Technical Blueprint
Technical blueprint for a European Compute and Data Continuum
- Published
- May 2026
The Blueprint at a glance
Select a layer, a column or a capability to read about it, and follow its links to the SRIDA's priorities.
› Resource Federation
4.6 Resource Federation
Read about Resource Federation
A European Compute and Data Continuum must allow resources from different providers and domains to function as part of a single, coherent ecosystem. For HEP and RA, where workflows routinely span multiple facilities, this means going beyond isolated access models to enable true federation of access and allocation of continuum resources. Researchers should be able to use HPC, cloud, storage, and edge resources through a unified entry point, with allocations that can be honoured seamlessly across institutional and national boundaries.
Capabilities:
SRIDA priorities acting on Resource Federation (from SRIDA Annex C.4):P01 Cross-Infrastructure GovernanceP02 Federated Resource AllocationP03 Federated Identity & TrustP11 Community Collaboration & Co-DesignP13 Skills, Careers & Training
› › Federated Access & Allocation
Federated Access & Allocation
Read more in Resource Federation
Researchers use HPC, cloud, storage and edge resources through a unified entry point, with allocations honoured seamlessly across institutional and national boundaries.
SRIDA priorities acting on Resource Federation (from SRIDA Annex C.4):P01 Cross-Infrastructure GovernanceP02 Federated Resource AllocationP03 Federated Identity & TrustP11 Community Collaboration & Co-DesignP13 Skills, Careers & Training
› › Interoperability & Standards
Interoperability & Standards
Read more in Resource Federation
Interoperable standards for tasks such as job submission, data access and monitoring, so that resources from different providers and domains work as one coherent ecosystem.
SRIDA priorities acting on Resource Federation (from SRIDA Annex C.4):P01 Cross-Infrastructure GovernanceP02 Federated Resource AllocationP03 Federated Identity & TrustP11 Community Collaboration & Co-DesignP13 Skills, Careers & Training
› › Identity and Access Integration
Identity and Access Integration
Read more in Resource Federation
Federated identity and access management, building on eduGAIN and the WLCG's Virtual Organisations, giving single sign-on across facilities with role-based access, attribute aggregation and service accounts.
SRIDA priorities acting on Resource Federation (from SRIDA Annex C.4):P01 Cross-Infrastructure GovernanceP02 Federated Resource AllocationP03 Federated Identity & TrustP11 Community Collaboration & Co-DesignP13 Skills, Careers & Training
› › Accounting
Accounting
Read more in Resource Federation
Usage data such as CPU and GPU hours, storage consumption and data transfer volumes, collected and reported consistently across all sites, so that allocation is fair and funders and providers can verify contributions.
SRIDA priorities acting on Resource Federation (from SRIDA Annex C.4):P01 Cross-Infrastructure GovernanceP02 Federated Resource AllocationP03 Federated Identity & TrustP11 Community Collaboration & Co-DesignP13 Skills, Careers & Training
› Monitoring and Observability
4.7 Monitoring and Observability
Read about Monitoring and Observability
To operate a complex continuum, comprehensive monitoring and analytics are needed. The Monitoring and Observability block ensures that the European Compute and Data Continuum is not only performant and reliable, but also transparent, efficient, and environmentally responsible. For HEP and RA, which operate on multi-decadal timelines and exascale-scale infrastructures, observability and sustainability are not optional features but foundational requirements.
Capabilities:
SRIDA priorities acting on Monitoring and Observability (from SRIDA Annex C.4):P02 Federated Resource AllocationP05 Data Management & TransportP12 Environmental Sustainability
› › Infrastructure Monitoring
Infrastructure Monitoring
Read more in Monitoring and Observability
End-to-end monitoring of the health and performance of resources: compute utilisation, storage usage and errors, and data-centre sensors such as temperature, power and PUE, ingested in real time so that maintainers can intervene before a failure.
SRIDA priorities acting on Monitoring and Observability (from SRIDA Annex C.4):P02 Federated Resource AllocationP05 Data Management & TransportP12 Environmental Sustainability
› › Application Performance Monitoring
Application Performance Monitoring
Read more in Monitoring and Observability
Application-level monitoring that traces scientific workflows: each pipeline step emits logs and metrics such as job durations, queue wait times, I/O patterns and error counts; performance monitoring also supports billing and reporting.
SRIDA priorities acting on Monitoring and Observability (from SRIDA Annex C.4):P02 Federated Resource AllocationP05 Data Management & TransportP12 Environmental Sustainability
› › Operational Intelligence
Operational Intelligence
Read more in Monitoring and Observability
Monitoring data turned into actionable intelligence: anomaly detection, trend analysis and forecasting for capacity planning, and AI-driven predictive maintenance for round-the-clock operations such as telescopes and LHC detectors.
SRIDA priorities acting on Monitoring and Observability (from SRIDA Annex C.4):P02 Federated Resource AllocationP05 Data Management & TransportP12 Environmental Sustainability
› › Energy Monitoring
Energy Monitoring
Read more in Monitoring and Observability
Fine-grained monitoring of resource usage across compute, storage and data movement, with PUE, CUE and water use complemented by life-cycle assessment of environmental impact, to guide usage towards sufficiency.
SRIDA priorities acting on Monitoring and Observability (from SRIDA Annex C.4):P02 Federated Resource AllocationP05 Data Management & TransportP12 Environmental Sustainability
› Security and Trust
4.8 Security and Trust
The European Compute and Data Continuum will be a federated infrastructure spanning multiple countries, institutions, and administrative domains. In such an environment, trust and security are not add-ons but foundational enablers (e.g. AARC Blueprint Architecture). They ensure that (sensitive) data, compute resources, and workflows are protected while still enabling frictionless collaboration across organizational and national boundaries. Both HEP and RA rely heavily on global collaboration, but they have to comply with EU regulations on privacy, data protection, and cybersecurity.
Capabilities:
SRIDA priorities acting on Security and Trust (from SRIDA Annex C.4):P01 Cross-Infrastructure GovernanceP03 Federated Identity & Trust
› › Identity & Access Management
Identity & Access Management
Read more in Security and Trust
A federated IAM system governs access: researchers use their home institution credentials through identity federations such as eduGAIN, and attributes such as collaboration membership and VO roles travel with the identity. The SPECTRUM CoP survey found the tools split between Indigo-IAM, CERN-SSO and EGI Check-in.
SRIDA priorities acting on Security and Trust (from SRIDA Annex C.4):P01 Cross-Infrastructure GovernanceP03 Federated Identity & Trust
› › Logging
Logging
Read more in Security and Trust
Logged trails of data access, a technical safeguard for data protection, and traceability of actions to a unique and natural person, which many legal interpretations require and which still has to be solved for bulk workloads.
SRIDA priorities acting on Security and Trust (from SRIDA Annex C.4):P01 Cross-Infrastructure GovernanceP03 Federated Identity & Trust
› › Policy Enforcement
Policy Enforcement
Read more in Security and Trust
A trust framework that sets the criteria for participation and validates participants continuously, with policies enforced in real time, such as keeping a telescope's data in GDPR-compliant facilities in the EU or requiring multi-factor authentication for Tier-0 resources.
SRIDA priorities acting on Security and Trust (from SRIDA Annex C.4):P01 Cross-Infrastructure GovernanceP03 Federated Identity & Trust
› › Privacy & Data Protection
Privacy & Data Protection
Read more in Security and Trust
Most HEP and RA data are not personal, but some sensitive datasets will be processed: GDPR compliance, encryption at rest and in transit, privacy-preserving AI/ML techniques, and data kept within designated jurisdictions when required.
SRIDA priorities acting on Security and Trust (from SRIDA Annex C.4):P01 Cross-Infrastructure GovernanceP03 Federated Identity & Trust
› AI/ML and HPC Applications
4.5 AI/ML and HPC Applications
Read about AI/ML and HPC Applications
AI/ML and traditional HPC/HTC applications together form the scientific and computational backbone of modern data-intensive research. They drive new methods in simulation, reconstruction, calibration, analysis, and instrument operations, while simultaneously pushing the technological evolution of the continuum. Both classes of workloads benefit from access to large-scale compute, distributed data fabrics, and interoperable software environments, but they expose different performance characteristics that must be supported in parallel. Ensuring that these heterogeneous workloads can operate efficiently across HPC, HTC, cloud, and data-lake infrastructures is therefore a central requirement of the continuum.
Capabilities:
SRIDA priorities acting on AI/ML and HPC Applications (from SRIDA Annex C.4):P07 AI
› › Large-Scale Data Processing & Scientific Pipelines
Large-Scale Data Processing & Scientific Pipelines
Read more in AI/ML and HPC Applications
Domain pipelines that turn raw instrument output into calibrated, analysis-ready data (reconstruction, calibration, Monte Carlo production, data reduction), running on heterogeneous resources without per-site customisation.
SRIDA priorities acting on AI/ML and HPC Applications (from SRIDA Annex C.4):P07 AI
› › AI/ML platforms & Distributed Model Training
AI/ML platforms & Distributed Model Training
Read more in AI/ML and HPC Applications
Platforms to build, train, deploy and manage models: distributed, GPU-rich training across HPC, cloud and edge, scalable inference, model registries, and federated learning across distributed data.
SRIDA priorities acting on AI/ML and HPC Applications (from SRIDA Annex C.4):P07 AI
› › Advanced Analytics & Interactive Exploration
Advanced Analytics & Interactive Exploration
Read more in AI/ML and HPC Applications
Interactive environments such as Jupyter notebooks and portals linked to the data federation, where researchers explore large datasets and scale out from interactive tests to offline workflows, for example with Dask.
SRIDA priorities acting on AI/ML and HPC Applications (from SRIDA Annex C.4):P07 AI
› › Simulation frameworks & Digital Twins
Simulation frameworks & Digital Twins
Read more in AI/ML and HPC Applications
Domain simulation frameworks such as GEANT4 or N-body and fluid solvers, and digital twins that combine physics models, operational data and AI to represent instruments or detectors in near real time.
SRIDA priorities acting on AI/ML and HPC Applications (from SRIDA Annex C.4):P07 AI
› Orchestration and Workflows
4.4 Orchestration and Workflows
Read about Orchestration and Workflows
The European Compute and Data Continuum will span multiple facilities, resource types, and governance domains. To unlock its potential, scientific workflows must be orchestrated seamlessly across HPC, cloud, edge, and specialized instruments. This requires more than ad-hoc job scheduling; rather, it calls for an integrated orchestration layer that manages resources, tasks, and data flows coherently, while ensuring reproducibility and policy compliance. For both HEP and RA, which rely on large, distributed, multi-decade collaborations, orchestration is what transforms a federation of resources into a functioning continuum.
Capabilities:
SRIDA priorities acting on Orchestration and Workflows (from SRIDA Annex C.4):P04 Heterogeneous Computing IntegrationP06 Orchestration & Workflows
› › Workflow Management
Workflow Management
Read more in Orchestration and Workflows
Analyses described as workflows of dependent tasks and run end to end, with failed jobs retried or moved elsewhere; standards such as CWL and WDL are a baseline that must be extended to federated execution.
SRIDA priorities acting on Orchestration and Workflows (from SRIDA Annex C.4):P04 Heterogeneous Computing IntegrationP06 Orchestration & Workflows
› › Resource Orchestration
Resource Orchestration
Read more in Orchestration and Workflows
Compute, storage and network tasks allocated dynamically across facilities: expanding into the cloud when HPC queues are full, balancing load across sites, and scheduling close to the edge when latency is critical.
SRIDA priorities acting on Orchestration and Workflows (from SRIDA Annex C.4):P04 Heterogeneous Computing IntegrationP06 Orchestration & Workflows
› › Task Scheduling
Task Scheduling
Read more in Orchestration and Workflows
Within workflows, tasks scheduled against hardware requirements, data locality and deadlines: a GPU-intensive training job goes to accelerator nodes, while a transient detection task may have to run at the edge within milliseconds.
SRIDA priorities acting on Orchestration and Workflows (from SRIDA Annex C.4):P04 Heterogeneous Computing IntegrationP06 Orchestration & Workflows
› › Provenance
Provenance
Read more in Orchestration and Workflows
Provenance metadata recorded automatically for each workflow step, and lineage kept as data move and transform, so that results can be reproduced and the FAIR principles are supported.
SRIDA priorities acting on Orchestration and Workflows (from SRIDA Annex C.4):P04 Heterogeneous Computing IntegrationP06 Orchestration & Workflows
› Software Distribution and Execution
4.3 Software Distribution and Execution
Read about Software Distribution and Execution
The software, compiler, and library ecosystem is the critical abstraction layer that bridges scientific applications with the heterogeneous resources of the Compute and Data Continuum. While the Compute and Data resources provide the raw infrastructure, it is the software stack that makes these resources usable, and enables portable, and reproducible workflows for global collaborations. For communities such as HEP and RA, whose workflows must run consistently across hundreds of sites and over decades, a robust and harmonized software and execution environment is indispensable.
Capabilities:
SRIDA priorities acting on Software Distribution and Execution (from SRIDA Annex C.4):P08 Software PortabilityP09 Open Science & ReproducibilityP10 Software Preservation & Sustainability
› › Software, Libraries & Compilers
Software, Libraries & Compilers
Read more in Software Distribution and Execution
Software and compiler registries that catalogue validated applications, frameworks and libraries, with metadata on authorship, licensing and hardware compatibility.
SRIDA priorities acting on Software Distribution and Execution (from SRIDA Annex C.4):P08 Software PortabilityP09 Open Science & ReproducibilityP10 Software Preservation & Sustainability
› › Container Registry
Container Registry
Read more in Software Distribution and Execution
Container registries that store portable images (Docker, Apptainer), so that scientists can package complex environments and run them reproducibly across HPC, cloud and edge.
SRIDA priorities acting on Software Distribution and Execution (from SRIDA Annex C.4):P08 Software PortabilityP09 Open Science & ReproducibilityP10 Software Preservation & Sustainability
› › Virtual File System
Virtual File System
Read more in Software Distribution and Execution
Read-only virtual file systems such as CVMFS that distribute versioned software environments to thousands of nodes, a model already proven in WLCG and at the Vera C. Rubin Observatory.
SRIDA priorities acting on Software Distribution and Execution (from SRIDA Annex C.4):P08 Software PortabilityP09 Open Science & ReproducibilityP10 Software Preservation & Sustainability
› › Runtime Environment Management
Runtime Environment Management
Read more in Software Distribution and Execution
Runtime configuration across heterogeneous systems: environment modules, dependency resolution, support for x86, ARM, RISC-V and GPUs, and software stacks requested declaratively through stable APIs, with EasyBuild and EESSI as the European HPC references.
SRIDA priorities acting on Software Distribution and Execution (from SRIDA Annex C.4):P08 Software PortabilityP09 Open Science & ReproducibilityP10 Software Preservation & Sustainability
› Compute Resources
4.1 Compute Resources
The Compute Resources capability is a foundational layer of the European Compute and Data Continuum, providing seamless access to heterogeneous computational resources distributed across HPC centers, community and commercial clouds, edge infrastructures, and emerging quantum platforms. For data-intensive domains such as HEP and RA, the Compute Continuum transforms a fragmented ecosystem of sites into a federated, performance-optimized environment that can deliver exascale throughput.
Capabilities:
SRIDA priorities acting on Compute Resources (from SRIDA Annex C.4):P04 Heterogeneous Computing IntegrationP06 Orchestration & WorkflowsP07 AI
› › HPC
HPC
Read more in Compute Resources
Federated HPC centres reached through standardised interfaces, bridging local batch schedulers such as Slurm with the experiments' global workload managers, and using accelerators such as GPUs transparently.
SRIDA priorities acting on Compute Resources (from SRIDA Annex C.4):P04 Heterogeneous Computing IntegrationP06 Orchestration & WorkflowsP07 AI
› › Cloud Computing
Cloud Computing
Read more in Compute Resources
Cloud capacity as a complementary pathway, elastic in public clouds and more queue-like in research clouds; federated cloud stacks such as the EGI Federated Cloud let workflows start the environments they need where they need them.
SRIDA priorities acting on Compute Resources (from SRIDA Annex C.4):P04 Heterogeneous Computing IntegrationP06 Orchestration & WorkflowsP07 AI
› › Real-Time & Low-Latency Computing
Real-Time & Low-Latency Computing
Read more in Compute Resources
Edge nodes at observatories and colliders run real-time pipelines that filter, compress or detect transients before data leave the site; LOFAR and the SKAO produce raw streams of 10 to 100 Tb/s.
SRIDA priorities acting on Compute Resources (from SRIDA Annex C.4):P04 Heterogeneous Computing IntegrationP06 Orchestration & WorkflowsP07 AI
› › Quantum Computing
Quantum Computing
Read more in Compute Resources
A forward-looking interoperability layer that keeps the door open to quantum and hybrid quantum-classical workflows without disrupting established pipelines, with the emphasis on readiness rather than deployment.
SRIDA priorities acting on Compute Resources (from SRIDA Annex C.4):P04 Heterogeneous Computing IntegrationP06 Orchestration & WorkflowsP07 AI
› Data Resources
4.2 Data Resources
Scientific data in HEP and RA are massive, distributed and federated. The Data Continuum is a federated infrastructure that enables scientific data to be stored, moved, discovered, and preserved across geographically distributed and technologically heterogeneous sites. For data-intensive disciplines such as HEP and RA, where exabyte-scale datasets are generated and shared globally, the Data Continuum is not a peripheral service but a central enabler of scientific discovery. It transforms fragmented storage silos into a seamless, policy-compliant, and performance-optimized ecosystem.
Capabilities:
SRIDA priorities acting on Data Resources (from SRIDA Annex C.4):P05 Data Management & TransportP09 Open Science & Reproducibility
› › Storage
Storage
Distributed storage across many sites presented as one namespace, with replication, integrity checks and automated repair; ATLAS and CMS manage more than an exabyte across over 120 centres this way with Rucio.
SRIDA priorities acting on Data Resources (from SRIDA Annex C.4):P05 Data Management & TransportP09 Open Science & Reproducibility
› › Data Transfer
Data Transfer
High-performance data movement over research networks such as GÉANT, LHCOPN and LHCONE, with bulk scheduling and automatic retries; LHC experiments transfer tens of petabytes a day.
SRIDA priorities acting on Data Resources (from SRIDA Annex C.4):P05 Data Management & TransportP09 Open Science & Reproducibility
› › Data Discovery & Cataloging
Data Discovery & Cataloging
Metadata catalogues and registries that make data, software included, findable, with rich standardised metadata and provenance: Rucio in HEP, and the LOFAR long-term archive and IVOA standards in radio astronomy.
SRIDA priorities acting on Data Resources (from SRIDA Annex C.4):P05 Data Management & TransportP09 Open Science & Reproducibility
› › Data Lifecycle Management
Data Lifecycle Management
Policies that retain, move, expire and preserve data over decades across storage tiers, with integrity checks and curation metadata; the ATLAS Data Carousel, for example, stages data from tape to disk for scheduled analyses and removes it until the next reprocessing.
SRIDA priorities acting on Data Resources (from SRIDA Annex C.4):P05 Data Management & TransportP09 Open Science & Reproducibility
About the Technical Blueprint
The Technical Blueprint (D6.1) sets out the architecture of a European compute and data continuum for data-intensive science. It starts from High Energy Physics (the HL-LHC) and Radio Astronomy (the SKA Observatory and LOFAR), whose data volumes and workflows need high-performance computing, cloud platforms, edge devices and large-scale data services that work together and are easy for researchers to use. Other data-intensive sciences with similar needs can apply the same design.
Its thirty-two capabilities, in the map above, sit in eight groups: five layers, from compute and data resources up to AI/ML and HPC applications, and three columns that run across them for resource federation, monitoring and observability, and security and trust.
Many of these capabilities exist already. The gaps are inconsistent interfaces between infrastructures, uneven provenance, fragmented trust and security, and long-term sustainability: environmental impact, operational funding and skills. Its recommendations include harmonised interfaces and standards, closer co-design between infrastructures and major scientific projects, cross-facility workflow orchestration, a federated trust and security framework, and AI/ML as a core capability.
The Blueprint draws on the Community of Practice, public surveys, expert interviews, the analysis of use cases (D5.1) and the review of existing infrastructures (D5.3). The SRIDA builds on it to set priorities and a roadmap.