基于模型的DevOps架构：用于DEVS数字孪生仿真服务

中文摘要 AI 辅助

数字孪生仿真模型像软件一样被演进和重新部署，然而基于DEVS的引擎提供了坚实的正式基础，但对版本控制、自动化验证或云原生环境中的持续交付支持甚少，导致在大多数部署中模型生命周期管理是临时性的。本文提出了一种以模型为中心的DevOps架构，用于将基于DEVS的数字孪生仿真部署为托管服务。仿真模型被视为一流的DevOps工件，以声明式YAML语言定义，并具有到multiPDEVS的正式映射，支持在CI/CD流水线中进行结构和语义验证，该流水线生成不可变的版本化工件，从而将回滚到先前已验证版本简化为固定其标识符。该平台被分解为Kubernetes上的容器化微服务，并针对状态外部化和生命周期控制进行了引擎适配。一项关于里加22号公路公共交通走廊的初步案例研究，这是拉脱维亚里加市计划中的全市多模式交通数字孪生的首次实例化，演练了完整生命周期，并报告了包含约47,870个DEVS原子组件场景的单容器引擎吞吐量；流水线级别的捕获统计和集群级别的并发多场景执行是配套实证研究的主题。

英文摘要

Digital twin simulation models are evolved and redeployed like software, yet DEVS-based engines offer a sound formal basis with little support for versioning, automated validation, or continuous delivery in cloud-native environments, leaving model lifecycle management ad hoc in most deployments. This paper proposes a model-centric DevOps architecture for deploying DEVS-based digital twin simulations as managed services. Simulation models are treated as first-class DevOps artefacts defined in a declarative YAML language with a formal mapping to multiPDEVS, supporting structural and semantic validation in a CI/CD pipeline that produces immutable versioned artefacts, so that reverting to an earlier validated version reduces to pinning its identifier. The platform is decomposed into containerised microservices on Kubernetes, with engine adaptations for state externalisation and lifecycle control. An initial case study on the Riga Route 22 public-transit corridor, the first instantiation of a planned city-wide multi-modal transport digital twin for Riga, Latvia, exercises the full lifecycle and reports single-container engine throughput for a scenario with roughly 47,870 DEVS atomic components; pipeline-level catch statistics and cluster-level concurrent multi-scenario execution are the subject of companion empirical studies.

发表机构

• Riga Technical University（里加技术大学）
• Dati Group Ltd.（Dati集团有限公司）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

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Submitted to Software and Systems Modeling (Springer). Under review