• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.135-145
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• 2010

Efficient Directed Acyclic Graph(DAG) scheduling is critical for achieving high performance in Distributed Heterogeneous computing System(DHCS). In this paper, we present a new high-performance scheduling algorithm, called the LCFT(Levelized Critical First Task) algorithm, for DHCS. The LCFT algorithm is a list-based scheduling that uses a new attribute to efficiently select tasks for scheduling in DHCS. The complexity of LCFT is $O(\upsilon+e)(p+log\;\upsilon)$. The performance of the algorithm has been observed by its application to some practical DAGs, and by comparing it with other existing scheduling algorithms such as PETS, HPS, HCPT and GCA in terms of the schedule length and SpeedUp. The comparison studies show that LCFT significantly outperforms PETS, HPS, HCPT and GCA in schedule length, SpeedUp.

• The Korean Journal of Applied Statistics
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• v.29 no.6
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• pp.1107-1116
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• 2016

Recent astronomical survey observations have produced substantial amounts of data as well as completely changed conventional methods of analyzing astronomical data. Both classical statistical inference and modern machine learning methods have been used in every step of data analysis that range from data calibration to inferences of physical models. We are seeing the growing popularity of using machine learning methods in classical problems of astronomical data analysis due to low-cost data acquisition using cheap large-scale detectors and fast computer networks that enable us to share large volumes of data. It is common to consider the effects of inhomogeneous spatial and temporal coverage in the analysis of big astronomical data. The growing size of the data requires us to use parallel distributed computing environments as well as machine learning algorithms. Distributed data analysis systems have not been adopted widely for the general analysis of massive astronomical data. Gathering adequate training data is expensive in observation and learning data are generally collected from multiple data sources in astronomy; therefore, semi-supervised and ensemble machine learning methods will become important for the analysis of big astronomical data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.12
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• pp.4305-4323
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• 2014

In this paper, we investigate the degrees of freedom (DOF) of the Y channel consisting of three single-antenna users and a two-antenna common access relay, where each user intends to exchange independent messages with the other two users with the assistance of the relay. We show that the DOF of this particular scenario is 1.5. In order to prove this result, we firstly derive a DOF upper bound based on cut-set bound by allowing cooperation among users, which shows that the total DOF is upper bounded by 1.5. Then we propose a novel transmission scheme based on asymmetric signal space alignment (ASSA) to demonstrate the achievability of the upper bound. Theoretical evaluation and numerical results confirm that the upper bound can be achieved by utilizing ASSA, which also proves the optimality of the ASSA-based scheme in terms of DOF. Combining the upper bound and achievability, we conclude that the exact DOF is 1.5. Moreover, we present a novel iterative joint beamforming optimization (I-JBO) algorithm to further improve the sum rate. Numerical simulations have been provided to demonstrate the convergence speed and performance advantage of the I-JBO algorithm.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.131.1-131.1
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• 2011

We present applications of clustering methods to detect variability in massive astronomical time series data. Focusing on variability of bright stars, we use clustering methods to separate possible variable sources from other time series data, which include intrinsically non-variable sources and data with common systematic patterns. We already finished the analysis of the Northern Sky Variability Survey data, which include about 16 million light curves, and present candidate variable sources with their association to other data at different wavelengths. We also apply our clustering method to the light curves of bright objects in the SuperWASP Data Release 1. For the analysis of the SuperWASP data, we exploit a elastically configurable Cloud computing environments that the KISTI Supercomputing Center is deploying. Two quite different configurations are incorporated in our Cloud computing test bed. One system uses the Hadoop distributed processing with its distributed file system, using distributed processing with data locality condition. Another one adopts the Condor and the Lustre network file system. We present test results, considering performance of processing a large number of light curves, and finding clusters of variable and non-variable objects.

• Annual Conference of KIPS
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• 2002.11a
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• pp.115-118
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• 2002

소켓 프로그래밍(socket programming) 인터페이스를 지원하는 C/C++, perl, python 과 같은 언어들은 폴링(polling) 기능을 갖는 select() 함수를 제공한다. 이 select()함수를 이용할 경우, 단일 쓰레드(또는 프로세스)로 다중의 클라이언트 요청을 처리할 수 있다. 최근 네트워크 프로그래밍 분야에서 주목받는 자바 언어의 경우, 최신 JDK 1.4 의 비동기 입출력 패키지에서 select()함수를 제공하고 있으나, JDK 1.3을 포함한 그 이하의 버전에서는 아직까지 이 함수를 제공하지 않고 있다. 일반적으로 다중 쓰레드를 이용하여 소켓서버 응용프로그램을 개발할 경우, 코드가 단순해지고 응답이 빠른 장점이 있는 반면에 네트워크 연결이 증가할수록 다수의 쓰레드를 관리하는 일이 CPU에 큰 부담이 된다. 반면에 소켓폴링(socket polling)을 사용할 경우, 이러한 연결 유지에 대한 부담이 줄어드는 대신, 다중 쓰레드를 이용하는 방법에 비하여 구현이 어렵다. 본 논문에서는 다양한 시뮬레이션 환경에서 세가지 소켓 프로그래밍 모델에 대하여 그 성능을 비교평가 하였다. 이 세가지 모델은 단순 다중 쓰레드 모델(typical multi-thread model), 단일 쓰레드 소켓폴링 모델(socket polling with single-thread model), 다중 쓰레드 소켓폴링 모델(socket polling with multi-threadmodel)이다. 본 논문에서는 다중 쓰레드 소켓폴링 모델을 제안하고 JDK 1.3.1을 이용하여 구현하였다. 이 모델의 경우 복잡한 구조에도 불구하고 단순 다중 쓰레드 모델와 유사하거나 더 나은 성능을 보여주었다. 또한 동일한 용량의 쓰레드 풀(thread pool)을 사용하더라도 단순 다중 쓰레드 모델보다 더 많은 수의 클라이언트를 수용할 수 있는 장점이 있다. 이러한 결과를 바탕으로 본 연구팀에서 수행중인 MoIM-Messge서버의 네트워크 모듈로 다중 쓰레드 소켓폴링 모델을 적용하였다.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.12
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• pp.673-681
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• 2004

As the use of digital videos is getting popular, there is an increasing demand for efficient browsing and retrieval of video. To support such operations, effective video indexing should be incorporated. One of the most fundamental steps in video indexing is to parse video stream into shots and scenes. Generally, it takes long time to parse a video due to the huge amount of computation in a traditional single computing environment. Previous studies had widely used Round Robin scheduling which basically allocates tasks to each slave for a time interval of one quantum. This scheduling is difficult to adapt in a heterogeneous environment. In this paper, we propose two different parallel parsing algorithms which are Size-Adaptive Round Robin and Dynamic Size-Adaptive Round Robin for the heterogeneous distributed computing environments. In order to show their performance, we perform several experiments and show some of the results.

• 한국전산유체공학회:학술대회논문집
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• 2006.05a
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• pp.116-120
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• 2006

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.649-650
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• 2006

In this paper, finite element analysis (FEA)-based optimization using Internet distributed computing is proposed for the real world and complex optimization such as optimal design of permanent magnet do motor (PMDCM).

• Proceedings of the Korean Information Science Society Conference
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• 2003.04a
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• pp.4-6
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• 2003

시스템의 구조는 확장 또는 부하 균형을 위해 재구성 및 재구조화가 필요하다. 이러한 상황에서, 다수의 새로운 시스템이 기존의 시스템에 추가되며 이전의 시스템이 담당하고 있던 역할의 일부분이 새로 추가된 시스템으로 분산된다. 이러한 자원 관리는 몇 가지 문제를 야기시키는데, 가장 큰 문제점은 일부 시스템 또는 전체 시스템의 재구조화를 위해 일정기간 동안 서비스 제공이 중단된다는 점이다. 이 논문에서는 동적인 자원관리가 요구되는 변화하는 환경에서 자동적으로 새로운 시스템을 추가하고 적응적으로 시스템의 부하를 재조정할 수 있는 이동 컴퓨팅 모델을 제안한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.637-640
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• 2022

최근 빅데이터 및 인공지능의 중요성이 커짐에 따라 클라우드 시스템을 효율적으로 설계하고 관리하기 위한 연구가 활발히 진행 중이다. 본 논문은 기술 발전으로 각 개인은 고성능의 컴퓨팅 자원을 소유하고 있지만, 이 자원이 대부분 잉여 자원으로써 낭비되고 있다는 점을 착안하여, 잉여 컴퓨팅 자원을 효율적으로 활용하기 위해 엣지 클라우드 환경에서 분산된 자원의 가용성을 확보하기 위한 방법을 제안한다.