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Status

WIP

Stakeholders
Outcome
Due Date

 

Owner

Marie Goavec

Solution/Domain/Data Architect

OLIVEIRA, Tiago


Target Architecture: Logical Model

WiP

Target Architecture: Domain Boundaries

WiP

Pillar & Principles

  1. Secure by design: Architected to meet strict regulatory, ITAR, and compliance standards, embedding security and data isolation into every component—from lab to cloud.
  2. Globally distributed: Enables distributed collaboration while respecting data sovereignty and regulatory zones, with secure interconnects optimized for compliance and performance.
  3. High Interoperability: Designed to connect specialized lab instruments, simulation tools, and enterprise platforms across heterogeneous environments—securely and without friction.
  4. Cost Efficient & Scalable: Scales elastically to support burst compute, large simulations, and AI workflows while optimizing cost through dynamic resource allocation and tiered data strategies.
  5. Unified UX: Offers a seamless, role-aware user experience—from lab scientists to HPC engineers—ensuring consistent access, visualization, and orchestration across environments.
  6. Data Backbone (Data-Centric by Design): Establishes a governed, high-throughput data layer that ensures seamless access and movement of data across the platform — enabling consistent ingestion, transformation, AI-driven learning, synthetic data generation, and simulation workflows.
  7. Sustainability-Aligned Computing (Green by Architecture): Leverages cloud elasticity, workload-based optimization, and infrastructure modernization to reduce energy usage, eliminate underutilized on-prem resources, and support sustainability goals through measurable carbon footprint reduction.
  8. Quantum-Ready Architecture (Future-Proof by Design): Lays the foundation for seamless integration with quantum computing by enabling hybrid classical-quantum workflows, simulator access, and modular expansion paths — ensuring the HPC platform evolves in parallel with emerging computing paradigms.

Utility Tree

The Utility Tree brings a view on the most most architecturally significant requirements. With this tool, the business capabilities are assessed in terms of importance for the business and architecturally complexity for being accomplish, so a value engineering can be performed and strategical decisions been taken.

Data Backbone

  1. Main Capabilities
    1. Unified data flow from lab systems, external feeds, and internal platforms.
    2. Interoperable data access across AI engines, ETL/ELT pipelines, and HPC simulations.
    3. Data discoverability and traceability, which is critical for regulated environments.
    4. Performance and low-latency delivery for high-speed compute engines and powerful user workstations.
    5. Security and policy enforcement embedded into the data fabric to meet ITAR and compliance constraints.
  2. Architectural approaches
    1. Batch processing:
    2. Streaming processing:
    3. Event-driven: 
    4. Rest API - Request-Response:
    5. ETL: 

Data Flows

Following a list of the main pair-wise data flows: 

  • (Instrument data → LabPC/PiBox) → Cloud Data Storage
  • Instrument data → LIMS
  • Instrument data → ELN
  • Human Enhancement → LIMS
  • Human Enhancement → ELN
  • LIMS → Cloud Data Storage
  • ELN → Cloud Data Storage
  • Industrial → Cloud Data Storage
  • ERP/Customer Mngt → Cloud Data Storage
  • Modeling Simulation ↔ Cloud Data Storage
  • Modeling Simulation → HPC
  • Data Processing ↔ Gen AI
  • ...


Enablers, Future proof


Gen AI

TBD: collect LLM/GenAI use cases from the Study


Utility 


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