Docker Hub

Cook, Joshua

doi:10.1007/978-1-4842-3012-1_6

Cited by 10 publications

(5 citation statements)

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“…• RunC: following the Open With minimal resource usage, it facilitates easy project transfer between devices, providing isolation and segregation [22]. DockerHub [23] enables the sharing of containerized applications, making it suitable for ARM platforms, as well as scanning and security tools [24]. In general, Docker stands out for its ease of use, security, and community support on ARM platforms.…”

Section: A Container Technologymentioning

confidence: 99%

Performance Analysis of Container Technologies for Computer Vision Applications on Edge Devices

Alqaisi,

Şaman Tosun,

Korkmaz

2024

IEEE Access

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In the evolving tech landscape, various container technologies coexist and offer compelling advantages in quickly deploying applications and efficiently utilizing resources on edge devices. Despite the potential benefits of containerization in general, limited research has explored how various container technologies perform in specific domains. In response, this paper provides an extensive evaluation of container technologies (e.g., RunC, LXC, Containerd, Docker, Podman, and Singularity) in the context of OpenCV-based computer vision applications on ARM-based edge devices. Experiments verify that the performance of containerized computer vision applications is comparable to that of non-containerized ones. While the performance is roughly equivalent across all container runtimes/engines, Docker consistently demonstrates superior efficiency for computer vision applications on ARM-based edge devices. These insights contribute to bridge the existing gap to the integration of containers in IoT and ARM-based edge computing scenarios.

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Section: A Container Technologymentioning

confidence: 99%

Performance Analysis of Container Technologies for Computer Vision Applications on Edge Devices

Alqaisi,

Şaman Tosun,

Korkmaz

2024

IEEE Access

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“…We focused on the management of life cycles for file-based persistent volumes. With Shadowyer, a service program is assumed to run in a container such as containerized programs published on the Docker Hub repositories [14]. Shared data are assumed to be passed to a service program through persistent volumes in which these are stored as files.…”

Section: Goalsmentioning

confidence: 99%

Container-native Managed Data Sharing

Onoue,

Ueno,

Noma

2023

Journal of Information Processing

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Cross-industrial collaboration can yield disruptive innovations. However, there are fears regarding data sharing across different organizations. Even if data providers make contracts covering the data shared with their consumers, they will not be able to delete the shared data in accordance with the expiration dates and modify them after they were delivered to the consumers. Data consumers need to be extremely careful about management of shared data since huge penalties are imposed against violations of data protection laws. To allay these fears, we propose a system to handle external data management instead of data providers and consumers. In our system, containerized lifetime controllers delete expired shared data in accordance with contracts for shared data called life cycle policies. To allow only service programs stipulated in the policies to manipulate shared data, containerized volume controllers enforce the access control on the basis of the FUSE interceptions and the /proc file system. The proposed system is transparent to service programs because the containerized controllers run in execution environments that are separate from service programs. The proposed system can be applied to multiple container orchestration clusters in which a provider and consumer independently administer Kubernetes container orchestrators, as well as a single container orchestration cluster. We built a prototype system on Kubernetes container orchestrators presented by the Kubernetes community and public cloud service providers. Experimental results demonstrate that the proposed system achieves data sharing between a provider and consumer with moderate overheads for disk consumption of the containerized controllers, the extensions of the volume drivers, and execution time of the FUSE access control.

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“…Container registries provide an authoritative mapping from a container name (e.g., https://quay.io/repository/biocontainers/hisat2?tag=2.1.0-py27pl5.22.0_0&tab=tags) to a specific container image. We register containers with Quay (https://quay.io), and since we build standard open container initiative (OCI) containers (https://www.opencontainers.org), other registries (e.g., DockerHub, Cook, 2017; BioContainers, da Veiga Leprevost et al, 2017; or Dockstore, O’Connor et al, 2017) can also be used. Finally, in addition to creating containers for single Bioconda packages, it is possible to automatically create containers for combinations of packages.…”

Section: A Technology Stack For Reproducibilitymentioning

confidence: 99%

Practical Computational Reproducibility in the Life Sciences

et al. 2018

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Many areas of research suffer from poor reproducibility, particularly in computationally intensive domains where results rely on a series of complex methodological decisions that are not well captured by traditional publication approaches. Various guidelines have emerged for achieving reproducibility, but implementation of these practices remains difficult due to the challenge of assembling software tools plus associated libraries, connecting tools together into pipelines, and specifying parameters. Here, we discuss a suite of cutting-edge technologies that make computational reproducibility not just possible, but practical in both time and effort. This suite combines three well-tested components—a system for building highly portable packages of bioinformatics software, containerization and virtualization technologies for isolating reusable execution environments for these packages, and workflow systems that automatically orchestrate the composition of these packages for entire pipelines—to achieve an unprecedented level of computational reproducibility. We also provide a practical implementation and five recommendations to help set a typical researcher on the path to performing data analyses reproducibly.

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Docker Hub

Cited by 10 publications

References 0 publications

Performance Analysis of Container Technologies for Computer Vision Applications on Edge Devices

Performance Analysis of Container Technologies for Computer Vision Applications on Edge Devices

Container-native Managed Data Sharing

Practical Computational Reproducibility in the Life Sciences

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