ExaNeSt will develop, evaluate, and prototype the physical platform and architectural solution for a unified Communication and Storage Interconnect and the physical rack and environmental structures required to deliver European Exascale Systems. The consortium brings technology, skills, and knowledge across the entire value chain from computing IP to packaging and system deployment; and from operating systems, storage, and communication to HPC with big data management, algorithms, applications, and frameworks. Building on a decade of advanced R&D, ExaNeSt will deliver the solution that can support exascale deployment in the follow-up industrial commercialization phases. Using direction from the ETP4HPC roadmap and soon-available high density and efficiency compute, we will model, simulate, and validate through prototype, a system with: 1. High throughput, low latency connectivity, suitable for exascale-level compute, their storage, and I/O, with congestion mitigation, QoS guarantees, and resilience. 2. Support for distributed storage located with the compute elements providing low latency that non-volatile memories require, while reducing energy, complexity, and costs. 3. Support for task-to-data sw locality models to ensure minimum data communication energy overheads and property maintenance in databases. 4. Hyper-density system integration scheme that will develop a modular, commercial, European-sourced advanced cooling system for exascale in ~200 racks while maintaining reliability and cost of ownership. 5. The platform management scheme for big-data I/O to this resilient, unified distributed storage compute architecture. 6. Demonstrate the applicability of the platform for the complete spectrum of Big Data applications, e.g. from HPC simulations to Business Intelligence support. All aspects will be steered and validated with the first-hand experience of HPC applications and experts, through kernel turning and subsequent data management and application analysis.

Despite their proliferation as a dominant computing paradigm, cloud computing systems lack effective mechanisms to manage their vast amounts of resources efficiently, leading to severe resource waste and ultimately limiting their applicability to large classes of critical applications that pose non-moderate resource demands. This creates a significant need to lift existing technological barriers of actual fluidity and scalability of cloud resources towards promoting cloud computing as a critical cornerstone for digital economy. ACTiCLOUD proposes a novel cloud computing architecture for drastically improved management of cloud resources, targeting 1.5x increase in resource efficiency and more than 10x in scalability. By utilizing modest investments on hardware intelligence that enables true resource disaggregation between multiple servers, we will progress current state-of-the-art in hypervisors and cloud management systems promoting holistic resource management at the rack scale and across distributed cloud sites. On top of this, we will evolve the ecosystem around in-memory databases, a core component for extremely demanding and highly critical classes of applications that up to now have faced severe difficulties in matching their resource requirements with state-of-the-art cloud offerings, with a final goal to provide cost-efficient and highly performant DataBase-as-a-Service (DBaaS) cloud platforms. ACTiCLOUD builds on top of cutting-edge European technologies for cloud servers brought into the project by Numascale and Kaleao, and extends OnApp's MicroVisor, an innovative hypervisor to virtualize resources at the rack-scale. Furthermore it joins the forces of highly acclaimed academic institutions to address key research challenges and extend the capabilities of OpenStack and JVM. Finally, it applies the foreseen innovation to MonetDB, the column-store database pioneer, and Neo4j, the world-leader in graph databases.

Shipping is the lifeblood of global economy, consequently one of the leading sources of greenhouse gases and one of the high-incident domains, due to heavy traffic especially in congested waters, therefore facing escalating pressure for safety, energy efficiency improvement and emissions reduction. Meanwhile, shipping generates extremely large amount of data in every minute, which potential, however, still remains untapped due to the involvement of enormous stakeholders and the sophistication of modern vessel design and operation. To address these challenges, VesselAI aims to develop, validate and demonstrate a unique framework to unlock the potential of extreme-scale data and advanced HPC, AI and Digital Twin technologies, and hence to promote the adoption and application of Big Data-driven innovations and solutions in maritime industry and beyond. By combining Digital Twin technologies and practices, VesselAI can efficiently fuse and assimilate huge amount of data, coming from both observations and simulations, to achieve highly accurate modelling, estimation and optimization of design and operation of ships and fleets under various dynamic conditions in near real time. Their technical enhancements and practical performance improvements are further demonstrated in 4 maritime industry pilots, tackling practical challenges for 1) global vessel traffic monitoring and management, 2) globally optimal ship energy system design, 3) short-sea autonomous shipping and 4) global fleet intelligence. VesselAI brings in a consortium of renowned actors in maritime and ICT domains, providing a perfect mix of high-level expertise in both domains and readily accessibility to huge amount of data for industry-leading research and innovation in the project. Together, VesselAI addresses the challenges of implementing extreme-scale analytics in industries and showcase how AI, cloud computing and HPC can encourage, and enable deeper digitalization in the maritime and wider industries.

The primary goal of each retailer is to “understand your customers”. Our interviews with retailers show a primary demand from the retail industry for predicting a customer's next demand. Surprisingly , even a complete record of past purchases (and returns) is not sufficient to understand how items in a company's catalog do or do not connect with the customer's general tastes, lifestyle and aspirations. Moverover, from a business perspective, any efficiency gains in the logistics of supplier management, shipping and handling are rather minor, compared to the gains one could obtain from a better understanding of the customers’ personalities and habits. Given that the customer demands trigger proactive stocking and fashion production, this appears as a logical consequence. In this project, we want to consolidate and extend existing European technologies in the area of database management, data mining, machine learning, image processing, information retrieval, and crowdsourcing to strengthen the positions of European fashion retailers among their world-wide competitors. Our choice for the fashion sector is a concise one: i) as a multi-billion euro industry, the fashion sector is extremely important for the European economy; ii) Europe already has a solid position in the world fashion stage, however, to maintain its position and keep up with the competitors, European fashion industry needs the help of advanced technology; and iii) European fashion industry provides an excellent exercise for new technologies, because it is a multi-sectorial by itself (i.e., imposes challenging data integration issues), it has a short life-cycle (i.e., requires timely reaction to the current events) and it involves diverse languages and cultures. The main outcome of the FashionBrain project is the improvement of the fashion industry value chain obtained thanks to the creation of novel on-line shopping experiences, the detection of influencers, and the prediction of upcoming fashion trends. Tangible outcomes will include software, demonstrators, and novel algorithms for a data-driven fashion industry.