• Journal of KIISE:Computing Practices and Letters
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• v.6 no.4
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• pp.421-429
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• 2000

In present, ATM integrated switching system has been developed to a mixed modules that complexed switching system including maintenance, operation based on B-ISDN/LAN service and plug-in module, , which runs on workstation computer system. Meanwhile, workstation has HMI operation system feature including file system management, time management, graphic processing, TMN agent function. The workstation has communicated with between ATM switching module and clients. This computer system architecture has much burden messages communication among processes or processor. These messages communication consume system resources which are socket, message queue, IO device files, regular files, and so on. Therefore, in this paper we proposed new performance modeling with this system architecture. We will analyze the system bottleneck and improve system performance. In addition, in the future, the system has many additional features should be migrated to workstation system, we need previously to evaluate system bottleneck and redesign it. In performance model, we use queueing network model and the simulation package is used PDQ and C-program.

• Journal of Advanced Navigation Technology
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• v.13 no.4
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• pp.509-514
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• 2009

In this paper, we designed and analyzed 256GB DRAM-based SSD storage using DDR1 memory and PCI-e interface. SSD is a storage system that uses DRAM or NAND Flash as primary storage media. Since the SSD read and write data directly to memory chips, which results in storage speeds far greater than conventional magnetic storage devices, HDD. Architecture of the proposed SSD system has performance of high speed data processing duo to use multiple RAM disks as primary storage and PCI-e interface bus as communication path of RAM disks. We constructed experimental system with UNIX, Windows/Linux server, SAN Switch, and Ethernet Switch and measured IOPS and bandwidth of proposed SSD using IOmeter. In experimental results, it has been shown that IOPS, 470,000 and bandwidth,800MB/sec of the DDR-1 SSD is better than those of the HDD and Flash-based SSD.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.2
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• pp.151-158
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• 2016

The HSA resolves an old problem with existing CPU and GPU architectures by allowing both units to directly access each other's memory pools via unified virtual memory. In a physically realized system, however, frequent data exchanges between CPU and GPU for a virtual memory block result bottlenecks and coherence request overheads. In this paper, we propose Fusion Processor Architecture for efficient access of main memory from both CPU and GPU. It consists of Job Manager, Re-mapper, and Pre-fetcher to control, organize, and distribute work loads and working areas for GPU cores. These components help on reducing memory exchanges between the two processors and improving overall efficiency by eliminating faulty page table requests. To verify proposed algorithm architectures, we develop an emulator based on QEMU, and compare several architectures such as CUDA(Compute Unified Device Architecture), OpenMP, OpenCL. As a result, Proposed fusion processor architectures show 198% faster than others by removing unnecessary memory copies and cache-miss overheads.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.45-52
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• 2014

The Ubiquitous-City (U-City) is a smart or intelligent city to satisfy human beings' desire to enjoy IT services with any device, anytime, anywhere. It is a future city model based on Internet of everything or things (IoE or IoT). It includes a lot of video cameras which are networked together. The networked video cameras support a lot of U-City services as one of the main input data together with sensors. They generate huge amount of video information, real big data for the U-City all the time. It is usually required that the U-City manipulates the big data in real-time. And it is not easy at all. Also, many times, it is required that the accumulated video data are analyzed to detect an event or find a figure among them. It requires a lot of computational power and usually takes a lot of time. Currently we can find researches which try to reduce the processing time of the big video data. Cloud computing can be a good solution to address this matter. There are many cloud computing methodologies which can be used to address the matter. MapReduce is an interesting and attractive methodology for it. It has many advantages and is getting popularity in many areas. Video cameras evolve day by day so that the resolution improves sharply. It leads to the exponential growth of the produced data by the networked video cameras. We are coping with real big data when we have to deal with video image data which are produced by the good quality video cameras. A video surveillance system was not useful until we find the cloud computing. But it is now being widely spread in U-Cities since we find some useful methodologies. Video data are unstructured data thus it is not easy to find a good research result of analyzing the data with MapReduce. This paper presents an analyzing system for the video surveillance system, which is a cloud-computing based video data management system. It is easy to deploy, flexible and reliable. It consists of the video manager, the video monitors, the storage for the video images, the storage client and streaming IN component. The "video monitor" for the video images consists of "video translater" and "protocol manager". The "storage" contains MapReduce analyzer. All components were designed according to the functional requirement of video surveillance system. The "streaming IN" component receives the video data from the networked video cameras and delivers them to the "storage client". It also manages the bottleneck of the network to smooth the data stream. The "storage client" receives the video data from the "streaming IN" component and stores them to the storage. It also helps other components to access the storage. The "video monitor" component transfers the video data by smoothly streaming and manages the protocol. The "video translator" sub-component enables users to manage the resolution, the codec and the frame rate of the video image. The "protocol" sub-component manages the Real Time Streaming Protocol (RTSP) and Real Time Messaging Protocol (RTMP). We use Hadoop Distributed File System(HDFS) for the storage of cloud computing. Hadoop stores the data in HDFS and provides the platform that can process data with simple MapReduce programming model. We suggest our own methodology to analyze the video images using MapReduce in this paper. That is, the workflow of video analysis is presented and detailed explanation is given in this paper. The performance evaluation was experiment and we found that our proposed system worked well. The performance evaluation results are presented in this paper with analysis. With our cluster system, we used compressed $1920{\times}1080(FHD)$ resolution video data, H.264 codec and HDFS as video storage. We measured the processing time according to the number of frame per mapper. Tracing the optimal splitting size of input data and the processing time according to the number of node, we found the linearity of the system performance.