The ever-growing resource needs of modern-day applications regarding guaranteed low latency and the massive data transfer rate are constantly pushing the boundaries of technologies and requiring a paradigm shift. To cater for these escalating resource needs, modern IT computing platforms have evolved beyond the more traditional central cloud/DC with bleeding-edge processing powers and high-capacity networking infrastructure to extend their coverage all the way to the network edge, which may also include the far-edge nowadays. This creates a new paradigm called cloud edge computing continuum (CECC), whereby the services span from core cloud to edge and far edge. To efficiently manage and continuously optimize resources through this new model using the CECC approach, we propose an Agile and Cognitive Cloud-edge Continuum (AC3) management framework. This framework will play a critical role in providing scalability, agility, effectiveness, and dynamicity in service delivery over the CECC infrastructure. AC3 will offer a common and secure federated platform that manages data source, CECC resources, and application behaviour in a unified and harmonized manner to ensure the desired SLA and save energy consumption. Moreover, the AC3 platform can adapt to a different context and events happening in the network, such as lack of resources, data deluge, or mobility of data source, by managing (i.e., deploying or migrating) micro-services across CECC infrastructures. AC3 will leverage AI, ML, and semantic and context awareness algorithms to provide an efficient life cycle management system of services, data sources, and CECC resources for ensuring low response time and high data rate while saving energy consumption.

CODECO is a cognitive, cross-layer and highly adaptive Edge-Cloud management framework with a unique orchestration approach that provides support for data management and governance decentralised data workflow; dynamic offloading of computation and computation status; and adaptive networking services (TRL5). In the core of the CODECO framework are privacy preserving decentralised learning mechanisms to i) reduce latency and power consumption from the far Edge to Cloud; ii) adjust the computation in real-time to available Edge-Cloud constraints; iii) adjust running services into the needs of the application, the data sources, the surrounding context; iv) benefit from a flexible networking infrastructure that adapts to the needs of active services; and v) democratize the technology to a faster market adoption of the toolkit, as well as products and services derived from it. CODECO proposes the following assets: i) A1: Open, cognitive toolkits and smart Apps, integrating the elastic and advanced concepts to manage, in a smart and flexible way, containerized applications across Edge and Cloud (dynamic cluster and multi-cluster environment; ii) A2: A developer-oriented open-source software repository, to be available in an early stage of the project, thus allowing for early exploitation of initial, advanced results and a better adaptation throughout the project lifetime; iii) A3: Training tools, to support the development of services based on the CODECO framework; iv) A4: Use-cases across 4 domains (Smart Cities, Energy, Manufacturing, Smar Buildings), as the basis for experimentation and demonstrations; v) A5: a Research and Innovation community engagement programme, based on the different use-cases and including the different CODECO stakeholders; vi) A6: CODECO integration into the large-scale EdgeNet , experimental infrastructure, to assist in the building of experimentation and novel concepts by the research community.

The new data-driven industrial revolution highlights the need for big data technologies to unlock the potential in various application domains. To this end, BigDataStack delivers a complete high-performant stack of technologies addressing the emerging needs of data operations and applications. The stack is based on a frontrunner infrastructure management system that drives decisions according to data aspects thus being fully scalable, runtime adaptable and performant for big data operations and data-intensive applications. BigDataStack promotes automation and quality and ensures that the provided data are meaningful, of value and fit-for-purpose through its Data as a Service offering that addresses the complete data path with approaches for data cleaning, modelling, semantic interoperability, and distributed storage. BigDataStack introduces a pioneering technique for seamless analytics which analyses data in a holistic fashion across multiple data stores and locations, handling analytics on both data in flight and at rest. Complemented with an innovative CEP running in federated environments for real-time cross-stream processing, predictive algorithms and process mining, BigDataStack offers a complete suite for big data analytics. BigDataStack holistic solution incorporates approaches for data-focused application analysis and dimensioning, and process modelling towards increased performance, agility and efficiency. A toolkit allowing the specification of analytics tasks in a declarative way, their integration in the data path, as well as an adaptive visualization environment, realize BigDataStack’s vision of openness and extensibility. With an emphasis on standardisation and open source contributions targeting high impact, BigDataStack will enable data operations and data-intensive applications to take full advantage of the developed technologies, exhibiting their applicability through three commercial use cases from the maritime, market and financing domains.

CONNECT addresses the convergence of security and safety in CCAM by assessing dynamic trust relationships and defining a trust reasoning framework based on which involved entities can establish trust for cooperatively executing safety-critical functions. This will enable both a) cyber-secure data sharing between data sources in the CCAM ecosystem that had no or insufficient pre-existing trust relationship, and b) outsourcing tasks to the MEC and cloud in a trustworthy way. Beyond the needs of functional safety, trustworthiness management should be included in CCAM’s security functionality solution for verifying trustworthiness of transmitting stations and infrastructure. CONNECT will build upon and expand the Zero Trust concept to tackle the issue of how to bootstrap vertical trust from the application, the execution environment and device hardware from the vehicle up to MEC and cloud environments. This includes measuring the system when instantiating network functions and determining the integrity and origin of software. Trusted Execution Environments (TEEs), as sw- or hw-based security elements, will be essential to establish a verifiable chain of trust throughout the entire application stack of the host vehicle, as well as protecting data in transit, at rest and in use. By coupling the Zero Trust security principle with the need of “Never Trust, Always Verify”, CONNECT bootstraps vertical trust for all users, devices and systems in the CCAM ecosystem by enabling continuous authorization and authentication prior to be granted access to data or resources. Through TEE-enabled “Chip-to-Cloud" assurances and verifiable chain of trust, CONNECT reaches its full potential: not only does it mitigates risks stemming from the Zero Trust CCAM environment but also ensures resilience. This can make CONNECT the cornerstone of future smart transportation as it will usher new levels of safety and connectivity and bring vehicles even close to autonomy.