ALICE is the heavy-ion experiment at the CERN Large Hadron Collider. The experiment continuously took data during the first physics campaign of the machine from fall 2009 until early 2013, using proton and lead-ion beams. In this paper we describe the running environment and the data handling procedures, and discuss the performance of the ALICE detectors and analysis methods for various physics observables.

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.

A: Performance of triple GEM prototypes in strong magnetic field has been evaluated by means of a muon beam at the H4 line of the SPS test area at CERN. Data have been reconstructed and analyzed offline with two reconstruction methods: the charge centroid and the micro-Time-Projection-Chamber exploiting the charge and the time measurement respectively. A combination of the two reconstruction methods is capable to guarantee a spatial resolution better than 150 µm in magnetic field up to a 1 T.

This paper presents the pan-European EGEE Grid focusing on aspects such as production infrastructure, the management tools and the operational services offered. Usage statistics and the provided Quality of Service are analysed to assess the maturity level, the current penetration of Grid technologies in Europe and the current expansion trends. Being EGEE a large distributed infrastructure, operations are a joint effort of different regional centres with central coordination. EGEE operations rely on a common and agreed set of procedures, policies and interfaces, which are the foundation of operational services such as middleware deployment, Grid oversight, accounting, operational security management and support. A transition is in place to lead EGEE to a more sustainable approach based on a set of integrated National Grid Initiatives. With the support of the EGI-InSPIRE project the EGEE e-infrastructure and its services will migrate into a

Minimising time and cost is key to exploit private or commercial clouds. This can be achieved by increasing setup and operational efficiencies. The success and sustainability are thus obtained reducing the learning curve, as well as the operational cost of managing community-specific services running on distributed environments. The greater beneficiaries of this approach are communities willing to exploit opportunistic cloud resources. DODAS builds on several EOSC-hub services developed by the INDIGO-DataCloud project and allows to instantiate on-demand container-based clusters. These execute software applications to benefit of potentially “any cloud provider”, generating sites on demand with almost zero effort. DODAS provides ready-to-use solutions to implement a “Batch System as a Service” as well as a BigData platform for a “Machine Learning as a Service”, offering a high level of customization to integrate specific scenarios. A description of the DODAS architecture will be given, including the CMS integration strategy adopted to connect it with the experiment’s HTCondor Global Pool. Performance and scalability results of DODAS-generated tiers processing real CMS analysis jobs will be presented. The Instituto de Física de Cantabria and Imperial College London use cases will be sketched. Finally a high level strategy overview for optimizing data ingestion in DODAS will be described.