
This deliverable presents the NOUS Reference Architecture and Open-Source Development Platform, capturing the results of the architectural design activities carried out within the project. The document describes the key architectural layers, building blocks, and interactions that collectively enable the NOUS ecosystem, including data management, infrastructure provisioning, application orchestration, and user-facing services. The core of the NOUS architecture is structured around the Data Space Participant (DSP) and the Data Space Infrastructure Participant (DSIP), which together enable the seamless integration of data, compute, and edge resources. The NOUS Data Agent (NDA) supports secure data exchange, data orchestration, and application management, while the NOUS Infrastructure Agent (NIA) provides infrastructure discovery, provisioning, orchestration, and networking capabilities. These components are complemented by Data Space Common Services, which ensure identity management, authorization, governance, interoperability, and service discovery across the ecosystem. In addition to the architectural description, this deliverable outlines the open-source development platform supporting NOUS, detailing how architectural components are instantiated, integrated, and deployed. The document also presents example instantiations of the reference architecture, demonstrating how NOUS can be applied in practical scenarios involving distributed data processing, AI workload execution, and cross-domain collaboration. Overall, this deliverable provides a comprehensive and coherent view of the NOUS architecture, serving as a technical reference for project partners, developers, and stakeholders, and laying the foundation for future implementation, validation, and exploitation activities.
Policy-Aware Resource Discovery, Federated Infrastructure, Data Sovereignty, Data Spaces, Cloud–Edge Continuum, Federated Learning
Policy-Aware Resource Discovery, Federated Infrastructure, Data Sovereignty, Data Spaces, Cloud–Edge Continuum, Federated Learning
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