边缘驱动的 Docker 注册表:促进 XR 应用部署

IF 3.3 3区 计算机科学 Q2 COMPUTER SCIENCE, THEORY & METHODS Computing Pub Date : 2024-06-24 DOI:10.1007/s00607-024-01310-0
Antonios Makris, Evangelos Psomakelis, Ioannis Korontanis, Theodoros Theodoropoulos, Ioannis Kontopoulos, Maria Pateraki, Christos Diou, Konstantinos Tserpes
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引用次数: 0

摘要

近年来,容器化在部署应用程序和服务方面越来越流行,并极大地促进了边缘计算的扩展。然而,随着部署容器数量的增加,对有效且可扩展的容器映像管理的需求也随之增加。一种解决方案是在边缘使用本地化的 Docker 注册表,将映像存储在离部署站点更近的地方。这种方法可以大大减少从中央注册中心下载映像所需的延迟和带宽。此外,它还能优化下载延迟和网络流量,起到主动缓存机制的作用。在本文中,我们介绍了一种结合了本地化 Docker 注册表的边缘存储框架。该框架旨在简化容器映像的存储和分发,为边缘部署提供更好的控制、可扩展性和优化功能。我们采用了四个要求苛刻的 XR 应用程序作为用例,对所提出的解决方案进行实验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Edge-driven Docker registry: facilitating XR application deployment

In recent years, containerization is becoming more and more popular for deploying applications and services and it has significantly contributed to the expansion of edge computing. The demand for effective and scalable container image management, however, increases as the number of containers deployed grows. One solution is to use a localized Docker registry at the edge, where the images are stored closer to the deployment site. This approach can considerably reduce the latency and bandwidth required to download images from a central registry. In addition, it acts as a proactive caching mechanism by optimizing the download delays and the network traffic. In this paper, we introduce an edge-enabled storage framework that incorporates a localized Docker registry. This framework aims to streamline the storage and distribution of container images, providing improved control, scalability, and optimized capabilities for edge deployment. Four demanding XR applications are employed as use cases to experiment with the proposed solution.

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来源期刊
Computing
Computing 工程技术-计算机:理论方法
CiteScore
8.20
自引率
2.70%
发文量
107
审稿时长
3 months
期刊介绍: Computing publishes original papers, short communications and surveys on all fields of computing. The contributions should be written in English and may be of theoretical or applied nature, the essential criteria are computational relevance and systematic foundation of results.
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