Exploiting private and commercial clouds to generate on-demand CMS computing facilities with DODAS
| Autor: | Cristina Duma, Luciano Gaido, T. Boccali, Álvaro López García, Diego Ciangottini, Mirco Tracolli, Aida Palacio Hoz, Marica Antonacci, Daniele Spiga, Andrea Ceccanti, Davide Salomoni, Giacinto Donvito, Riccardo Di Maria |
|---|---|
| Přispěvatelé: | European Commission |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Service (systems architecture)
Database Exploit 010308 nuclear & particles physics business.industry Physics QC1-999 Big data computer.software_genre 01 natural sciences Personalization Learning curve 0103 physical sciences Container (abstract data type) Scalability Use case Detectors and Experimental Techniques 010306 general physics business computer |
| Zdroj: | Digital.CSIC. Repositorio Institucional del CSIC instname EPJ Web of Conferences, Vol 214, p 07027 (2019) EPJ Web of Conferences |
| Popis: | 23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018). 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 would like to thank the European Commission’s Horizon 2020 research and innovation programme for financial support, under grant agreement RIA 777536. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|