The main goal is to design an Extreme near-data platform to enable consumption, mining and processing of dis- tributed and federated data without needing to master the logistics of data access across heterogeneous data locations and pools. We go beyond traditional passive or bulk data ingested from storage systems towards next generation near-data processing platforms both in the Cloud and in the Edge. In our platform, Extreme Data in- cludes both metadata and trustworthy data connectors enabling advanced data management operations like data discovery, mining, and filtering from heterogeneous data sources. The three core objectives are: O-1 Provide high-performance near-data processing for Extreme Data Types: The first objective is to create a novel intermediary data service (XtremeDataHub) providing serverless data connectors that optimize data management operations (partitioning, filtering, transformation, aggregation) and interactive queries (search, discovery, matching, multi-object queries) to efficiently present data to analytics platforms. Our data connectors facilitate a elas- tic data-driven process-then-compute paradigm which significantly reduces data communication on the data interconnect, ultimately resulting in higher overall data throughput. O-2 Support real-time video streams but also event streams that must be ingested and processed very fast to Object Storage: The second objective is to seamlessly combine streaming and batch data processing for analytics. To this end, we will develop stream data connectors deployed as stream operators offering very fast stateful computations over low-latency event and video streams. O-3 The third objective is to create a Data Broker service enabling trustworthy data sharing and confidential orchestration of data pipelines across the Compute Continuum. We will provide secure data orchestration, transfer, processing and access thanks to Trusted Execution Environments (TEEs) and federated learning architectures.

As of today, 80% of the data processing and analysis occurs in cloud data centers, and only 20% of processing occurs at the edge. This incipient exploitation of edge resources increases time to value and prevents business processes, decisions, and intelligence to be taken outside of the data center, which prevents Europe to unlock an entire set of new opportunities to serve different industries and use cases in Europe in the next years. To help to materialize the European bid on a true continuum in the next few years, CloudSkin pursues to build a cognitive cloud continuum platform with three main innovations: 1. The CloudSkin platform will leverage AI/ML to optimize workloads, resources, energy, and network traffic for a rapid adaptation to changes in application behavior and data variability, re-configuraing the "sweet spot" between the cloud and the edge in the face of the rapid varying conditions; 2. The CloudSkin platform will also help users to achieve “stack identicality” across the Cloud-edge continuum, whereby the same (legacy) software stacks (e.g., MPI programs) running in data centers can seamlessly run at remote edges. The development of a new lightweight, portable virtualizaion abstraction will be paired with the development of new confidential abstractions to protect data while it is in use; and 3. CloudSkin will also contribute to prepare the needed infrastructure to integrate the new virtualized execution abstractions into the virtual resource continuum, particularly, for those Cloud-edge applications composed of small tasks with fast data access and sharing requirements. The infrastructure will expose the relevant control knobs to enable dynamic reconfiguration of resources as assisted by the AI/ML-based orchestration plane in the CloudSkin platform. Altogether, the above innovations are the strategic elements of what we envision as the new “cognitive continuum for the cloud and edge".