Manufacturing in Europe should urgently address the following challenges towards the adoption of the Manufacturing as a Service paradigm: product customisation, circularity and sustainability, minimal downtime, predictive maintenance, seamless communication, reliability and robustness to uncertainties in demand and variability of resources, and cost reduction and performance optimisation. Unified Modeling and Automated Scheduling for Manufacturing as a Service (UniMaaS) project will develop a platform with a set of advanced technologies for offering flexible and decentralized manufacturing resources and supply chains as a Service to European SMEs and Industries. To realize this vision, the proposed platform will deploy 3 integrated Suites: (i) Data Modelling Suite: offering manufacturing dataspaces, cloud-based resource monitoring, trusted cross-company data exchange and digital product passport, (ii) Modelling Suite: offering modular modeling of manufacturing resources, intent-based servitization , AI-based estimations and Zero-X analysis, and (iii) Decision-Making Suite: offering scheduling and planning of manufacturing resources, circularity and sustainability optimization. The platform will adopt advanced digital technologies such as cloud computing, digital twins and trustworthy AI, as key enablers towards fulfilling its objectives. UniMaaS platform will provide easy access to customized manufacturing service chain and will support the entire life-cycle management of processes and products. The UniMaaS Suites and the Platform will be demonstrated in 4 Pilots including aircraft maintenance, automotive seating, 3D construction printing and logistics/warehouse management.

MANOLO will deliver a complete stack of trustworthy algorithms and tools to help AI systems reach better efficiency and seamless optimization in their operations, resources and data required to train, deploy and run high-quality and lighter AI models in both centralised and cloud-edge distributed environments. It will push the state of the art in the development of a collection of complementary algorithms for training, understanding, compressing and optimising machine learning models by advancing research in the areas of: model compression, meta-learning (few-shot learning), domain adaptation, frugal neural network search and growth and neuromorphic models. Novel dynamic algorithms for data/energy efficient and policy-compliance allocation of AI tasks to assets and resources in the cloud-edge continuum will be designed, allowing for trustworthy widespread deployment. To support these activities a data management framework for distributed tracking of assets and their provenance (data, models, algorithms) and a benchmark system to monitor, evaluate and compare new AI algorithms and model deployments will be developed. Trustworthiness evaluation mechanisms will be embedded at its core for explainability, robustness and security of models while using the Z-Inspection methodology for TrustworthyAI assesment, helping AI systems conform to the new AI Act regulation. MANOLO will be deployed as a toolset and tested in lab environments via Use Cases with different distributed AI paradigms within cloud-edge continuum settings; it will be validated in verticals such as health, manufacturing, and telecommunications aligned with ADRA identified market opportunities, and with a granular set of embedded devices covering robotics, smartphones, IoT as well as using Neuromorphic chips. MANOLO will integrate with ongoing projects at EU level developing the next operating system for cloud-edge continuum, while promoting its sustainability via the AI-on-demand platform and EU portals.

The ability to integrate data from multiple sources is nowadays a major competitive advantage for organizations. Data-driven applications using AI techniques are reshaping various industries such as manufacturing, tourism, and mobility. The European Strategy for Data aims to create a single market for data while ensuring Europe's global competitiveness and data sovereignty. This has led to the development of Common European data spaces, yet the governance of the data life cycle in organizations has not kept up with the rapid technology evolution and remains largely manual. This is especially evident in scenarios where tens or hundreds of continuously evolving data sources produce semi-structured data, and create significant challenges when governed manually, causing organizations to end up with data silos. A systematic and standardized mechanism is needed to ingest, integrate, and process data, thus boosting the ability to develop new data-centric business models. However, current research and development efforts typically target one aspect of the end-to-end data lifecycle, such as scalable data management, ML performance, AI explainability, or sharing, while dismissing its governance. To overcome this limitation, CyclOps proposes a new framework for the governance and maintenance of the complete data lifecycle for large-scale volumes of data generated in heterogeneous distributed sources to enable data sharing and exchange. CyclOps intelligently automates, by means of knowledge graphs (KGs) and with a human-in-the-loop approach, the generation and execution of data processing pipelines. KGs are the established formal models to represent data and metadata while providing context and guaranteeing interoperability with other systems adhering to the FAIR Guiding Principles. CyclOps will enable organizations to seamlessly provide, cross and analyze machine- and human-generated data from and for data spaces, thus facilitating the provision of added-value services on top.

ENACT develops cutting-edge techniques and technology solutions to realise a Cognitive Computing Continuum (CCC) that can address the needs for optimal (edge and Cloud) resource management and dynamic scaling, elasticity, and portability of hyper-distributed data-intensive applications. At infrastructure level, the project brings visibility to distributed edge and Cloud resources by developing Dynamic Graph Models capable of capturing and visualising the real-time and historic status information, connectivity types, dependencies, energy consumption etc. from diverse edge and Cloud resources. The graph models are used by AI (Graph Neural Networks - GNN) models and Deep Reinforcement Learning (DRL) agents to suggest the optimal deployment configurations for hyper distributed applications considering their specific needs. The AI (GNN and DRL) models are packaged as an intelligent decision-making engine that can replace the scheduling component of open-source solutions such as KubeEdge. This will enable real-time and predictive management of distributed infrastructure and applications. To take full advantage of the potential (compute, storage, energy efficiency etc) opportunities in the CCC, ENACT will develop an innovative Application Programming Model (APM). The APM will support the development of distributed platform agnostic applications, capable of self-determining their optimal deployment and optimal execution configurations while taking advantage of diverse resources in the CCC. An SDK to develop APM-based distributed applications will be developed. Moreover, services for automatic (zero-touch provisioning-based) resource configuration and (telemetry) data collections are developed to help design and update dynamic graph models. ENACT CCC solutions will be validated in 3 use-cases with challenging resource and application requirements. International collaboration is planned as Japan Productivity Center has committed to support with knowledge sharing.

Service-based architecture in the 5G core network, disaggregated RAN, and network slicing are a few of the 5G network's innovative paradigms. There are however new requirements for 6G, such as the efficient use of integrated cloud resources, end-to-end network programmability, and the dynamic integration of multiple connectivity domains to realize intelligent connectivity across increasingly pervasive cloud environments. The 6G-Cloud project will research, develop, and validate key technologies to realize an artificial intelligence (AI)-native and cloud-friendly system architecture atop the cloud continuum. It will integrate cloud resources offered by multiple stakeholders and allow network functions from different 6G network segments to be composed flexibly and dynamically based on service needs in hybrid cloud environments. 6G-Cloud will address the following key topics: i) designing a holistic, AI-native service-oriented 6G system architecture atop a cloud continuum; ii) developing AI-driven cloud continuum and management frameworks and business interfaces for a multistakeholder environment; iii) defining an AI/machine learning (ML) framework for native-AI support in the 6G system; iv) supporting the 6G "network-of-networks" concept using service-oriented network design. 6G-Cloud will incorporate scalability, sustainability, resilience, and security requirements into system design. The concept will be validated by three well-defined proofs-of-concept and promoted through 6G architectural standardization work and open-source projects to reach maximum impact.