• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.5
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• pp.7-19
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• 2019

Ransomware is a malicious program that requires the cost of decryption after encrypting files on the user's desktop. Since the frequency and the financial damage of ransomware attacks are increasing each year, ransomware prevention, detection and recovery system are needed. Baek et al. proposed SSD-Insider, an algorithm for detecting ransomware within SSD. In this paper, we propose an AdvanSSD-Insider algorithm that substitutes a hash table used for the overwriting check with a bloom filter in the SSD-Insider. Experimental results show that the AdvanSSD-Insider algorithm reduces memory usage by up to 90% and execution time by up to 77% compared to the SSD-Insider algorithm and achieves the same detection accuracy. In addition, the AdvanSSD-Insider algorithm can monitor 10 times longer than the SSD-Insider algorithm in same memory condition. As a result, detection accuracy is increased for some ransomware which was difficult to detect using previous algorithm.

• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.33-39
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• 2022

A storage device used in the mobile computing field should have low power, light weight, durability, etc., and should be able to effectively store and manage large-capacity data generated by users. NAND flash memory is mainly used as a storage device in the field of mobile computing. Due to the structural characteristics of NAND flash memory, it is impossible to overwrite in place when a data update request is made, so it can be solved by accurately separating requests that frequently request data update and requests that do not, and storing and managing them in each block. The classification method for such a data update request is called a hot data identification method, and various studies have been conducted at present. This paper continuously records the occurrence of data update requests using a counting filter for more accurate hot data validation, and also verifies hot data by considering how often the requested update requests occur during a specific time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.11
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• pp.1490-1501
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• 2016

In this paper, we propose an image-based indoor localization system using parallel distributed computing. In order to reduce computation time for indoor localization, an scale invariant feature transform (SIFT) algorithm is performed in parallel by using Apache Spark. Toward this goal, we propose a novel image processing interface of Apache Spark. The experimental results show that the speed of the proposed system is about 3.6 times better than that of the conventional system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5375-5400
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• 2016

IOMMU is a hardware unit that is indispensable for DMA. Besides address translation and remapping, it also provides I/O virtual address space isolation among devices and memory access control on DMA transactions. However, currently commodity virtualization platforms lack of IOMMU virtualization, so that the virtual machines are vulnerable to DMA security threats. Previous works focus only on DMA security problem of directly assigned devices. Moreover, these solutions either introduce significant overhead or require modifications on the guest OS to optimize performance, and none can achieve high I/O efficiency and good compatibility with the guest OS simultaneously, which are both necessary for production environments. However, for simulated virtual devices the DMA security problem also exists, and previous works cannot solve this problem. The reason behind that is IOMMU circuits on the host do not work for this kind of devices as DMA operations of which are simulated by memory copy of CPU. Motivated by the above observations, we propose an IOMMU para-virtualization solution called PVIOMMU, which provides general functionalities especially DMA security guarantees for both directly assigned devices and simulated devices. The prototype of PVIOMMU is implemented in Qemu/KVM based on the virtio framework and can be dynamically loaded into guest kernel as a module, As a result, modifying and rebuilding guest kernel are not required. In addition, the device model of Qemu is revised to implement DMA access control by separating the device simulator from the address space of the guest virtual machine. Experimental evaluations on three kinds of network devices including Intel I210 (1Gbps), simulated E1000 (1Gbps) and IB ConnectX-3 (40Gbps) show that, PVIOMMU introduces little overhead on DMA transactions, and in general the network I/O performance is close to that in the native KVM implementation without IOMMU virtualization.

• Journal of KIISE
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• v.41 no.9
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• pp.674-685
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• 2014

The Knowledge service system needs to infer a new knowledge from indicated knowledge to provide its effective service. Most of the Knowledge service system is expressed in terms of ontology. The volume of knowledge information in a real world is getting massive, so effective technique for massive data of ontology is drawing attention. This paper is to provide the method to infer massive data-ontology to the extent of RDFS, based on cloud computing environment, and evaluate its capability. RDFS inference suggested in this paper is focused on both the method applying MapReduce based on RDFS meta table, and the method of single use of cloud computing memory without using MapReduce under distributed file computing environment. Therefore, this paper explains basically the inference system structure of each technique, the meta table set-up according to RDFS inference rule, and the algorithm of inference strategy. In order to evaluate suggested method in this paper, we perform experiment with LUBM set which is formal data to evaluate ontology inference and search speed. In case LUBM6000, the RDFS inference technique based on meta table had required 13.75 minutes(inferring 1,042 triples per second) to conduct total inference, whereas the method applying the cloud computing memory had needed 7.24 minutes(inferring 1,979 triples per second) showing its speed twice faster.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.5
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• pp.150-164
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• 2013

Currently it is being gradually focused on the mobile application's processing performance implemented by Javascript and HTML (Hyper Text Markup Language) due to the dissemination of mobile web application supply based on the WAC (Wholesale Application Community). If the application software has a simple functional processing structure, then the problem is benign, however, the load of a browser is getting heavier as the amount of Javascript processing is being increased. There is a limitation on the processing time and capacity of the Javascript in the ordinary mobile browsers which are on the market now. In order to solve those problems, the Web Worker that is not supported from the existing Javascript technology is now provided by the HTML 5 to implement the multi thread. The Web Worker provides a mechanism that process a part from the single thread through a separate one. However, it can not guarantee the computing ability as a native application on the mobile and is not enough as a solution for improving the fundamental processing speed. The Cyclostorm overcomes the limitation of resources as a mobile client and guarantees the performance as a native application by providing high computing service and ascripting the Javascript process on the mobile to the computer server on the cloud. From the performance evaluation experiment, the Cyclostorm shows a maximally 6 times faster computing speed than in the existing mobile browser's Javascript and 3 to 6 times faster than in Web Worker of the HTML 5. In addition, the usage of memory is measured less than the existing method since the server's memory has been used. In this paper, the Cyclostorm is introduced as one of the mobile cloud computing services to conquer the limitation of the WAC based mobile browsers and to improve the existing web application's performances.

• Journal of Computing Science and Engineering
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• v.6 no.4
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• pp.287-293
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• 2012

We introduce a methodology for the study of the application-level performance of time-sharing parallel jobs on a set of compute nodes in high performance clusters and report our findings. We assume that parallel jobs arriving at a cluster need to share a set of nodes with the jobs of other users, in that they must compete for processor time in a time-sharing manner and other limited resources such as memory and I/O in a space-sharing manner. Under the assumption, we developed a methodology to simulate job arrivals to a set of compute nodes, and gather and process performance data to calculate the percentage slowdown of parallel jobs. Our goal through this study is to identify a better combination of jobs that minimize performance degradations due to resource sharing and contention. Through our experiments, we found a couple of interesting behaviors for overlapped parallel jobs, which may be used to suggest alternative job allocation schemes aiming to reduce slowdowns that will inevitably result due to resource sharing on a high performance computing cluster. We suggest three job allocation strategies based on our empirical results and propose further studies of the results using a supercomputing facility at the San Diego Supercomputing Center.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.731-751
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• 2017

The burgeoning distribution of smartphone web applications based on various mobile environments is increasingly focusing on the performance of mobile applications implemented by JavaScript and HTML5 (Hyper Text Markup Language 5). If application software has a simple functional processing structure, then the problem is benign. However, browser loads are becoming more burdensome as the amount of JavaScript processing continues to increase. Processing time and capacity of the JavaScript in current mobile browsers are limited. As a solution, the Web Worker is designed to implement multi-threading. However, it cannot guarantee the computing ability as a native application on mobile devices, and is not sufficient to improve processing speed. The method proposed in this research overcomes the limitation of resources as a mobile client and guarantees performance by native application software by providing high computing service. It shifts the JavaScript process of a mobile device on to a cloud-based computer server. A performance evaluation experiment revealed the proposed algorithm to be up to 6 times faster in computing speed compared to the existing mobile browser's JavaScript process, and 3 to 6 times faster than Web Worker. In addition, memory usage was also less than the existing technology.

• Journal of the Korea Society of Computer and Information
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• v.11 no.3
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• pp.141-149
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• 2006

In this paper, we introduce an improved algorithm for computing matrix triple product that commonly arises in primal-dual optimization method. In computing $P=AHA^{t}$, we devise a single pass algorithm that exploits the block diagonal structure of the matrix H. This one-phase scheme requires fewer floating point operations and roughly half the memory of the generic two-phase algorithm, where the product is computed in two steps, computing first $Q=HA^{t}$ and then P=AQ. The one-phase scheme achieved speed-up of 2.04 on Intel Itanium II platform over the two-phase scheme. Based on memory latency and modeled cache miss rates, the performance improvement was evaluated through performance modeling. Our research has impact on performance tuning study of complex sparse matrix operations, while most of the previous work focused on performance tuning of basic operations.

• Journal of the Korea Society of Computer and Information
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• v.19 no.6
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• pp.81-92
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• 2014

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.