• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.742-753
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• 2022

Cloud computing, which is rapidly growing as one of the core technologies of the 4th industrial revolution, has become the center of global IT trend change, and Docker, a container-based open source platform, is the mainstream for virtualization technology for cloud computing. Therefore, in this paper, research trends based on Docker containers were compared and analyzed, focusing on studies published from March 2013 to July 2022. As a result of the study, first, the number of papers published by year, domestic and foreign research were steadily increasing. Second, the keywords of the study, in domestic research, Docker, Docker Containers, and Containers were in the order, and in foreign research, Cloud Computing, Containers, and Edge Computing were in the order. Third, in the frequency of publishing institutions to estimate research trends, the utilization was the highest in two papers of the Korean Next Generation Computer Society and the Korean Computer Accounting Society. In the overseas research, IEEE Communications Surveys & Tutorials, IEEE Access, and Computer were in the order. Fourth, in the research method, experiments 78(26.3%) and surveys 32(10.8%) were conducted in domestic research. In foreign research, experiments 128(43.1%) and surveys 59(19.9%) were conducted. In the experiment of implementation research, In domestic research, System 25(8.4%), Algorithm 24(8.1%), Performance Measurement and Improvement 16(5.4%) were in the order, In foreign research, Algorithm 37(12.5%), Performance Measurement and Improvement 17(9.1%), followed by Framework 26(8.8%). Through this, it is expected that it will be used as basic data that can lead the research direction of Docker container-based cloud computing such as research methods, research topics, research fields, and technology development.

• KIISE Transactions on Computing Practices
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• v.22 no.2
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• pp.89-94
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• 2016

Recently, cluster computing environments have been in a process of change toward virtualized cluster environments. The change of the cluster environment has great impact on the performance of large volume distributed processing. Therefore, many domestic and international IT companies have invested heavily in research on cluster environments. In this paper, we show how the hypervisor affects the performance of distributed processing of a large volume of data. We present a performance comparison of MapReduce processing in two virtualized cluster environments, one built using the Xen hypervisor and the other built using the container-based Docker. Our results show that Docker is faster than Xen.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.2
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• pp.238-240
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• 2016

Recently, the improvement in the data communication networks in terms of availability and reliability is emerging as a critical issue. In this context, the high availability and reliability of routers that control data flow and routing path between networks on the data communication network are being recognized as critical problems. Many methods for providing high availability to minimize the loss cost caused by the failure of a router are being studied. This paper proposed a BGP session takeover method using the Docker to support the high availability of the BGP session and to improve the takeover performance.

• International Journal of Contents
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• v.12 no.3
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• pp.29-33
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• 2016

Cloud computing makes extensive use of virtual machines that permit for workloads, as well as resource usage, to be isolated from one another, and a hypervisor can be used by a virtual machine to construct cloud computing infrastructure. However, the hypervisor has high resource usage when constructing virtual machines, which results in a waste of allocated resources when not activated. Docker provides a more light-weight method to obtain agile computing resources based on a container technique that handles this problem. In this study, we have chosen this specific tool due to the increasing popularity of MapReduce and cloud container technologies such as Docker. This study aims to automatically configure Twister workloads for container-driven clouds. Basically, this is the first attempt towards automatic configuration of Twister jobs on a container-based cloud platform VM for many workloads.

• Annual Conference of KIPS
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• 2018.10a
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• pp.46-48
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• 2018

Docker에서 제공하는 API들은 컨테이너 엔진을 통하여 실행되어지게 된다. 따라서 컨테이너의 생성과 삭제 또는 정보에 대한 요청에 대해서 속도가 매우 느린 것이 현실이다. 많은 컨테이너에서 API 요청을 보내는 경우에는 부하가 더욱 심각하다. 따라서 본 연구에서는 Serverless 환경에서 컨테이너에서 발생하는 API 요청에 대한 부하를 줄이기 위해서 컨테이너 내부에 자원관리에 관련된 모듈을 적용 시킬 수 있는 방법과, Serverless 환경에서 여러 개의 컨테이너 또는 여러 대의 Docker 서버의 자원을 관리 할 수 있는 프레임워크를 제시한다.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.11-20
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• 2020

In this paper, we propose the possibility of using containers in the HPC ecosystem and the criteria for selecting a proper PMI library. Although demand for container has been growing rapidly in the HPC ecosystem, Docker container which is the most widely used has a potential security problem and is not suitable for the HPC. Therefore, several HPC containers have appeared to solve this problem and the chance of performance differences also emerged. For this reason, we measured the performance difference between each HPC container and Docker container through NAS Parallel Benchmark experiment and checked the effect of the type of PMI library. As a result, the HPC container and the Docker container showed almost the same performance as native, or in some cases, rather better performance was observed. In the result of comparison between PMI libraries showed that PMIx was not superior to PMI-2 in all conditions.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.6
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• pp.1205-1214
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• 2021

Edge Computing is used as a solution to the cost problem and transmission delay problem caused by network bandwidth consumption that occurs when IoT/CPS devices are integrated into the cloud by performing artificial intelligence (AI) in an environment close to the data source. Since edge computing runs on devices that provide high-performance computation and network connectivity located in the real world, it is necessary to consider application integrity so that it is not exploited by cyber terrorism that can cause human and material damage. In this paper, we propose a technique to protect the integrity of edge computing applications implemented in a script language that is vulnerable to tampering, such as Python, which is used for implementing artificial intelligence, as container images and then digitally signed. The proposed method is based on the integrity protection technology (Docker Contents Trust) provided by the open source container technology. The Docker Client was modified and used to utilize the whitelist for container signature information so that only containers allowed on edge computing devices can be operated.

• KIISE Transactions on Computing Practices
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• v.21 no.9
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• pp.587-595
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• 2015

Several fields of science have traditionally demanded large-scale workflow support, which requires thousands of central processing unit (CPU) cores. In order to support such large-scale scientific workflows, large-capacity cluster systems such as supercomputers are widely used. However, as users require a diversity of software packages and configurations, a system administrator has some trouble in making a service environment in real time. In this paper, we present a container-based cluster management platform and introduce an implementation case to minimize performance reduction and dynamically provide a distributed computing environment desired by users. This paper offers the following contributions. First, a container-based virtualization technology is assimilated with a resource and job management system to expand applicability to support large-scale scientific workflows. Second, an implementation case in which docker and HTCondor are interlocked is introduced. Lastly, docker and native performance comparison results using two widely known benchmark tools and Monte-Carlo simulation implemented using various programming languages are presented.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1398-1404
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• 2018

High-performance computing (HPC) provides to researchers a powerful ability to resolve problems with intensive computations, such as those in the math and medical fields. When an HPC platform is provided as a service, users may suffer from unexpected obstacles in developing and running applications due to restricted development environments and dependencies. In this context, operating system level virtualization can be a solution for HPC service to ensure lightweight virtualization and consistency in Dev-Ops environments. Therefore, this paper proposes three types of typical HPC structure for container environments built with HPC container and Docker. The three structures focus on smooth integration with existing HPC job framework, message passing interface (MPI). Lastly, the performance of the structures is analyzed with High Performance Linpack benchmark from the aspect of performance degradation in network communications under Docker.

• Annual Conference of KIPS
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• 2022.05a
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• pp.420-423
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• 2022

최근 딥러닝 기술이 모바일 기기에 활발히 적용됨에 따라 다양한 엣지 디바이스에서 신경망 모델의 추론 성능을 측정하는 것이 중요해지고 있다. 하지만 디바이스 별 환경 구성과 런타임별 모델 변환 방식이 다르기 때문에 이를 실제로 수행하는 것은 많은 시간을 필요로 한다. 따라서 본 논문에서는 이기종 환경을 고려하여 추론 성능을 측정할 수 있는 Docker 기반의 프로그램을 구현하였고, 이를 이용하여 다양한 엣지 디바이스에서 최신 모델들의 추론 성능을 측정하였다. 또한, 본 프로그램으로 확보 가능한 추론시간 데이터 기반 추론 성능 예측 연구의 사전 연구로서, 대표적 경량모델인 MobilenetV1 에 대한 연산자별 프로파일링을 수행하여 추론시간의 변화 양상을 관찰하였다.