Model-Based DevOps

基于模型的 DevOps

• 批准号: 505496753
• 负责人: Professor Dr. Bernhard Rumpe
• 金额: --
• 依托单位: Institut für Steuerungstechnik der Werkzeugmaschinen und Fertigungseinrichtungen (ISW)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/505496753?language=en
• 关键词: Model; Based; DevOps

Time-to-market and continuous improvement are key success indicators to deliver the cloud-native and Internet of Things (IoT) systems, which includes the IoT part of Cyber-Physical Systems (CPSs) as well. These systems are essential to the European Commission's vision on Industry 5.0, i.e., a sustainable, human-centric, and resilient Industry 4.0. In that context, the increasingly popular DevOps approach aims at combining software development ("Dev") and IT operations ("Ops") into a highly-integrated continuous loop. Through agility and automation of complex pipelines across the various stakeholders of the lifecycle, DevOps aims to shorten systems development time and provide continuous delivery with high software quality. Through this continuous delivery, quality, sustainability, and resiliency of IoT systems can be continuously improved. Automation in DevOps is enabled by leveraging multiple models, typically expressed in Domain-Specific Languages or - most often - implicitly using JSON, XML, or ad-hoc annotations in a programming language. These models are used for (M1) design-time code generation and (M2) for runtime IoT system configuration They can and should be updated in four stages of the lifecycle of a system: (U1) automatically immediately (self-adaptive, based on data and events) during the system’s operation; (U1b) automatically, but with some delay, based on data collection, needs recalculation or training and especially quality assurance; (U2) by human operators; and (U3) by human system designers only. The updates U1-U3 change M2 models, while U4 changes M1 models. Self-adaptive (U1, U1b), human-adaptable (U2) and design models (U3) are rather disjoint in their usage, but overlap in the targets they describe: Design models describe requirements, structure, functional, and extra-functional concerns, while runtime models focus on component configurations, deployment, and on measures in operations. Self-adaptive models (U1, U1b) are linked to collecting data that enables the system to reflect on its own behavior and identify possible optimizations, additional detailed knowledge about usage, etc. Self-adaptive models (U1, U1b) and also human-adaptable models (U2) will be present for adaptation in a digital twin of the system, while the structurally fixed design models (U3) also define the structure of a digital twin These models today are separated and can either be used during design or runtime. This hinders optimizing represented systems, learning from their operation, and improving future iterations of it. There currently is no way to migrate a model from design to runtime or vice versa. Hence, improvements made during system runtime are lost or must be migrated to the design models manually, which is costly, error-prone, and, thus, rarely done. Our goal in MBDO is to provide the foundations for a Model-Based DevOps framework unifying these different forms of models in the specific context of cloud-native and IoT systems.

上市时间和持续改进是交付云原生和物联网（IoT）系统的关键成功指标，其中也包括网络物理系统（CPS）的物联网部分。这些系统对于欧盟委员会关于工业5.0的愿景至关重要，即，一个可持续的、以人为本的、有弹性的工业4.0。在这种情况下，越来越流行的DevOps方法旨在将软件开发（“Dev”）和IT运营（“Ops”）结合到高度集成的连续循环中。通过在生命周期的各个利益相关者之间实现复杂管道的敏捷性和自动化，DevOps旨在缩短系统开发时间，并提供高软件质量的持续交付。通过这种持续交付，物联网系统的质量、可持续性和弹性可以不断提高。DevOps中的自动化是通过利用多个模型来实现的，这些模型通常用特定于领域的语言来表达，或者最常见的是隐式地使用JSON、XML或编程语言中的ad-hoc注释。这些模型用于（M1）设计时代码生成和（M2）运行时物联网系统配置它们可以并且应该在系统生命周期的四个阶段进行更新：（U1）立即自动（自适应，基于数据和事件）在系统运行期间;（U1 b）自动地，但有一些延迟，基于数据收集，需要重新计算或培训，特别是质量保证;（U2）由人工操作员;（U3）仅由人类系统设计者设计。更新U1-U3改变M2模型，而U4改变M1模型。自适应模型（U1，U1 b），人类自适应模型（U2）和设计模型（U3）在它们的使用上是相当不相交的，但在它们描述的目标上是重叠的：设计模型描述需求，结构，功能和功能外的关注点，而运行时模型则关注组件配置，部署和操作中的度量。自适应模型（U1、U1 b）被链接到收集数据，该数据使得系统能够反映其自身的行为并识别可能的优化、关于使用的附加详细知识等。自适应模型（U1、U1 b）以及人类可适应模型（U2）将存在以用于在系统的数字孪生中进行适应，而结构固定的设计模型（U3）也定义了数字孪生的结构， 今天，这些模型是分开的，可以在设计或运行时使用。这阻碍了优化所表示的系统，从它们的操作中学习，以及改进未来的迭代。目前还没有办法将模型从设计迁移到运行时，反之亦然。因此，在系统运行时所做的改进会丢失，或者必须手动迁移到设计模型中，这是昂贵的，容易出错的，因此很少这样做。我们在MBDO中的目标是为基于模型的DevOps框架提供基础，在云原生和物联网系统的特定上下文中统一这些不同形式的模型。