• Annual Conference of KIPS
• /
• 2017.11a
• /
• pp.679-681
• /
• 2017

Apache Storm은 대용량 데이터 스트림을 처리하기 위한 실시간 분산 병렬 처리 프레임워크이며, 이를 사용해 다수의 프로세스 및 스레드를 동시에 동작시킬 수 있다. 하지만, 이러한 멀티 프로세스 및 스레드 환경을 제공하는 Storm은 많은 네트워크 시스템 호출을 수행하고, 이는 잦은 문맥 전환(context switch), 운영체제로의 버퍼 복사, 운영체제 내의 버퍼 복사 등으로 인해 CPU 과부하 문제를 발생시킬 수 있다. 이러한 문제는 고성능 네트워크 장비인 InfiniBand의 IPoIB(IP over InfiniBand) 통신을 사용할 때, InfiniBand가 지원하는 대역폭(bandwidth) 대비 저용량 데이터의 송수신으로 인해 더 잦은 문맥 전환과 버퍼 복사가 발생하여 CPU 과부하 문제가 더욱 심각해진다. 따라서, 본 논문에서는 InfiniBand의 RDMA(Remote Direct Memory Access)를 Storm에 적용하는 설계안을 제시함으로써 CPU 과부하 문제를 해결한다.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.15 no.1
• /
• pp.58-70
• /
• 1990

This paper describes a VLSI implementation of real-time two dimensional DCT processor for the subprimary rate video codec system. The proposed architecture exploits the parallelism and concurrency of the distributes architecture for vector inner product operation of DCT and meets the CCITT performance requirements of video codec for full CSIF 30 frames/sec. It is also shown that this architecture satisfies all the CCITT IDCT accuracy specification by simulating the suggested architecture in bit level. The efficient VLSI disign methodology to design suggested architecture is considered and the module generator oriented design environments are constructed based on SUN 3/150C workstation. Using the constructed design environments. the suggensted architecture have been designed by double metal 2micron CMOS technology. The chip area fo designed 8x8 2-D DA-DCT (Distributed Arithmetic DCT) processor is about 3.9mmx4.8mm.

• 전자공학회논문지 IE
• /
• v.44 no.3
• /
• pp.12-22
• /
• 2007

In this paper, we designed of the high speed digital neuron processor in order to digital neural networks. we designed of the MAC(Multiplier and Accumulator) operation unit used residue number system without carry propagation for the high speed operation. and we implemented sigmoid active function which make it difficult to design neuron processor. The Designed circuits are descripted by VHDL and synthesized by Compass tools. we designed of MAC operation unit and sigmoid processing unit are proved that it could run time 19.6 nsec on the simulation and decreased to hardware size about 50%, each order. Designed digital neuron processor can be implementation in parallel distributed processing system with desired real time processing, In this paper.

• The Journal of Information Technology
• /
• v.2 no.2
• /
• pp.145-161
• /
• 1999

This paper proposes a high speed back propagation neural networks which uses the residue number system. making the high speed operation possible without carry propagation Consisting of MAC(Multiplication and Accumulation) operator unit using Residue number system and sigmoid function operator unit using Mixed Residue Conversion is designed, The Designed circuits are descripted by VHDL and synthesized by Compass tools. Result of simulations shows that critical path delay time is about 19nsec and the size can be reduced to 40% compared to the neural networks implemented by the real number operation unit. The proposed design circuits can be implemented in parallel distributed processing system with desired real time processing.

• The Journal of the Korea Contents Association
• /
• v.9 no.5
• /
• pp.13-23
• /
• 2009

One of the main purpose of multiple-view image processing technology is support realistic 3D image to device user by using multiple viewpoint display devices and compressed data restoration devices. This paper proposes a multiple view image synthesis scheme based on RAM disk which makes possible to browse 3D images generated by applying effective composing method to real time input stereo images. The proposed scheme first converts input images to binary image. We applies edge detection algorithm such as Sobel algorithm and Prewiit algorithm to find edges used to evaluate disparities from images of 4 multi-cameras. In addition, we make use of time interval between hardware trigger and software trigger to solve the synchronization problem which has stated ambiguously in related studies. We use a unique identifier on each snapshot of images for distributed environment. With respect of performance results, the proposed scheme takes 0.67 sec in each binary array. to transfer entire images which contains left and right side with disparity information for high quality 3D image browsing. We conclude that the proposed scheme is suitable for real time 3D applications.

• Journal of Internet Computing and Services
• /
• v.14 no.6
• /
• pp.71-84
• /
• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.

• Journal of KIISE
• /
• v.42 no.8
• /
• pp.998-1009
• /
• 2015

In recent years, there has been a growing interest in RDFS Inference to build a rich knowledge base. However, it is difficult to improve the inference performance with large data by using a single machine. Therefore, researchers are investigating the development of a RDFS inference engine for a distributed computing environment. However, the existing inference engines cannot process data in real-time, are difficult to implement, and are vulnerable to repetitive tasks. In order to overcome these problems, we propose a method to construct an in-memory distributed inference engine that uses a parallel graph structure. In general, the ontology based on a triple structure possesses a graph structure. Thus, it is intuitive to design a graph structure-based inference engine. Moreover, the RDFS inference rule can be implemented by utilizing the operator of the graph structure, and we can thus design the inference engine according to the graph structure, and not the structure of the data table. In this study, we evaluate the proposed inference engine by using the LUBM1000 and LUBM3000 data to test the speed of the inference. The results of our experiment indicate that the proposed in-memory distributed inference engine achieved a performance of about 10 times faster than an in-storage inference engine.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.22 no.9
• /
• pp.1867-1879
• /
• 1997

DREAM-S is a distributed main-memory database system for the real-time processing of shared operational datra in ATM switching systems. DREAM-S has a client-server architecture in which only the server has the diskstorage, and provides the T-Tree index structure for efficient accesses to the data. We propose a recovery technique for the T-Tree index structre in DREAM-S. Although main-memory database system offer efficient access performance, the database int he main-memory may be broken when system failure such as database transaction failure or power failure occurs. Therfore, a recovery technique that recovers the database (including index structures) is essential for fault tolerant ATM switching systems. Proposed recovery technique relieves the bottleneck of the server processors disk operations by maintaining the T-Tree index structure only in the main-memory. In addition, fast recovery is guaranteed even in large number of client systems since the T-Tree index structure(s) in each system can be recovered cncurrently.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.217-217
• /
• 2023

상수도관망은 사용자에게 고품질의 물을 안정적으로 공급하는 것을 목적으로 하며, 이를 평가하기 위한 지표 중 하나로 압력을 활용한다. 최근 스마트 센서의 설치가 확장됨에 따라 기계학습기법을 이용한 실시간 데이터 기반의 분석이 활발하다. 따라서 어디에서 데이터를 수집하느냐에 대한 센서 위치 결정이 중요하다. 본 연구는 eXtreme Gradient Boosting(XGBoost) 모델을 활용하여 대규모 상수도관망 내 센서 위치를 최적화하는 방법론을 제안한다. XGBoost 모델은 여러 의사결정 나무(decision tree)를 활용하는 앙상블(ensemble) 모델이며, 오차에 따른 가중치를 부여하여 성능을 향상시키는 부스팅(boosting) 방식을 이용한다. 이는 분산 및 병렬 처리가 가능해 메모리리소스를 최적으로 사용하고, 학습 속도가 빠르며 결측치에 대한 전처리 과정을 모델 내에 포함하고 있다는 장점이 있다. 모델 구현을 위한 독립 변수 결정을 위해 압력 데이터의 변동성 및 평균압력 값을 고려하여 상수도관망을 대표하는 중요 절점(critical node)를 선정한다. 중요 절점의 압력 값을 예측하는 XGBoost 모델을 구축하고 모델의 성능과 요인 중요도(feature importance) 값을 고려하여 센서의 최적 위치를 선정한다. 이러한 방법론을 기반으로 상수도관망의 특성에 따른 경향성을 파악하기 위해 다양한 형태(예를 들어, 망형, 가지형)와 구성 절점의 수를 변화시키며 결과를 분석한다. 본 연구에서 구축한 XGBoost 모델은 추가적인 전처리 과정을 최소화하며 대규모 관망에 간편하게 사용할 수 있어 추후 다양한 입출력 데이터의 조합을 통해 센서 위치 외에도 상수도관망에서의 성능 최적화에 활용할 수 있을 것으로 기대한다.

• The KIPS Transactions:PartA
• /
• v.16A no.1
• /
• pp.9-16
• /
• 2009

Because Grid system consists of heterogenous computing resources, which are distributed on a wide scale, it is impossible to efficiently execute applications with scheduling algorithms of a conventional parallel system that, in contrast, aim at homogeneous and controllable resources. To suggest an algorithm that can fully reflect the characteristics of a grid system, our research is focused on examining the type of information used in current scheduling algorithms and consequently, deriving factors that could develop algorithms further. The results from the analysis of these algorithms not only show that static information of resources such as capacity or the number of processors can facilitate the scheduling algorithms but also verified a decrease in efficiency in case of utilizing real time load information of resources due to the intrinsic characteristics of a grid system relatively long computing time, and the need for the means to evade unfeasible resources or ones with slow processing time. In this paper, we propose a new algorithm, which is revised to reflect static information in the logic of WQR(Workqueue Replication) algorithms and show that it provides better performance than the one used in the existing method through simulation.