• Journal of Information Processing Systems
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• v.20 no.3
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.8
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• pp.22-34
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• 1999

In this paper, an efficient test algorithm for embedded dual-port memories is presented. The proposed test algorithm can be used to test embedded dual-port memories faster than the conventional multi-port test algorithms and can be used to completely detect stuck-at faults, transition faults and coupling faults which are major target faults in embedded memories. Also, in this work, BIST which performs the proposed memory testing algorithm is designed using Verilog-HDL, and simulation and synthesis for BIST are performed using Cadence Verilog-XL and Synopsys Design-Analyzer. It has been shown that the proposed test algorithm has high efficiency through experiments on various size of embedded memories.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.7
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• pp.476-487
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• 2007

Flash memory offers attractive features, such as non-volatile, shock resistance, fast access, and low power consumption for data storage. However, it has one main drawback of requiring an erase before updating the contents. Furthermore, flash memory can only be erased limited number of times. To overcome limitations, flash memory needs a software layer called flash translation layer (FTL). The basic function of FTL is to translate the logical address from the file system like file allocation table (FAT) to the physical address in flash memory. In this paper, a new FTL algorithm called an efficient and advanced space-management technique (EAST) is proposed. EAST improves the performance by optimizing the number of log blocks, by applying the state transition, and by using reallocation blocks. The results of experiments show that EAST outperforms FAST, which is an enhanced log block scheme, particularly when the usage of flash memory is not full.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.5
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• pp.350-354
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• 2009

To optimize the performance of software programs, it is important to know certain hardware parameters such as the CPU speed, the cache size, the number of TLB entries, and the parameters of the memory subsystem. There exist several ways to obtain the values of various hardware parameters. Firstly. the values can be taken from the hardware manual. Secondly, the parameters can be obtained by calling functions provided by the operating systems. Finally, hardware detection programs can find the desired values. Such programs are usually executed on PC or server systems and report the CPU speed, the cache size, the number of TLB entries, and so on. However, they do not sufficiently detect the parameters of one of the most important parts of the computer concerning performance, namely the memory bank layout in the memory subsystem. In this paper, we present an algorithm to detect the memory bank parameters. We run an implementation of our algorithm on various embedded systems and compare the detected values with the real hardware parameters. The results show that the presented algorithm detects the cache size, the number of TLB entries, and the memory bank layout with high accuracy.

• The KIPS Transactions:PartA
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• v.13A no.2 s.99
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• pp.137-146
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• 2006

Shared Memory Multiprocessor (SMP) systems adopting Chip-level MultiThreading (CMT) technology are becoming mainstream servers in commercial applications and High Performance Computining (HPC) applications as well. OpenMP has become the standard paradigm to parallelize applications for SMP mostly because of its ease of use. As the demand for more computing power in HPC applications is growing rapidly, obtaining high performance and scalability for these applications parallelized using OpenMP API's will become more important. In this paper, we study the performance and scalability of HPC applications parallelized using OpenMP, SPEC OMPL (standard OpenMP benchmark suite), on the Sun Fire E25K server which adopts CMT technology. We also study the effect of CMT on SPEC OMPL.

• Journal of Internet of Things and Convergence
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• v.7 no.4
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• pp.9-14
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• 2021

In computing systems that require high reliability, the method of predicting the lifetime of a storage device is one of the important factors for system management because it can maximize usability as well as data protection. The life of a solid state drive (SSD) that has recently been used as a storage device in several storage systems is linked to the life of the NAND flash memory that constitutes it. Therefore, in a storage system configured using an SSD, a method of accurately and efficiently predicting the lifespan of a NAND flash memory is required. In this paper, a method for optimizing the lifetime prediction of a flash memory-based storage device using the frequency of NAND flash memory failure is proposed. For this, we design a cost matrix to collect the frequency of defects that occur when processing data in units of Drive Writes Per Day (DWPD). In addition, a method of predicting the remaining cost to the slope where the life-long finish occurs using the Gradient Descent method is proposed. Finally, we proved the excellence of the proposed idea when any defect occurs with simulation.

• Journal of the Korea Society of Computer and Information
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• v.25 no.10
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• pp.269-277
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• 2020

In this paper, we propose an efficient dynamic workload balancing strategy which improves the performance of high-performance computing system. The key idea of this dynamic workload balancing strategy is to minimize execution time of each job and to maximize the system throughput by effectively using system resource such as CPU, memory. Also, this strategy dynamically allocates job by considering demanded memory size of executing job and workload status of each node. If an overload node occurs due to allocated job, the proposed scheme migrates job, executing in overload nodes, to another free nodes and reduces the waiting time and execution time of job by balancing workload of each node. Through simulation, we show that the proposed dynamic workload balancing strategy based on CPU, memory improves the performance of high-performance computing system compared to previous strategies.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.43-50
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• 2020

In this paper, we propose an efficient dynamic workload balancing strategy which improves the performance of high-performance computing system. The key idea of this dynamic workload balancing strategy is to minimize execution time of each job and to maximize the system throughput by effectively using system resource such as CPU, memory. Also, this strategy dynamically allocates job by considering demanded memory size of executing job and workload status of each node. If an overload node occurs due to allocated job, the proposed scheme migrates job, executing in overload nodes, to another free nodes and reduces the waiting time and execution time of job by balancing workload of each node. Through simulation, we show that the proposed dynamic workload balancing strategy based on CPU, memory improves the performance of high-performance computing system compared to previous strategies.

• KIISE Transactions on Computing Practices
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• v.24 no.3
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• pp.129-137
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• 2018

NVMe(Non-Volatile Memory Express) SSD(Solid State Drive) is a high-performance storage that makes use of flash memory as a storage cell, PCIe as an interface and NVMe as a protocol on the interface. It supports multiple I/O queues which makes it feasible to process parallel-I/Os on multi-core environments and to provide higher bandwidth than SATA SSDs. Hence, NVMe SSD is considered as a next generation-storage for data-center and cloud computing system. However, in the virtualization system, the performance of NVMe SSD is not fully utilized due to the bottleneck of the software I/O stack. Especially, when it uses I/O stack of the hypervisor or the host operating system like Xen and KVM, I/O performance degrades seriously due to doubled-I/O stack between host and virtual machine. In this paper, we propose a new I/O engine, called Direct-AIO (Direct-Asynchronous I/O) engine, that can access NVMe SSD directly for I/O performance improvements on QEMU emulator. We develop our proposed I/O engine and analyze I/O performance differences between the existed I/O engine and Direct-AIO engine.

• Journal of the Korea Society of Computer and Information
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• v.16 no.9
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• pp.115-122
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• 2011

In this paper, we propose an efficient dynamic workload balancing strategy which improves the performance of high-performance computing system. The key idea of this dynamic workload balancing strategy is to minimize execution time of each job and to maximize the system throughput by effectively using system resource such as CPU, memory. Also, this strategy dynamically allocates job by considering demanded memory size of executing job and workload status of each node. If an overload node occurs due to allocated job, the proposed scheme migrates job, executing in overload nodes, to another free nodes and reduces the waiting time and execution time of job by balancing workload of each node. Through simulation, we show that the proposed dynamic workload balancing strategy based on CPU, memory improves the performance of high-performance computing system compared to previous strategies.