The vision of RAINBOW was to design and develop an open and trusted fog computing platform that facilitated the deployment and management of scalable, heterogeneous and secure IoT services and cross-cloud applications (i.e., microservices). RAINBOW fell within the bigger vision of delivering a platform enabling users to remotely control the infrastructure that is running, potentially, on hundreds of edge devices (e.g., wearables), thousands of fog nodes in a factory building or flying in the sky (e.g., drones), and millions of vehicles travelling in a certain area or across Europe. RAINBOW aspired to enable fog computing to reach its true potential by providing the deployment, orchestration, network fabric and data management for scalable and secure edge applications, addressing the need to timely process the ever-increasing amount of data continuously gathered from heterogeneous IoT devices and appliances. Our solution provided significant benefits for popular cloud platforms, fog middleware, and distributed data management engines, and extended the open-source ecosystem by pushing intelligence to the network edge while also ensuring security and privacy primitives across the device-fog-cloud-application stack. To evaluate its wide applicability, RAINBOW was demonstrated in various real world and demanding scenarios, such as automated manufacturing (Industry 4.0), connected vehicles and critical infrastructure surveillance with drones. These application areas are safety-critical and demanding; requiring guaranteed extra-functional properties, including real-time responsiveness, availability, data freshness, efficient data protection and management, energy-efficiency and industry-specific security standards.
The UNICORN project aimed at fostering the provision and adoption of competitive, innovative, secure and reliable Cloud computing services by SMEs through the development of a portable, collaborative and user-friendly Web-based IDE plugin (based on Eclipse Che) with desktop compatibility support to design, deploy, monitor and manage secure, elastic micro-services with emphasis on data privacy over multi-cloud programmable infrastructure (unikernels).
The unstoppable proliferation of novel computing and sensing device technologies, and the ever-growing demand for data-intensive applications in the edge and cloud, are driving a paradigm shift in computing around dynamic, intelligent and yet seamless interconnection of IoT, edge and cloud resources, in one single computing system to form a continuum. Many research initiatives have focused on deploying a sort of management plane intended to properly manage the continuum. Simultaneously, several solutions exist aimed at managing edge and cloud systems through not suitably addressing the whole continuum challenges though. The next step is, with no doubt, the design of an extended, open, secure, trustable, adaptable, technology agnostic and much more complete management strategy, covering the full continuum, i.e. IoT-to-edge-to-cloud, with a clear focus on the network connecting the whole stack, leveraging off-the-shell technologies (e.g. AI, data, etc.), but also open to accommodate novel services as technology progress goes on. The ICOS project aimed at covering the set of challenges coming up when addressing this continuum paradigm, proposing an approach embedding a well-defined set of functionalities, ending up in the definition of an IoT2cloud Operating System (ICOS). Indeed, the main objective of the project ICOS was to design, develop and validate a meta operating system for a continuum, by addressing the challenges of: i) devices volatility and heterogeneity, continuum infrastructure virtualization and diverse network connectivity; ii) optimized and scalable service execution and performance, as well as resources consumptions, including power consumption; iii) guaranteed trust, security and privacy, and; iv) reduction of integration costs and effective mitigation of cloud provider lock-in effects, in a data-driven system built upon the principles of openness, adaptability, data sharing and a future edge market scenario for services and data.
P2CODE envisioned the design and development of an open platform for the deployment and dynamic management of end user applications, over distributed, heterogeneous and trusted IoT-Edge node infrastructures, with enhanced programmability features and tools at both the network infrastructure level and the service design and operational level. The platform was implemented following three innovative design approaches: i) The deployment and management of the applications was conducted by an orchestration framework that followed a vertical layered approach from the end user interface to the infrastructure management while spanning horizontally across the device-edge-core-cloud continuum. The deployment followed the user-defined networking and operational features of the application in its northbound interface and a tight integration with state-of-the-art IoT, edge/cloud computing, and networking platforms in its southbound interface through a well-define driver API framework. With this approach the full programmability and reconfigurability of resources across the continuum was enabled. ii) An open and extensible, programming toolset facilitated application development and deployment for large swarms of devices at the edge through a multi-role Internal Developer Platform (IDP) and new feature development and testing, iii) A secure and trusted framework for registering and authenticating IoT device and edge nodes entering the system as well as the data sharing and application deployment. The concept was tested and validated over a mature testing environment that integrated diverse IoT application areas in smart logistics, manufacturing, utility inspection, and community PPDR over a programable infrastructure extended to O-RAN, 5G, SDN enable core Cloud. The consortium addressed all the required development sectors from the platform technology innovations, to supported IoT infrastructure and applications, including the end user interfacing and resource management intelligence.
The EOSC-hub project created the integration and management system of the future European Open Science Cloud that delivered a catalogue of services, software and data from the EGI Federation, EUDAT CDI, INDIGO-DataCloud and major research e-infrastructures. This integration and management system (the Hub) built on mature processes, policies and tools from the leading European federated e-Infrastructures to cover the whole life-cycle of services, from planning to delivery. The Hub aggregated services from local, regional and national e-Infrastructures in Europe, Africa, Asia, Canada and South America.
The Hub acted as a single contact point for researchers and innovators to discover, access, use and reuse a broad spectrum of resources for advanced data-driven research. Through the virtual access mechanism, more scientific communities and users had access to services supporting their scientific discovery and collaboration across disciplinary and geographical boundaries.
The project also improved skills and knowledge among researchers and service operators by delivering specialised trainings and by establishing competence centres to co-create solutions with the users. In the area of engagement with the private sector, the project created a Joint Digital Innovation Hub that stimulated an ecosystem of industry/SMEs, service providers and researchers to support business pilots, market take-up and commercial boost strategies.
EOSC-hub built on existing technology already at TRL 8 and addressed the need for interoperability by promoting the adoption of open standards and protocols. By mobilizing e-Infrastructures comprising more than 300 data centres worldwide and 18 pan-European infrastructures, this project was a ground-breaking milestone for the implementation of the European Open Science Cloud.
The main objective of CyclOps is to provide interoperable, trustworthy and secure automatic management, governance, and maintenance of the entire data life cycle for large-scale volumes of data generated in heterogeneous distributed sources to enable data sharing and exchange in data spaces.
Collecting and analysing large amounts of data in the Cloud-to-Edge computing continuum raises novel challenges. Processing all this data centrally in cloud data centres is not feasible anymore as transferring large amounts of data to the cloud is time-consuming, expensive, degrade performance and may raise security concerns. Therefore, novel distributed computing paradigms, such as edge and fog computing emerged to support processing data closer to its origin. However, such hyper-distributed systems require fundamentally new methods. To overcome the limitation of current centralised application management approaches, Swarmchestrate will develop a completely novel decentralised application-level orchestrator, based on the notion of self-organised interdependent Swarms. Application microservices are managed in a dynamic Orchestration Space by decentralised Orchestration Agents, governed by distributed intelligence that provides matchmaking between application requirements and resources, and supports the dynamic self-organisation of Swarms. Knowledge and trust, essential for the operation of the Orchestration Space, will be managed through blockchain-based trusted solutions using methods of Self-Sovereign Identities (SSI) and Distributed Identifiers (DID). End-to-end security of the overall system will be assured by utilising state-of-the-art cryptographic algorithms and privacy preserving data analytics. Due to the imminent complexity of the decentralised system, novel simulation approaches will be developed to test and optimise system behaviour (e.g., energy efficiency) in the early stages of development. Additionally, the simulator will be further extended into a digital twin running in parallel to the physical system and improving its behaviour with predictive feedback. The Swarmchestrate concept will be prototyped on four real life demonstrators from the areas of flood prevention, parking space management, urban noise classification and a digital twin of natural habitat.