Dynamic On Demand Analysis Service (DODAS) is a Platform as a Service tool built combining several solutions and products developed by the INDIGO-DataCloud H2020 project. DODAS allows to instantiate on-demand container-based clusters. Both HTCondor batch system and platform for the Big Data analysis based on Spark, Hadoop etc, can be deployed on any cloud-based infrastructures with almost zero effort. DODAS acts as cloud enabler designed for scientists seeking to easily exploit distributed and heterogeneous clouds to process data. Aiming to reduce the learning curve as well as the operational cost of managing community specific services running on distributed cloud, DODAS completely automates the process of provisioning, creating, managing and accessing a pool of heterogeneous computing and storage resources. DODAS was selected as one of the Thematic Services that will provide multidisciplinary solutions in the EOSC-hub project, an integration and management system of the European Open Science Cloud starting in January 2018. The main goals of this contribution are to provide a comprehensive overview of the overall technical implementation of DODAS, as well as to illustrate two distinct real examples of usage: the integration within the CMS Workload Management System and the extension of the AMS computing model.

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Abstract. Over the past two years, the operations at CNAF, the ICT center of the Italian Institute for Nuclear Physics, have undergone significant changes. The adoption of configuration management tools, such as Puppet, and the constant increase of dynamic and cloud infrastructures have led us to investigate a new monitoring approach. The present work deals with the centralization of the monitoring service at CNAF through a scalable and highly configurable monitoring infrastructure.The selection of tools has been made taking into account the following requirements given by users: (I) adaptability to dynamic infrastructures, (II) ease of configuration and maintenance, capability to provide more flexibility, (III) compatibility with existing monitoring system, (IV) re-usability and ease of access to information and data. In the paper, the CNAF monitoring infrastructure and its related components are hereafter described: Sensu as monitoring router, InfluxDB as time series database to store data gathered from sensors, Uchiwa as monitoring dashboard and Grafana as a tool to create dashboards and to visualize time series metrics.

Distributed Computing Infrastructures have dedicated mechanisms to provide user communities with computational environments. While in the last decade the Grid has demonstrated to be a powerful paradigm in supporting scientific research, the complexity of the user experience still limits its adoption by unskilled user communities. Command line interfaces, X.509 certificates, template customization for job submission and data access tools require end-users to dedicate significant learning effort and thus represent a barrier to access Grid computing facilities. In this paper, we present a Web portal that solves the aforementioned limitations by providing simplified Valerio Venturi deceased 25

The INFN Tier-1 center at CNAF has been extended in 2016 and 2017 in order to include a small amount of resources (∼ 22 kHS06 corresponding to ∼ 10% of the CNAF pledges for LHC in 2017) physically located at the Bari-ReCas site (∼ 600 km distant from CNAF). In 2018, a significant fraction of the CPU power (∼ 170 kHS06, equivalent to ∼ 50% of the total CNAF pledges) is going to be provided via a collaboration with the PRACE Tier-0 CINECA center (a few km from CNAF), thus building a truly geographically distributed (WAN) center. The two sites are going to be interconnected via an high bandwidth link (400-1200 Gb/s), in order to ensure a transparent access to data residing on CNAF storage; the latency between the centers is small enough not to require particular caching strategies. In this contribution we describe the issues and the results of the production configuration, focusing both on the management aspects and on the performance provided to end-users.