IPCEI-CIS Reference Architecture

Why a Reference Architecture?

The content of this section has been created by the CISERO project, the original content can be accessed here: https://cisero-project.eu/ipcei-cis-reference-architecture

The digital future of Europe requires a cohesive and interoperable infrastructure, one that spans cloud and edge environments, integrates AI and data services, and ensures security, sustainability, and sovereignty across borders. To support this, the IPCEI-CIS Reference Architecture (ICRA) defines a common framework for designing, deploying, and operating cloud-edge systems in a federated, multi-provider landscape, representing a strategic instrument within the 8ra Initiative.

Layers

The content of this section has been created by the CISERO project, the original content can be accessed here: https://cisero-project.eu/ipcei-cis-reference-architecture

Application Layer
	Application Designer	This component enables developers to design, create and customize applications using intuitive interfaces or predefined templates. It facilitates rapid development, integration and delivery of tailored applications using automated CI/CD practices (DevOps). The Application Designer facilitates the description of the application in terms of: set of application components it is made of and how they are connected (service function chain), runtime environment each of the application components will require, including the set of functions/services to support its execution, the attributes that may allow the selection of the computing node to host it (hardware requirements, latency, privacy, etc. ).
	Application Packager	The Application Packager supports the packaging of applications for its deployment in the Cloud-Edge continuum. It facilitates the automation of application deployment and update (DevOps, both traditional and AI-assisted), providing an integrated toolkit that enables quick, secure and innovative ways to deploy cloud-aware applications. It also provides tools for automatic verification and validation (CV/CT) of the application and its supply chain before its final packaging.
	API Gateway	This component provides the interface to invoke and use the applications contained in the catalog. It checks the identity and authenticates the user and checks his authorization to use the application before providing access to it.
	Application Monitoring	It tracks application usage and execution, monitors the performance and identifies abnormal behavior and suboptimal use of resources.
	Application Catalog	It implements a directory of applications and functions that the providers have made available. They contain the characteristics of the application and the environment it requires for its execution (runtime, services, hardware characteristics).
	Application Accounting and Billing	This component implements the accounting of application usage and provides online charging information for the customer to track application expenditure in real-time.
Data Layer
	Data Pipelines	This component provides the functionality for data collection, including the connectors to integrate with the data sources and the capabilities for data curation and pre-processing that ensure its quality and readiness for analytics, insight generation, training, modelling or inferencing phases.
	Data Modelling	This component enables data cataloguing to enable exposure and discovery at scale to easily search, find and browse data, over a distributed environment.
	Data Exposure	The Data Exposure component provides customers with standard mechanisms and interfaces for safe and controlled access to data. It includes capabilities for making data offers and contract data acquisition, identity checking and data access authentication and authorization.
	Data Policy Control	Data Policy Control sets the required policies for data sharing, providing a safe, controlled and regulation-compliant environment for data exchange. It allows the data owner to manage the permissions to access its data: who can make it, at which conditions and for which purposes.
	Data Catalog	Data Catalog provides efficient storage and indexing of data to facilitate browsing, searching and finding data over a distributed environment.
	Data Federation	Data Federation enables standard mechanisms and interfaces (connectors) for partnering in the provision of datasets, providing a unified view of data catalogs and databases from multiple data providers. This component enables real-time data exchange across companies using data mesh principles, connecting distributed and heterogeneous actors over the cloud-edge continuum, keeping data owners in full control of their data. In order to create and maintain a coherent federated multi-provider Cloud Edge Continuum, Data Federation capabilities should be designed consistently with the other federation capabilities described in this document.
AI Layer
	Cloud-Edge Training	This component facilitates the dynamic and adjustable training of AI models across cloud and edge environments, ensuring scalability, reduced latency, and optimized resource utilization.
	Cloud-Edge Inference	The Inference components facilitate real-time deployment and execution of trained AI models on edge devices with efficient synchronization with the cloud for updates, monitoring, and enhancements.
	Cloud-Edge Agent Manager	The Cloud-Edge Agent Manager enables the deployment and management of agents and agentic workflows on edge and hybrid edge-cloud deployments creating an agentic mesh.
	AI Model Catalog	This component contains trained foundational models: LLMs, SLMs, multimodal LLMs, in multiple languages and managing multiple data types: text, images, video, code, etc. These models provide support for Natural Language Processing (NLP), Machine Translation (MT), speech processing, text analysis, information extraction, summarization or text and speech generation. They can be fine-tuned and adapted to specific use cases, using techniques like RAG, model quantization, pruning or distillation. The catalog contains multilingual and multimodal LLMs tailored to diverse EU languages, capable of understanding and processing diverse data types, including text, images, and multimedia. These models address the scarcity of generative AI solutions in non-English languages, ensuring semantic precision, completeness, and compliance with the AI Act.
	Federated Learning	AI workloads can be split across multiple nodes with central orchestration for scalability and efficiency (Distributed AI). AI Federation enables autonomous nodes to collaborate securely, ensuring privacy and sovereignty. Together, they balance task-sharing efficiency with autonomy. In distributed AI training, the AI model is generated at a central point based on the combination of models produced by different training agents distributed across a ecosystem of federated AI service providers or owners. The distributed training agents work locally on local datasets reducing the need to transfer data to a central location for training. This component allows to use and orchestrate AI resources across multiple providers to collaboratively perform a specific machine learning training task. It leverages a federated network of AI capabilities geographically distributed across the multi-provider Cloud Edge Continuum, enabling seamless resource sharing and scaling while maintaining sovereignty and compliance. It ensures efficient distribution of AI computational workloads, minimizes data movement, and facilitates parallel model training without requiring centralized data aggregation, thus preserving data privacy and autonomy while enhancing overall system performance. In order to create and maintain a coherent federated multi-provider Cloud Edge Continuum, Federated Learning capabilities should be designed consistently with the other federation capabilities described in this document.
	AI Explainability	This Explainable AI component ensures transparency by providing interpretable insights into AI decision-making processes. It supports compliance, accountability, and trust by enabling users and regulators to understand, audit, and validate AI models while respecting privacy and data sovereignty.
Service Orchestration
	Service Orchestrator	Service orchestration assures efficient tasks execution, load balancing and real-time operations. For example, it could communicate with the Multi-Cloud Orchestrator that manages the virtualized infrastructure layer offering a single unified environment for application development and monitoring. This allows applications and services to be deployed seamlessly across multiple platforms, optimizing resource allocation and reducing operational complexity. Alternatively, the Service Orchestrator may directly or indirectly interact with the underlying capabilities of the cloud platform or virtualization management layer to orchestrate workload execution. The Service Orchestrator automates application and tenant deployment, and lifecycle management processes. By automating workflows (or service function chains), orchestration ensures that services communicate efficiently across the cloud-edge continuum.
	Application Performance Management	It monitors the performance and resource consumption of the application or service and communicates deviations from set thresholds or SLAs to the Service Orchestrator for this to take actions to recover a state that meets application requirements. It provides a unified view of states, including logging, monitoring, and alerting, for effective real-time application management and validation at runtime.
	Application Repository	This component tracks the applications and services that have been deployed and their configuration, the locations where the application and service components are installed and the resources they are consuming.
	Service Federation	This component interconnects the Service Orchestrator with those of other federated providers, enabling the deployment and execution of applications (service function chains) across multiple providers in a seamless way, interacting with a single provider. In order to create and maintain a coherent federated multi-provider Cloud Edge Continuum, Service Federation capabilities should be designed consistently with the other federation capabilities described in this document.

Mapping of Horizon Europe EUCEI RIAs on the Reference Architecture

The table below provides a schematic mapping of Research and Innovation Actions, as related to the IPCEI-CIS Reference Architecture components.

	Programme	EuroVoc ID	IPCEI-CIS Reference Architecture high level
CODECO	Horizon Europe	/natural sciences/computer and information sciences/software	AI Layer Application layer Cloud Edge Platform Data layer Management Network Systems, SDN controllers Physical Cloud Edge Resources Security and compliance Service orchestration Sustainability Virtualization
COGNIFOG	Horizon Europe	/natural sciences/computer and information sciences/internet/internet of things	AI Layer Application layer Cloud Edge Platform Data layer Management Network Systems, SDN controllers Physical Cloud Edge Resources Security and compliance Service orchestration Sustainability Virtualization
EDGELESS	Horizon Europe	/natural sciences/computer and information sciences/internet	AI Layer Application layer Cloud Edge Platform Data layer Management Physical Cloud Edge Resources Physical Network Resources Security and compliance Service orchestration Sustainability Virtualization
HYPER-AI	Horizon Europe	/natural sciences/computer and information sciences/internet/internet of things	AI Layer Application layer Cloud Edge Platform Data layer Management Physical Cloud Edge Resources Physical Network Resources Security and compliance Service orchestration Sustainability Virtualization