From edge to cloud, big data analytics is growing fast, and its energy consumption has become a reason of concern for national grids and they generate significant carbon emissions. The GLACIATION project aims to address this issue through energy-efficient data operations. By developing a novel Distributed Knowledge Graph (DKG) that stretches across the edge-core-cloud architecture, reduction in the energy consumption for data processing will be achieved through AI enforced minimal data movement operations. GLACIATION will achieve significant power consumption reduction through optimizing the location where analytics are carried out. The projects Meta Data framework will provide tools that incorporate privacy and trust aspects in the data operations. GLACIATION is demonstrated on three relevant industry settings which benefit from optimized data movement and power consumption reduction. More specifically, GLACIATION use cases cover public-service, manufacturing, and enterprise data analytics.

Coastal urban development incorporates a wide range of development activities that are taking place as a result of the water element existing in the fabric of the city. This element may have different forms (i.e. a bay, a river, or a brook) but in almost all cases the surrounding area constitutes what maybe considered as the heart of the city. Every city that incorporates the water-element in its fabric is confronted with the fundamental requirement of developing policies for driving development in the surrounding area, while balancing between: a) economic growth, b) protection of the environmental, and c) safeguarding social cohesion. This requirement is tightly connected with the concept of Urban Resilience, which is the capacity of individuals, communities, businesses and systems within a city to survive, adapt and grow no matter what chronic stresses and acute shocks they experience. In developing policies that add value to the resilience of a city, we shift the existing paradigm of policy making, which is largely based on intuition, towards an evidence-driven approach enabled by big data. Our attention is placed on policies related to the water element. Our basis is the sensing infrastructures installed in the cities offering demographic data, statistical information, sensor readings and user contributed content forming the big data layer. Methods for big data analytics are used to measure the economic activity, assess the environmental impact and evaluate the social consequences. The extracted pieces of evidence are used to inform, advice, monitor, evaluate and revise the decisions made by policy planners. Finally, effective policies are developed dealing with: a) the economic and urban development of Thermaikos Bay, Thessaloniki, b) the transformation of Düden Brook into a recreation and park area, Antalya, c) the development of a Storm Water Plan, Antwerp, and d) the review of the Country Development Plan in the River Lee territory, City of Cork.

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".

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.

At the 2011 OECD High-Level Meeting “The Internet Economy: Generating Innovation and Growth”, Vint Cerf, VP and Chief Internet Evangelist at Google, said that one of the biggest issues with the Internet is “keeping the network as open as possible to invite as much innovation as we can with as little barrier to that innovation as possible, so that new Googles and Amazons and PayPals and Skypes can happen all around the world.” A significant barrier to innovation by SMEs is the ossification of the Internet transport architecture. New groundbreaking services often require different transport protocols, better signalling between application and network, or a more flexible choice of links. A few large enterprises have the resources to support their innovations by developing their own transport systems—Adobe, Google and Microsoft have done so. Open sophisticated transport protocols exist now, but are difficult for SMEs to use owing to their lack of support across the Internet. NEAT addresses two obstacles to Internet innovation: 1) It lowers the barrier to service innovation by developing a free open-source transport system that will allow SMEs to leverage the rich set of available transport protocols. 2) It paves the way for an architectural change of the Internet where new transport layer services can seamlessly be integrated and quickly made available, minimising deployment difficulties, and allowing Internet innovators to take advantage of them wherever possible. By optionally signalling between applications and the network, NEAT demonstrates new avenues for in-network support of application services. By decoupling the services offered to applications from the underlying network technologies, NEAT enables seamless integration with different computing environments and generalised mobility. The NEAT transport system will provide built-in security and privacy, allowing the implementation of these functions more efficiently and making them more attractive to use.