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This deliverable provides overview of the release of first maintenance package for common services required by the thematic and specialized services. The package addresses common services in the area of T6.1, 6.2, 6.3 and T6.4 (without B2NOTE). It comprises first software adaptations of the common services that feature horizontal and vertical interoperability.

The eXtreme DataCloud (XDC) project is aimed at developing data management services capable to cope with very large data resources allowing the future e-infrastructures to address the needs of the next generation extreme scale scientific experiments. Started in November 2017, XDC is combining the expertise of 8 large European research organisations, the project aims at developing scalable technologies for federating storage resources and managing data in highly distributed computing environments. The project is use case driven with a multidisciplinary approach, addressing requirements from research communities belonging to a wide range of scientific domains: Life Science, Biodiversity, Clinical Research, Astrophysics, High Energy Physics and Photon Science, that represent an indicator in terms of data management needs in Europe and worldwide. The use cases proposed by the different user communities are addressed integrating different data management services ready to manage an increasing volume of data. Different scalability and performance tests have been defined to show that the XDC services can be harmonized in different contexts and complex frameworks like the European Open Science Cloud. The use cases have been used to measure the success of the project and to prove that the developments fulfill the defined needs and satisfy the final users. The present contribution describes the results carried out from the adoption of the XDC solutions and provides a complete overview of the project achievements.

International audience; The eXtreme DataCloud (XDC) project is aimed at developing data management services capable to cope with very large data resources allowing the future e-infrastructures to address the needs of the next generation extreme scale scientific experiments. Started in November 2017, XDC is combining the expertise of 8 large European research organisations. The project aims at developing scalable technologies for federating storage resources and managing data in highly distributed computing environments. The project is use case driven with a multidisciplinary approach, addressing requirements from research communities belonging to a wide range of scientific domains: Life Science, Biodiversity, Clinical Research, Astrophysics, High Energy Physics and Photon Science, that represent an indicator in terms of data management needs in Europe and worldwide. The use cases proposed by the different user communities are addressed integrating different data management services ready to manage an increasing volume of data. Different scalability and performance tests have been defined to show that the XDC services can be harmonized in different contexts and complex frameworks like the European Open Science Cloud. The use cases have been used to measure the success of the project and to prove that the developments fulfil the defined needs and satisfy the final users. The present contribution describes the results carried out from the adoption of the XDC solutions and provides a complete overview of the project achievements.

International audience; The development of data management services capable to cope with very large data resources is a key challenge to allow the future e-infrastructures to address the needs of the next generation extreme scale scientific experiments. To face this challenge, in November 2017 the H2020 eXtreme DataCloud - XDC project has been launched. Lasting for 27 months and combining the expertise of eight large European research organisations, the project aims at developing scalable technologies for federating storage resources and managing data in highly distributed computing environments. The targeted platforms are the current and next generation e-Infrastructures deployed in Europe, such as the European Open Science Cloud (EOSC), the European Grid Infrastructure (EGI), and the Worldwide LHC Computing Grid (WLCG). The project is use-case driven with a multidisciplinary approach, addressing requirements from research communities belonging to a wide range of scientific domains: High Energy Physics, Astronomy, Photon and Life Science, Medical research. XDC is aimed at implementing data management scalable services, combining already established data management and orchestration tools, to address the following high level topics: policy driven data management based on Quality-of-Service, Data Life-cycle management, smart placement of data with caching mechanisms to reduce access latency, meta-data with no predefined schema handling, execution of pre-processing applications during ingestion, data management and protection of sensitive data in distributed e-infrastructures, intelligent data placement based on access patterns. This contribution introduces the project, presents the foreseen overall architecture and the developments that are being carried on to implement the requested functionalities.

Journal of grid computing 16(3), 381 - 408 (2018). doi:10.1007/s10723-018-9453-3, This paper describes the achievements of the H2020 project INDIGO-DataCloud. The project has provided e-infrastructures with tools, applications and cloud framework enhancements to manage the demanding requirements of scientific communities, either locally or through enhanced interfaces. The middleware developed allows to federate hybrid resources, to easily write, port and run scientific applications to the cloud. In particular, we have extended existing PaaS (Platform as a Service) solutions, allowing public and private e-infrastructures, including those provided by EGI, EUDAT, and Helix Nebula, to integrate their existing services and make them available through AAI services compliant with GEANT interfederation policies, thus guaranteeing transparency and trust in the provisioning of such services. Our middleware facilitates the execution of applications using containers on Cloud and Grid based infrastructures, as well as on HPC clusters. Our developments are freely downloadable as open source components, and are already being integrated into many scientific applications., Published by Springer Nature, Dordrecht

With the increasing availability of high performance computing power, new possibilities with respect to simulation and analysis become available and feasible. One of such methodologies is data farming, where large amounts of data are generated through simulation of several configurations from large parameter space and then analyzed for patterns. Unfortunately, the availability of versatile data farming systems is very limited and none of existing solutions allows integration with novel Cloud solutions. In this paper, we present our system, which is a flexible solution for running very large Data Farming experiments on both intranet clusters as well as on remote computational resources, including public Clouds. Another important aspect of the presented system is the support of interactive Data Farming experiments with online analysis of partial experiment results and experiment extending capability. Sample application of our system is present on military mission planning support scenario.

Using multiple Clouds as a single environment to conduct simulation-based virtual experiments at a large-scale is a challenging problem. This paper describes how this can be achieved with the Scalarm platform in the context of data farming. In particular, a use case with a private Cloud combined with public, commercial Clouds is studied. We discuss the current architecture and implementation of Scalarm in terms of supporting different infrastructures, and propose how it can be extended in order to attain a unification of different Clouds usage. We discuss different aspects of the Cloud usage unification including: scheduling virtual machines, authentication, and virtual machine state monitoring. An experimental evaluation of the presented solution is conducted with a genetic algorithm solving the well-known Travel Salesman Problem. The evaluation uses three different resource configurations: using only public Cloud, using only private Cloud, and using both public and private Clouds.

Big science needs synergistic approach from theory, experiment and computations. Significance of the computational science is still on its way up due to the importance of results concerning features of extreme time and space scales as well as of multiscale phenomena being a subject of current research. These needs have been recognized internationally and one of the activities undertaken in the last decade is the European Grid Infrastructure, consolidating national efforts on computational infrastructures developed under National Grid Initiatives (NGI). In the paper we present the way of building Polish NGI established for Polish scientists, allowing them to conduct interdisciplinary research on a national scale, and giving them transparent access to international grid resources via affiliated international grid infrastructures. The approach applied in setting up this e-infrastructure ensured integration with other similar platforms around the world, recently with special focus on domain specific solutions.

Background An increasing demand for blood products forces the rationalisation of management and conservation of blood. The aim of the study is to evaluate the possibility of retransfusion of blood conservation and the cost-effectiveness of this procedure when employed in Total Hip Replacement and Total Knee Arthroplasty. Material and methods This prospective cohort study involved two groups of patients. Group I comprised 50 patients who underwent blood retransfusion and in several cases had supplementary allogeneic transfusion. Group II, a control group, consisted of 50 patients who did not receive retransfusion. Results The retransfusion in Group I enabled the recovery of a mean amount of 364.5 ml (± 52.7) of blood in THR patients and 403.8 ml (± 110.7) in TKA patients. Demand for allogeneic blood transfusions in Group I versus Group II was 46% lower in THR patients and 42% lower in TKA patients. The blood recovered for retransfusion is biologically valuable with regard to cellular elements and plasma chemistries. In the costs evaluation, the total savings in Group I were 5,000 PLN. Conclusion Retransfusion of recuperated blood from postoperative drainage tubing is a simple and safe method that provides clinical and cost-effectiveness advantages.

In a modern globalised world, military and peace keeping forces often face situations which require very subtle and well planned operations taking into account cultural and social aspects of a given region and its population as well as dynamic psychological awareness related to recent events which can have impact on the attitude of the civilians. The goal of the EUSAS project is to develop a prototype of a system enabling mission planning support and training capabilities for soldiers and police forces dealing with asymmetric threat situations, such as crowd control in urban territory. In this paper, we discuss the data-farming infrastructure developed for this project, allowing generation of large amount of data from agent based simulations for further analysis allowing soldier training and evaluation of possible outcomes of different rules of engagement.