自动生成部署描述符,用于管理云到边缘连续体中基于微服务的应用程序

IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
James DesLauriers , Jozsef Kovacs , Tamas Kiss , André Stork , Sebastian Pena Serna , Amjad Ullah
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引用次数: 0

摘要

随着在网络边缘收集大量数据的物联网(IoT)设备的出现,跨越云、雾和边缘计算资源的新一代超分布式应用正在兴起。此类应用的自动部署和管理需要协调工具,将部署描述符(如 Kubernetes 清单、Helm 图表或 TOSCA)作为输入,并在运行时部署和管理应用程序的执行。虽然大多数部署描述符都是由单个人员或组织在某个特定时间准备的,但在一些值得注意的场景中,这些描述符需要由不同角色或组织在应用程序生命周期的不同时间协作创建。数字孪生的模块化开发就是这种情况的一个例子,数字孪生由数据、模型和算法等基本构件组成。其中的每一个构件都可以由不同的个人或公司在不同的时间独立创建。这里的挑战是如何从这些单独的组件中自动组成和构建部署描述符。本文提出了一种新颖的解决方案,可在云到边缘的连续统一体中为分布式应用程序自动协同组合和生成部署描述符。该解决方案已在 DIGITbrain 项目的超过 25 个工业用例中进行了原型验证,本文将以其中一个用例为代表进行介绍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated generation of deployment descriptors for managing microservices-based applications in the cloud to edge continuum
With the emergence of Internet of Things (IoT) devices collecting large amounts of data at the edges of the network, a new generation of hyper-distributed applications is emerging, spanning cloud, fog, and edge computing resources. The automated deployment and management of such applications requires orchestration tools that take a deployment descriptor (e.g. Kubernetes manifest, Helm chart or TOSCA) as input, and deploy and manage the execution of applications at run-time. While most deployment descriptors are prepared by a single person or organisation at one specific time, there are notable scenarios where such descriptors need to be created collaboratively by different roles or organisations, and at different times of the application’s life cycle. An example of this scenario is the modular development of digital twins, composed of the basic building blocks of data, model and algorithm. Each of these building blocks can be created independently from each other, by different individuals or companies, at different times. The challenge here is to compose and build a deployment descriptor from these individual components automatically. This paper presents a novel solution to automate the collaborative composition and generation of deployment descriptors for distributed applications within the cloud-to-edge continuum. The implemented solution has been prototyped in over 25 industrial use cases within the DIGITbrain project, one of which is described in the paper as a representative example.
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来源期刊
CiteScore
19.90
自引率
2.70%
发文量
376
审稿时长
10.6 months
期刊介绍: Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.
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