Exploiting private and commercial clouds to generate on-demand CMS computing facilities with DODAS
Istituto Nazionale di Fisica Nucleare,
2 Istituto Nazionale di Fisica Nucleare, 70126 Bari, Italy
3 Istituto Nazionale di Fisica Nucleare, 56127 Pisa, Italy
4 Istituto Nazionale di Fisica Nucleare CNAF, 40127 Bologna, Italy
5 Imperial College London, South Kensington, SW7 2AZ, London, UK
6 Istituto Nazionale di Fisica Nucleare, 10125 Torino, Italy
7 Instituto de Física de Cantabria (CSIC-UC), 39005 Santander Cantabria, Spain
1 Corresponding author: email@example.com
Published online: 17 September 2019
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.
© The Authors, published by EDP Sciences, 2019
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