面向分布式网络信息物理系统实验的云 continuum 研究基础设施
A Cloud Continuum Research Infrastructure for Distributed CPS Experimentation
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中文总结 AI 辅助
本文提出基于 SLICES 云 continuum 蓝图的两级参考架构，用于支持分布式 CPS 实验，通过可再生能源社区管理和 AirWatch 两个用例验证，可在同一可编程基础设施上部署、定制和比较不同控制与监控策略。
中文摘要 AI 辅助
云 continuum 应用需要能够结合异构边缘、雾、云以及高性能计算资源的实验环境，同时保持分布式部署的可复现性、可观测性和可控性。本文提出了一种基于 SLICES 云 continuum 蓝图的两级云 continuum 实验参考架构。该方法将暴露和管理分布式资源的研究基础设施层，与按照边缘-雾-云模式组织网络信息物理（Cyber-Physical）工作流的应用层分离，在该模式中，部署位置、时序和数据溯源被视为一级实验关注点。该架构设计用于支持多个 continuum 应用，而非单一领域特定原型：在边缘层，应用与物理设备交互并执行低延迟感知或安全动作；在雾层，它们执行近源协调、中介和流处理逻辑；在云层，它们通过分析、优化和可视化整合全局知识。这种划分使研究人员能够在同一可编程基础设施基板上部署、定制和比较不同的控制与监控策略。该方法通过两个代表性用例验证：一是可再生能源社区管理，需要分布式数字孪生协调和基于时间窗口的能源控制；二是 AirWatch，一种专注于异常检测、低延迟告警和云端聚合的监控管道。两个工作负载均通过在地理分布式基础设施上对虚拟化和物理边缘部署进行的 40 次运行的系统实验进行评估。
英文摘要
Cloud Continuum applications require experimental environments capable of combining heterogeneous Edge, Fog, Cloud, and high-performance computing resources while preserving reproducibility, observability, and control over distributed deployments. This paper presents a two-level reference architecture for Cloud Continuum experimentation built on top of the SLICES Cloud Continuum Blueprint. The proposed approach separates the research-infrastructure layer, which exposes and manages distributed resources, from the application layer, where Cyber-Physical workflows are organized according to an Edge-Fog-Cloud pattern in which placement, timing, and data provenance are treated as first-class experimental concerns. The architecture is designed to support multiple continuum applications rather than a single domain-specific prototype. At the Edge, applications interact with physical devices and perform low-latency sensing or safety actions; at the Fog, they execute near-source coordination, mediation, and stream-processing logic; at the Cloud, they consolidate global knowledge through analytics, optimization, and visualization. This partitioning enables researchers to deploy, customize, and compare alternative control and monitoring strategies over the same programmable infrastructure substrate. The approach is validated through two representative use cases: Renewable Energy Community management, where distributed Digital Twin coordination and time-window-based energy control are requested, and AirWatch, a monitoring pipeline focused on anomaly detection, low-latency alerting, and cloud-side aggregation. Both workloads are evaluated through a systematic campaign of 40 runs comparing virtualized and physical edge deployments over a geographically distributed infrastructure.
