VINEYARD will develop an integrated platform for energy-efficient data centres based on new servers with novel, coarse-grain and fine-grain, programmable hardware accelerators. It will, also, build a high-level programming framework for allowing end-users to seamlessly utilize these accelerators in heterogeneous computing systems by using typical data-centre programming frameworks (e.g. MapReduce, Storm, Spark, etc.). VINEYARD will develop two types of energy-efficient servers integrating two novel hardware accelerator types: coarse-grain programmable dataflow engines and fine-grain all-programmable FPGAs that accommodate multiple ARM cores. The former will be suitable for data centre applications that can be represented in dataflow graphs while the latter will be used for accelerating applications that need tight communication between the processor and the hardware accelerators. Both types of programmable accelerators will be customized based on application requirements, resulting in higher performance and significantly reduced energy budgets. VINEYARD will additionally develop a new programming framework and the required system software to hide the programming complexity of the resulting heterogeneous system based on the hardware accelerators. This programming framework will also allow the hardware accelerators to be swapped in and out of the heterogeneous infrastructure so as to offer efficient energy use. VINEYARD will foster the expansion of the soft-IP cores industry, currently limited in the embedded systems, to in data centre market. The VINEYARD consortium has strong industrial foundations, and covers the whole value chain in the data-centre ecosystem; from the data-centre vendors up to the data-centre application programmers. VINEYARD plans to demonstrate the advantages of its approach in three real use-cases a) a bioinformatics application for high-accuracy brain modelling, b) two critical financial applications and c) a big-data analysis application.

The project aims at producing a European Cloud Database Appliance for providing a Database as a Service able to match the predictable performance, robustness and trustworthiness of on premise architectures such as those based on mainframes. The project will evolve cloud architectures to enable the increase of the uptake of cloud technology by providing the robustness, trustworthiness, and performance required for applications currently considered too critical to be deployed on existing clouds. CloudDBAppliance will deliver a cloud database appliance featuring: 1. A scalable operational database able to process high update workloads such as the ones processed by banks or telcos, combined with a fast analytical engine able to answer analytical queries in an online manner. 2. A Hadoop data lake integrated with the operational database to cover the needs from companies on big data. 3. A cloud hardware appliance leveraging the next generation of hardware to be produced by Bull, the main European hardware provider. This hardware is a scale-up hardware similar to the one of mainframes but with a more modern architecture. Both the operational database and the in-memory analytics engine will be optimized to fully exploit this hardware and deliver predictable performance. Additionally, CloudDBAppliance will deal with the need to tolerate catastrophic cloud data centres failures (e.g. a fire or natural disaster) providing data redundancy across cloud data centres.

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.