• Journal of the Korean Society of Systems Engineering
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• v.19 no.1
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• pp.1-13
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• 2023

Transportation of goods by rail in border areas requires considerable time, money, and human resources. Therefore, in this study, a blockchain-based non-stop passing system is proposed to solve this problem. In this study, each transit station and train are designated as one network node, and the corresponding node participates in the blockchain network to record and verify data. In the process, we will design a blockchain network using Docker and design a network interface. Without changing the data and information generated in the existing legacy clearance system, it is possible to configure a blockchain network to ensure the integrity and reliability of the data and to minimize the consumption of time and human resources. The railroad non-stop passing system aims to change the existing legacy system to a blockchain-based non-stop passing system.

• Annual Conference of KIPS
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• 2022.11a
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• pp.913-915
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• 2022

본 논문은 클라우드 환경에 적합한 IAM(Identity and Access Management) 솔루션을 제안한다. 오픈소스 라이브러리인 Keycloak[1]을 이용하여 그룹 별 권한 관리 및 권한에 따른 리소스 관리가 가능하도록 하며, 솔루션을 쉽게 도입하여 사용할 수 있도록 컨테이너 기술을 통해 신속하게 환경을 구축하고 배포할 수 있게 도와주는 플랫폼인 Docker 를 사용해 Docker image 형식으로 제공한다.

• Annual Conference of KIPS
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• 2024.05a
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• pp.101-104
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• 2024

최근 클라우드가 전 산업에 도입되면서 클라우드 네이티브 환경에 관한 관심이 증가하고 있다. 클라우드 서비스 개발자는 도커 (Docker) 이미지를 활용하여 개발 환경을 구축하고 배포한다. 그러나 종래의 이미지 스캐닝 도구들은 해시값 기반의 시그니처 탐지 방법론을 사용하기 때문에 제로데이 취약점을 탐지하지 못하거나, 이미 저장된 CVE DB에 있는 취약점만 탐지할 수 있었다. 본 논문은 도커 이미지의 계층성을 활용하여 다형성 도커 이미지 공격을 탐지할 수 있는 기법을 제안한다. 실험결과에 따르면 제안한 방법은 종래 방법 대비 다형성 도커 이미지 공격 탐지율을 28.6% 개선할 수 있었다.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.10
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• pp.415-420
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• 2017

Virtualization technique of OS-level is a new paradigm for deploying applications, and is attracting attention as a technology to replace traditional virtualization technique, VM (Virtual Machine). Especially, docker containers are capable of distributing application images faster and more efficient than before by applying layered image structures and union mount point to existing linux container. These characteristics of containers can only be used in layered file systems that support snapshot functionality, so it is required to select appropriate layered file systems according to the characteristics of the containerized application. We examine the characteristics of representative layered file systems and conduct write performance evaluations of each layered file systems according to the operating principles of the layered file system, Allocate-on-Demand and Copy-up. We also suggest the method of determining a appropriate layered file system principle for unknown containerized application by learning block I/O usage history of each layered file system principles in artificial neural network. Finally we validate effectiveness of artificial neural network created from block I/O history of each layered file system principles.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.65-73
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• 2022

Recently, as the Windows system supports the Windows subsystem for Linux (WSL), various researchers have studied to apply a docker container on various systems such as server systems, workstation system and so on. However, in various existing researchers, there is a lack of performance-related indicators to apply the system to each operating system (linux system and windows system). In this paper, we compared a performance comparison and analysis of container-based host operating systems. We configured experimental environments of operating systems for microsoft windows systems and linux systems based on a docker container support. In experimental results, the containers of linux systems reduced the average data latency of dataset 1-6 by 3.9%, 62.16%, 1552.38%, 7.27%, 60.83%, and 1567.2%, compared to the containers on microsoft windows systems.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.4
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• pp.372-380
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• 2022

This study proposes a distributed parallel processing system, called the Fast Analysis System for remote sensing daTa(FAST), for large-scale satellite image processing and analysis. FAST is a system that designs jobs in vertices and sequences, and distributes and processes them simultaneously. FAST manages data based on the Hadoop Distributed File System, controls entire jobs based on Apache Spark, and performs tasks in parallel in multiple slave nodes based on a docker container design. FAST enables the high-performance processing of progressively accumulated large-volume satellite images. Because the unit task is performed based on Docker, it is possible to reuse existing source codes for designing and implementing unit tasks. Additionally, the system is robust against software/hardware faults. To prove the capability of the proposed system, we performed an experiment to generate the original satellite images as ortho-images, which is a pre-processing step for all image analyses. In the experiment, when FAST was configured with eight slave nodes, it was found that the processing of a satellite image took less than 30 sec. Through these results, we proved the suitability and practical applicability of the FAST design.

• Genomics & Informatics
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• v.15 no.4
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• pp.178-182
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• 2017

Next-generation sequencing (NGS) technology has become a trend in the genomics research area. There are many software programs and automated pipelines to analyze NGS data, which can ease the pain for traditional scientists who are not familiar with computer programming. However, downstream analyses, such as finding differentially expressed genes or visualizing linkage disequilibrium maps and genome-wide association study (GWAS) data, still remain a challenge. Here, we introduce a dockerized web application written in R using the Shiny platform to visualize pre-analyzed RNA sequencing and GWAS data. In addition, we have integrated a genome browser based on the JBrowse platform and an automated intermediate parsing process required for custom track construction, so that users can easily build and navigate their personal genome tracks with in-house datasets. This application will help scientists perform series of downstream analyses and obtain a more integrative understanding about various types of genomic data by interactively visualizing them with customizable options.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.4
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• pp.153-161
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• 2021

Real-time access is required to handle continuous and unstructured data and should be flexible in management under dynamic state. Platform can be built to allow data collection, storage, and processing from local-server or multi-server. Although the former centralize method is easy to control, it creates an overload problem because it proceeds all the processing in one unit, and the latter distributed method performs parallel processing, so it is fast to respond and can easily scale system capacity, but the design is complex. This paper provides data collection and processing on one platform to derive significant insights from various data held by an enterprise or agency in the latter manner, which is intuitively available on dashboards and utilizes Spark to improve distributed processing performance. All service utilize dockers to distribute and management. The data used in this study was 100% collected from Kafka, showing that when the file size is 4.4 gigabytes, the data processing speed in spark cluster mode is 2 minute 15 seconds, about 3 minutes 19 seconds faster than the local mode.

• Journal of Practical Engineering Education
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• v.16 no.3_spc
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• pp.267-274
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• 2024

One challenge with teaching practical programming classes is the standardization of development tools on student computers. This is particularly true when a complicated setup process is required before beginning to code, or in remote classes, such as those necessitated by the COVID-19 pandemic, where the instructor cannot provide individual troubleshooting assistance. In such cases, students who encounter problems during the setup process may give up on the class altogether before even beginning to code. Therefore, this paper recommends using GitHub Codespaces as a tool for implementing standardized student development environments from day one. Codespaces provides Docker containers that an instructor can configure in such a way as to enable students to practice installing various coding tools within a controlled space, while also providing a language-specific, fully optimized development environment. In addition, Codespaces may be used more effectively in collaboration with GitHub Classroom, which helps instructors manage both the starter code and coding environment in which students work. In this paper, we compare two semesters of university Node.JS programming classes that utilized different development environments: one localized on student computers, the other containerized in Codespaces online. Then, we discuss how GitHub Codespaces and GitHub Classroom can be used to increase the effectiveness of practical programming classes while also increasing student engagement and programming confidence in class.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.97-106
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• 2020

Due to the rapid development and dissemination of 5G communication and IoT technologies, there are increasing demands for big data analysis techniques and service systems. In particular, explosively growing demands on AI technology adoption are also causing high competitions to take advantages of machine/deep-learning models to extract novel values from enormously collected data. In order to adopt AI technology to various research and application domains, it is necessary to prepare high-performance GPU-equipped systems and perform complicated settings to utilze deep learning models. To relieve the efforts and lower the barrier to utilize AI techniques, AIaaS(AI as a service) platform is attracting a great deal of attention as a promising on-line service, where the complexity of preparation and operation can be hidden behind the cloud side and service developers only need to utilize the high-level AI services easily. In this paper, we propose an AIaaS system which can support the creation of AI services based on Docker and OpenFaaS from the registration of models to the on-line operation. We also describe a case study to show how AI services can be easily generated by the proposed system.