differentiated services according to the different type of packet. Simulation results evaluated by the NS-2 show that proposed algorithm extended the network lifetime 2.47 times at average. And each of the case in the class 1 and class 2 data packet, the throughput is improved 312% and 61% each.

• The KIPS Transactions:PartD
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• v.14D no.7
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• pp.733-742
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• 2007

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Due to this reason, memory usage for data stream analysis should be confined finitely although new data elements are continuously generated in a data stream. To satisfy this requirement, data stream processing sacrifices the correctness of its analysis result by allowing some errors. The old distribution statistics are diminished by a predefined decay rate as time goes by, so that the effect of the obsolete information on the current result of clustering can be eliminated without maintaining any data element physically. This paper proposes a grid based clustering algorithm for a data stream. Given a set of initial grid cells, the dense range of a grid cell is recursively partitioned into a smaller cell based on the distribution statistics of data elements by a top down manner until the smallest cell, called a unit cell, is identified. Since only the distribution statistics of data elements are maintained by dynamically partitioned grid cells, the clusters of a data stream can be effectively found without maintaining the data elements physically. Furthermore, the memory usage of the proposed algorithm is adjusted adaptively to the size of confined memory space by flexibly resizing the size of a unit cell. As a result, the confined memory space can be fully utilized to generate the result of clustering as accurately as possible. The proposed algorithm is analyzed by a series of experiments to identify its various characteristics

• Convergence Security Journal
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• v.9 no.2
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• pp.71-78
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• 2009

It is very difficult to apply previous security protocols to WSNs(Wireless Sensor Networks) directly because WNSs have resource constrained characteristics such as a low computing ability, power, and a low communication band width. In order to overcome the problem, we proposes a secure group communication scheme applicable to WSNs. The proposed scheme is a combined form of the TEEN(Threshold sensitive Energy Efficient sensor Network protocol) clustering based hierarchical routing protocol and security mechanism, and we assume that WSNs are composed of sensor nodes, cluster headers, and base stations. We use both private key and public key cryptographic algorithms to achieve an enhanced security and an efficient key management. In addition, communications among sensor nodes, cluster headers, and base stations are accomplished by a hierarchical tree architecture to reduce power consumption. Therefore, the proposed scheme in this paper is well suited for WSNs since our design can provide not only a more enhanced security but also a lower power consumption in communications.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.10
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• pp.221-230
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• 2020

Supercomputer that shares limited resources to multiple users needs a way to optimize the execution of application. For this, it is useful for system administrators to get prior information and hint about the applications to be executed. In most high-performance computing system operations, system administrators strive to increase system productivity by receiving information about execution duration and resource requirements from users when executing tasks. They are also using profiling techniques that generates the necessary information using statistics such as system usage to increase system utilization. In a previous study, we have proposed a scheduling optimization technique by developing a hardware performance counter-based profiling technique that enables characterization of applications without further understanding of the source code. In this paper, we constructed a profiling testbed cluster to support optimal execution of the supercomputer and experimented with the scalability of the profiling method to analyze application characteristics in the built cluster environment. Also, we experimented that the profiling method can be utilized in actual scheduling optimization with scalability even if the application class is reduced or the number of nodes for profiling is minimized. Even though the number of nodes used for profiling was reduced to 1/4, the execution time of the application increased by 1.08% compared to profiling using all nodes, and the scheduling optimization performance improved by up to 37% compared to sequential execution. In addition, profiling by reducing the size of the problem resulted in a quarter of the cost of collecting profiling data and a performance improvement of up to 35%.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.217-223
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• 2022

Subspace clustering for online data streams requires a large amount of memory resources as all subsets of data dimensions must be examined. In order to track the continuous change of clusters for a data stream in a finite memory space, in this paper, we propose a grid-based subspace clustering algorithm that effectively uses memory resources. Given an n-dimensional data stream, the distribution information of data items in data space is monitored by a grid-cell list. When the frequency of data items in the grid-cell list of the first level is high and it becomes a unit grid-cell, the grid-cell list of the next level is created as a child node in order to find clusters of all possible subspaces from the grid-cell. In this way, a maximum n-level grid-cell subspace tree is constructed, and a k-dimensional subspace cluster can be found at the k^th level of the subspace grid-cell tree. Through experiments, it was confirmed that the proposed method uses computing resources more efficiently by expanding only the dense space while maintaining the same accuracy as the existing method.

• 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.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.11
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• pp.381-394
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• 2022

Recently, due to the personalization and customization of data, Internet-based services have increased requirements for real-time processing, such as real-time AI inference and data analysis, which must be handled immediately according to the user's situation or requirement. Real-time tasks have a set deadline from the start of each task to the return of the results, and the guarantee of the deadline is directly linked to the quality of the services. However, traditional container systems are limited in operating real-time tasks because they do not provide the ability to allocate and manage deadlines for tasks executed in containers. In addition, tasks such as AI inference and data analysis basically utilize graphical processing units (GPU), which typically have performance impacts on each other because performance isolation is not provided between containers. And the resource usage of the node alone cannot determine the deadline guarantee rate of each container or whether to deploy a new real-time container. In this paper, we propose a monitoring technique for tracking and managing the execution status of deadlines and real-time GPU tasks in containers to support real-time processing of GPU tasks running on containers, and a node list management technique for container placement on appropriate nodes to ensure deadlines. Furthermore, we demonstrate from experiments that the proposed technique has a very small impact on the system.

• Convergence Security Journal
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• v.23 no.4
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• pp.61-70
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• 2023

Clustering is an unsupervised learning method that involves grouping data based on features such as distance metrics, using data without known labels or ground truth values. This method has the advantage of being applicable to various types of data, including images, text, and audio, without the need for labeling. Traditional clustering techniques involve applying dimensionality reduction methods or extracting specific features to perform clustering. However, with the advancement of deep learning models, research on deep clustering techniques using techniques such as autoencoders and generative adversarial networks, which represent input data as latent vectors, has emerged. In this study, we propose a deep clustering technique based on deep learning. In this approach, we use an autoencoder to transform the input data into latent vectors, and then construct a vector space according to the cluster structure and perform k-means clustering. We conducted experiments using the MNIST and Fashion-MNIST datasets in the PyTorch machine learning library as the experimental environment. The model used is a convolutional neural network-based autoencoder model. The experimental results show an accuracy of 89.42% for MNIST and 56.64% for Fashion-MNIST when k is set to 10.