14 research outputs found

    A Cloud-Based Framework for Machine Learning Workloads and Applications

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    [EN] In this paper we propose a distributed architecture to provide machine learning practitioners with a set of tools and cloud services that cover the whole machine learning development cycle: ranging from the models creation, training, validation and testing to the models serving as a service, sharing and publication. In such respect, the DEEP-Hybrid-DataCloud framework allows transparent access to existing e-Infrastructures, effectively exploiting distributed resources for the most compute-intensive tasks coming from the machine learning development cycle. Moreover, it provides scientists with a set of Cloud-oriented services to make their models publicly available, by adopting a serverless architecture and a DevOps approach, allowing an easy share, publish and deploy of the developed models.This work was supported by the project DEEP-Hybrid-DataCloud ``Designing and Enabling E-infrastructures for intensive Processing in a Hybrid DataCloud'' that has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant 777435Lopez Garcia, A.; Marco De Lucas, J.; Antonacci, M.; Zu Castell, W.; David, M.; Hardt, M.; Lloret Iglesias, L.... (2020). A Cloud-Based Framework for Machine Learning Workloads and Applications. IEEE Access. 8:18681-18692. https://doi.org/10.1109/ACCESS.2020.2964386S1868118692

    INDIGO-DataCloud: a Platform to Facilitate Seamless Access to E-Infrastructures

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    [EN] 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.INDIGO-Datacloud has been funded by the European Commision H2020 research and innovation program under grant agreement RIA 653549.Salomoni, D.; Campos, I.; Gaido, L.; Marco, J.; Solagna, P.; Gomes, J.; Matyska, L.... (2018). INDIGO-DataCloud: a Platform to Facilitate Seamless Access to E-Infrastructures. Journal of Grid Computing. 16(3):381-408. https://doi.org/10.1007/s10723-018-9453-3S381408163García, A.L., Castillo, E.F.-d., Puel, M.: Identity federation with VOMS in cloud infrastructures. In: 2013 IEEE 5Th International Conference on Cloud Computing Technology and Science, pp 42–48 (2013)Chadwick, D.W., Siu, K., Lee, C., Fouillat, Y., Germonville, D.: Adding federated identity management to OpenStack. Journal of Grid Computing 12(1), 3–27 (2014)Craig, A.L.: A design space review for general federation management using keystone. 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Model Dev. 8(7), 2067–2078 (2015)Plasencia, I.C., Castillo, E.F.-d., Heinemeyer, S., García, A.L., Pahlen, F., Borges, G.: Phenomenology tools on cloud infrastructures using OpenStack. The European Physical Journal C 73(4), 2375 (2013)Boettiger, C.: An introduction to docker for reproducible research. ACM SIGOPS Operating Systems Review 49(1), 71–79 (2015)Docker: http://www.docker.com (2013)Gomes, J., Campos, I., Bagnaschi, E., David, M., Alves, L., Martins, J., Pina, J., Alvaro, L.-G., Orviz, P.: Enabling rootless linux containers in multi-user environments: the udocker tool. Computing Physics Communications. https://doi.org/10.1016/j.cpc.2018.05.021 (2018)Zhang, Z., Chuan, W., Cheung, D.W.L.: A survey on cloud interoperability taxonomies, standards, and practice. SIGMETRICS perform. Eval. Rev. 40(4), 13–22 (2013)Lorido-Botran, T., Miguel-Alonso, J., Lozano, J.A.: A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments. 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Computer Standards & Interfaces 47, 19–23 (2016)Caballer, M., Zala, S., García, A.L., Montó, G., Fernández, P.O., Velten, M.: Orchestrating complex application architectures in heterogeneous clouds. Journal of Grid Computing 16 (1), 3–18 (2018)Hardt, M., Jejkal, T., Plasencia, I.C., Castillo, E.F.-d., Jackson, A., Weiland, M., Palak, B., Plociennik, M., Nielsson, D.: Transparent Access to Scientific and Commercial Clouds from the Kepler Workflow Engine. Computing and Informatics 31(1), 119 (2012)Fakhfakh, F., Kacem, H.H., Kacem, A.H.: Workflow Scheduling in Cloud Computing a Survey. In: IEEE 18Th International Enterprise Distributed Object Computing Conference Workshops and Demonstrations (EDOCW), 2014, Vol. 71, pp. 372–378. Springer, New York (2014)Stockton, D.B., Santamaria, F.: Automating NEURON simulation deployment in cloud resources. Neuroinformatics 15(1), 51–70 (2017)Plóciennik, M., Fiore, S., Donvito, G., Owsiak, M., Fargetta, M., Barbera, R., Bruno, R., Giorgio, E., Williams, D.N., Aloisio, G.: Two-level Dynamic Workflow Orchestration in the INDIGO DataCloud for Large-scale, Climate Change Data Analytics Experiments. Procedia Computer Science 80, 722–733 (2016)Moreno-Vozmediano, R., Montero, R.S., Llorente, I.M.: Multicloud deployment of computing clusters for loosely coupled mtc applications. IEEE transactions on parallel and distributed systems 22(6), 924–930 (2011)Katsaros, G., Menzel, M., Lenk, A.: Cloud Service Orchestration with TOSCA, Chef and Openstack. In: Ic2e (2014)Garcia, A.L., Zangrando, L., Sgaravatto, M., Llorens, V., Vallero, S., Zaccolo, V., Bagnasco, S., Taneja, S., Dal Pra, S., Salomoni, D., Donvito, G.: Improved Cloud resource allocation: how INDIGO-DataCloud is overcoming the current limitations in Cloud schedulers. J. Phys. Conf. Ser. 898(9), 92010 (2017)Singh, S., Chana, I.: A survey on resource scheduling in cloud computing issues and challenges. Journal of Grid Computing, pp. 1–48 (2016)García, A.L., Castillo, E.F.-d., Fernández, P.O., Plasencia, I.C., de Lucas, J.M.: Resource provisioning in Science Clouds: Requirements and challenges. Software: Practice and Experience 48(3), 486–498 (2018)Chauhan, M.A., Babar, M.A., Benatallah, B.: Architecting cloud-enabled systems: a systematic survey of challenges and solutions. Software - Practice and Experience 47(4), 599–644 (2017)Somasundaram, T.S., Govindarajan, K.: CLOUDRB A Framework for scheduling and managing High-Performance Computing (HPC) applications in science cloud. Futur. Gener. Comput. Syst. 34, 47–65 (2014)Sotomayor, B., Keahey, K., Foster, I.: Overhead Matters: A Model for Virtual Resource Management. In: Proceedings of the 2nd International Workshop on Virtualization Technology in Distributed Computing SE - VTDC ’06, p 5. 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Technical report (2015)Europen open science cloud: https://ec.europa.eu/research/openscience (2015)Proot: https://proot-me.github.io/ (2014)Runc: https://github.com/opencontainers/runc (2016)Fakechroot: https://github.com/dex4er/fakechroot (2015)Pérez, A., Moltó, G., Caballer, M., Calatrava, A.: Serverless computing for container-based architectures Future Generation Computer Systems (2018)de Vries, K.J.: Global fits of supersymmetric models after LHC run 1. Phd thesis Imperial College London (2015)Openstack: https://www.openstack.org/ (2015)See http://argus-documentation.readthedocs.io/en/stable/argus_introduction.html (2017)See https://en.wikipedia.org/wiki/xacml (2013)See http://www.simplecloud.info (2014)Opennebula: http://opennebula.org/ (2018)Redhat openshift: http://www.opencityplatform.eu (2011)The cloud foundry foundation: https://www.cloudfoundry.org/ (2015)Caballer, M., Blanquer, I., Moltó, G., de Alfonso, C.: Dynamic management of virtual infrastructures. Journal of Grid Computing 13(1), 53–70 (2015)See http://www.infoq.com/articles/scaling-docker-with-kubernetes http://www.infoq.com/articles/scaling-docker-with-kubernetes (2014)Prisma project: http://www.ponsmartcities-prisma.it/ (2010)Opencitiy platform: http://www.opencityplatform.eu (2014)Onedata: https://onedata.org/ (2018)Dynafed: http://lcgdm.web.cern.ch/dynafed-dynamic-federation-project http://lcgdm.web.cern.ch/dynafed-dynamic-federation-project (2011)Fts3: https://svnweb.cern.ch/trac/fts3 (2011)Fernández, P.O., García, A.L., Duma, D.C., Donvito, G., David, M., Gomes, J.: A set of common software quality assurance baseline criteria for research projects, see http://hdl.handle.net/10261/160086 . Technical reportHttermann, M.: Devops for developers Apress (2012)EOSC-Hub: ”Integrating and managing services for the European Open Science Cloud” Funded by H2020 research and innovation pr ogramme under grant agreement No. 777536. See http://eosc-hub.eu (2018)Apache License: author = https://www.apache.org/licenses/LICENSE-2.0 (2004)INDIGO Package Repo: http://repo.indigo-datacloud.eu/ (2017)INDIGO DockerHub: https://hub.docker.com/u/indigodatacloud/ https://hub.docker.com/u/indigodatacloud/ (2015)Indigo gitbook: https://indigo-dc.gitbooks.io/indigo-datacloud-releases https://indigo-dc.gitbooks.io/indigo-datacloud-releases (2017)Van Zundert, G.C., Bonvin, A.M.: Disvis: quantifying and visualizing the accessible interaction space of distance restrained biomolecular complexes. Bioinformatics 31(19), 3222–3224 (2015)Van Zundert, G.C., Bonvin, A.M.: Fast and sensitive rigid–body fitting into cryo–em density maps with powerfit. AIMS Biophys. 2(0273), 73–87 (2015

    CMS physics technical design report : Addendum on high density QCD with heavy ions

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    Orchestrating complex application architectures in heterogeneous clouds: the INDIGO-DataCloud case

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    Cloud infrastructures are now widely adopted across technology industries and research institutions. However, single cloud providers may not fully satisfy more complex user requirements, and to cope with them, a solution that do not rely on a single cloud environment but instead allow resource provisioning from external providers is required. As a result, in the recent years there has been a growing interest in developing hybrid cloud solutions that bind together distinct and heterogeneous cloud infrastructures. In this paper we will describe the orchestration approach for heterogeneous clouds being implemented within the INDIGO-DataCloud project, based on the OASIS Topology and Specification for Cloud Applications (TOSCA) standard

    INDIGO-DataCloud : a Platform to Facilitate Seamless Access to E-Infrastructures

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    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
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