• KIPS Transactions on Computer and Communication Systems
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• v.3 no.11
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• pp.409-418
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• 2014

The amount of data located in storage servers has dramatically increased with the growth in cloud and social networking services. Storage systems with very large capacities may suffer from poor reliability and long latency, problems which can be addressed by the use of a hybrid disk, in which mechanical and flash memory storage are combined. The Linux-based SSD(solid-state disk) uses a caching technique based on the DM-cache utility. We assess the limitations of DM-cache by evaluating its performance in diverse environments, and identify problems with the caching policy that it operates in response to various commands. This policy is effective in reducing latency when Linux is running in native mode; but when Linux is installed as a guest operating systems on a virtual machine, the overhead incurred by caching actually reduces performance.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.2
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• pp.67-72
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• 2022

Recently, various intelligent application services using artificial intelligence are being actively developed. In particular, research on artificial intelligence-based real-time prediction services is being actively conducted in the manufacturing industry, and the demand for artificial intelligence services that can detect and predict fire and odors is very high. However, most of the existing detection and prediction systems do not predict the occurrence of fires and odors, but rather provide detection services after occurrence. This is because AI-based prediction service technology is not applied in existing systems. In addition, fire prediction, odor detection and odor level prediction services are services with ultra-low delay characteristics. Therefore, in order to provide ultra-low-latency prediction service, edge computing technology is combined with artificial intelligence models, so that faster inference results can be applied to the field faster than the cloud is being developed. Therefore, in this paper, we propose an LSTM algorithm-based learning model that can be used for fire prediction and odor detection/prediction, which are most required in the manufacturing industry. In addition, the proposed learning model is designed to be implemented in edge devices, and it is proposed to receive real-time sensor data from the IoT terminal and apply this data to the inference model to predict fire and odor conditions in real time. The proposed model evaluated the prediction accuracy of the learning model through three performance indicators, and the evaluation result showed an average performance of over 90%.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.12
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• pp.287-296
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• 2019

Due to the development and increased usage of Internet services such as IoT and cloud computing, a large number of packets are being generated on the Internet. In order to create a safe Internet environment, malicious data that may exist among these packets must be processed and detected quickly. In this paper, we apply MongoDB, which is specialized for unstructured data analysis and big data processing, to intrusion detection system for rapid processing of big data security events. In addition, building the intrusion detection system(IDS) using some of the private cloud resources which is the target of protection, elastic and dynamic reconfiguration of the IDS is made possible as the number of security events increase or decrease. In order to evaluate the performance of MongoDB - based IDS proposed in this paper, we constructed prototype systems of IDS based on MongoDB as well as existing relational database, and compared their performance. Moreover, the number of virtual machine has been increased to find out the performance change as the IDS is distributed. As a result, it is shown that the performance is improved as the number of virtual machine is increased to make IDS distributed in MongoDB environment but keeping the overall system performance unchanged. The security event input rate based on distributed MongoDB was faster as much as 60%, and distributed MongoDB-based intrusion detection rate was faster up to 100% comparing to the IDS based on relational database.

• Journal of Korea Multimedia Society
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• v.11 no.8
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• pp.1169-1178
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• 2008

Most of information in Web2.0 is constructed by users and can be classified by tags which are also constructed and added by users. However, as we known, referring by the related works such as automatic tagging techniques and tag cloud's construction techniques, the research to be classified information and resources by tags effectively is to be given users which is still up to the mark. In this paper, we propose and implement a clustering system that does mapping each other according to relationships of the resource's tags collected from Web and then makes the mapping result into clusters to retrieve images. Tn addition, we analyze our system's efficiency by comparing our proposed system's image retrieval result with the image retrieval results searched by Flickr website.

• Journal of Software Assessment and Valuation
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• v.15 no.2
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• pp.67-72
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• 2019

The recent explosion of data traffic, including cloud services, coupled with the Internet penetration has led to a surge in the need for ultra-fast optical networks that can efficiently connect the data center's reconfiguable resources. In this paper, the algorithms for controlling switching cell operation in the optical switch connection structure are proposed, and the resulting performance is compared and analyzed through simulation. Performance analysis results showed that the algorithm proposed in this paper has improved the probability of successful multi-connection setup by about 3 to 7% compared to the existing algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.3
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• pp.97-103
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• 2020

Linux Cgroups takes a fundamental role for sharing system resources among multiple containers on container-based cloud computing environment. Especially for I/O resource, Linux Cgroups supports a mechanism for sharing I/O bandwidth in proportion to I/O weight. However, the current mechanism of Linux Cgroups using BFQ I/O scheduler seriously degrades the I/O performance with high bandwidth storage device such as NVMe SSDs. In this paper, we proposed a new feedback based I/O bandwidth sharing scheme for Linux Cgroups which allocates I/O credits to containers according to I/O weights and adjusts the amount of credits to performance fluctuation of NVMe SSDs. The proposed scheme is implemented on Linux kernel 5.3 and evaluated. The evaluation results show that it can share the I/O bandwidth among multiple containers proportionally to I/O weights while improving I/O performance more than twice as high as the existing scheme.

• 전자공학회논문지 IE
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• v.48 no.4
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• pp.61-68
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• 2011

As business management has a high level of dependence on Informational Technology (IT), protecting assets of a company from disaster is one of the most important thing that IT operating managers should consider. Because data or information is a major source of operation of the company, data security is the first priority as an aspect of continuity of business management. Therefore, this paper will realize disaster recovery system, which is suspended because of disaster, based on cloud computing system. Realized High Available Replicate System applied a method of multi thread target database to improve Replicate performance, and real time synchronize technology can improve efficiency of network. From Active to Active operation, it maximizes use of backup system, and it has a effect to disperse load of source database system. Also, High Available Replicate System realized consistency verification mechanism and monitoring technique. For Performance evaluation, High Available Replicate System used multi thread method, which shows more than threefold of replicate performance than single thread method.

• Convergence Security Journal
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• v.24 no.1
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• pp.35-42
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• 2024

Multi-tenancy architecture plays a crucial role in cloud-based services and applications, and data isolation within such environments has emerged as a significant security challenge. This paper investigates various data isolation methods including schema-based isolation, logical isolation, and physical isolation, and compares their respective advantages and disadvantages. It evaluates the practical application and effectiveness of these data isolation methods, proposing security considerations and selection criteria for data isolation in the development of multi-tenant websites. This paper offers important guidance for developers, architects, and system administrators aiming to enhance data security in multi-tenancy environments. It suggests a foundational framework for the design and implementation of efficient and secure multi-tenant websites. Additionally, it provides insights into how the choice of data isolation methods impacts system performance, scalability, maintenance ease, and overall security, exploring ways to improve the security and stability of multi-tenant systems.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.715-725
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• 2022

With the development of digital technology related to mobile, cloud, IoT, even traditional businesses have began to develop strategies for digital transformation. This is regarded as a very important part for business sustainability. However, many of these attempts are being conducted from the management point of view, and there has been little research on user experience design for digital transformation. In this study, Starbucks, which started a offline store business and expanded its experience with online services, was selected as a representative case and user research was conducted on it. Through an usability evaluation and post-task interviews of the Starbucks mobile application users, we discovered important issues of user experience to consider when transforming and expanding services online from offline. Based on the findings, the considersations for designing online user experience were discussed and proposed. These are expected to contribute to the process of researching and implementing digital transformation.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.479-479
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• 2021

최근 기상이변으로 인한 자연재해 발생이 증가하고 있고, 그에 따라 도시의 재난 대응력 강화가 국내에서는 물론 국제적으로도 중요한 이슈가 되고 있다. 특히 쇠퇴지역은 재난재해 발생 시 인적·물적 피해가 일반 지역 보다 상대적으로 크며, 복구에도 많은 시간과 예산이 소요되므로 대응책 마련을 위한 도시재생지역의 정밀한 재난재해의 위험성 분석 기술이 필요하다. 이에 본 연구에서는 도시재생사업 대상지(311개)에 대한 재난재해 유형별 위험성 및 회복성을 종합적으로 분석하는 종합진단 기법을 개발하고, 이를 적용한 프로토타입 시스템을 개발하였다. 재난재해의 범위는 「재난 및 안전관리 기본법」을 준용하여 이에 도시재생사업 시행에 영향을 받아 재난재해 발생에 따른 위험정도가 변화할 가능성이 높은 자연재해 (폭우, 폭염, 폭설, 강풍, 지진)5종과 사회재난 (화재, 붕괴, 폭발) 3종 총 8종으로 정의하였다. 종합진단 기법은 기후변화에 관한 정부간 협의체(IPCC) 위험도 평가 방법을 준용하여 위험요소 (위해성·취약성·노출성)와 대비·대응요소 (회복성)로 구분하고, 전문가 자문회의를 거쳐 재난재해에 특히 취약한 쇠퇴지역의 특성을 반영할 수 있는 종합진단지수 산정식을 개발하였다. 또한 쇠퇴지역 재난재해 종합진단 시스템은 도시재생 업무를 수행하는 사용자가 신속히 정보를 분석하고 활용에 용이하도록 Web-GIS 기반으로 설계하였으며, 종합진단 기법에 의해 산정된 분석결과를 100m × 100m 격자 단위의 등급으로 가시화한다. 분석 결과는 지속적인 연구 개발을 통해 최적의 도시재생사업 의사결정 지원 서비스를 위한 기초 분석 자료로 연계하여 활용되며, 분석 DB는 클라우드 서비스 기반의 도시재생 데이터 플랫폼을 통해 공유된다.