ELEGANT aims to solve the ever-increasing problem of software fragmentation in the IoT/Big Data interoperability domain. Software fragmentation prohibits the unification of these two ecosystems severely limiting the ability to regard them as a single system and tune the whole infrastructure towards defining its a) Performance, b) Energy Efficiency, c) Security, d) Reliability, and d) Dependability (PESRD) requirements. ELEGANT proposes a novel software programming paradigm, along with an associated set of methodologies and toolchains, to program IoT and Big Data frameworks using a unified programming framework. Its key proposed innovations in the areas of: a) Light-weight application virtualization, b) Automatic code extraction compatible with both IoT and Big Data frameworks, c) AI-assisted Intelligent Orchestration, d) dynamic code motion, and e) advanced code verification and cybersecurity mechanisms, will enable the seamless operation of end-to-end IoT/Big Data complex systems. This way, users employing the ELEGANT software stack and methodologies will be able to seamlessly define the pareto-optimal point in the PESRD optimization space while the entire system will be able to dynamically adjust itself during execution. To achieve its ambitious goals, ELEGANT assembles a consortium of experts across all domains ranging from low-level system software, IoT, Big Data, AI-assisted scheduling, and DevOps. Finally, the proposed solutions will be evaluated against pre-defined KPIs across a wide range of operational use cases from four distinct domains: health, automotive, smart metering, and video surveillance.

One of the key enablers of 6G is undoubtedly the Native support of AI/ML at all the system levels, components, and mechanisms, from the orchestration and management levels to the low-level optimization of the infrastructure resources, including Cloud, Edge, RAN, Core Network, as well as a transport network. Despite the opportunities, there are several gaps that hinder the adoption of AI/ML in 6G, such as the lack of extensive and high-quality datasets that are required to train the models. On the other hand, AI model testing and performance evaluation in a representative staging environment (by emulation or real deployment) is also challenging without access to an end-to-end 6G testbed or representative Digital Twin environment. To this end, 6G-DALI aims to deliver an end-to-end AI framework for 6G, structured in two interdependent pillars, (1) AI experimentation as a service via MLOps and (2) Data and analytics collection and storage via DataOps. The 6G-DALI DataOps pillar provides the mechanisms for preparing clean and processed data that are stored within a 6G Dataspace and are made available for training and validating machine learning models as a service, a part of the MLOps Pillar. The end-to-end framework also delivers continuous monitoring, drift detection and retraining of models. Finally, 6G-DALI will deliver open datasets, a 6G Dataspace for dataset storage and secure sharing, and a Digital Twin testbed for data generation on demand.

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.