EuroFMX (Foundational Models for Trustworthy Industrial Generative AI in Europe) is a Horizon Europe initiative developing sovereign and trustworthy generative AI for European manufacturing. The project builds physics-informed graph-based neural networks and industrial agentic systems that reason about factory data and manufacturing context, addressing the trustworthiness gaps of current AI in industrial settings. The consortium unites 72 partners across 20 European countries over 48 months.
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f-inference envisions shifting from centralized, cloud-based deployment of foundational models (FMs) towards a Resource-driven Computing Continuum (RCC) that spans Cloud, Edge, and IoT tiers, deploying intelligence as close as possible to data sources and end users. Leveraging Mixture of Experts (MoE), dynamic query routing, and early-exit and caching at the Edge, the RCC supports localized micro-inference that minimizes latency, energy, and cost while keeping sensitive data within each organisation's boundaries to strengthen digital sovereignty.
RADON aims at creating a DevOps framework to create and manage microservices-based applications that can optimally exploit serverless computing technologies. RADON applications will include fine-grained and independently deployable microservices that can efficiently exploit FaaS and container technologies. The end goal is to broaden the adoption of serverless computing technologies within the European software industry.
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DICE offers a novel UML profile and tools that will help software designers reasoning about reliability, safety and efficiency of Big Data applications. The DICE methodology covers quality assessment, architecture enhancement, continuous testing and agile delivery, relying on principles of the emerging DevOps paradigm.
The OptiMAM project focuses on the definition of novel algorithms based on matrix-analytix methods (MAM) to enable the optimisation of service-oriented architecture design and business processes. The project has a modelling scope and wants to deliver better methods for evaluating and optimising the design of workflows underpinning service-oriented architectures and business processes.
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The goal of SPANDO (Self-organising Performance Prediction and Optimisation for Large-scale Software Systems) is to contribute to the development of decentralised self-optimising software systems. The project focuses on the conceptual foundations and engineering techniques on the use of run-time performance prediction models and self-organising adaptation strategies to achieve a decentralised performance optimisation of the system
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iBids proposes to develop techniques and tools for the quantitative analysis and optimisation of multi-tiered data storage systems. The primary objective is to develop novel modelling approaches to define and facilitate the most appropriate data placement and data migration strategies. These strategies share the common aim of placing data on the most effective target device in a tiered storage architecture.
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The main goal of MODAClouds is to provide methods, a decision support system, an open source IDE and run-time environment for the high-level design, early prototyping, semi-automatic code generation, and automatic deployment of applications on multi-Clouds with guaranteed QoS.
