A Cloud-Based Framework for Machine Learning Workloads and Applications

[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

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

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

The CMS experiment at the CERN LHC

The Compact Muon Solenoid (CMS) detector is described. The detector operates at the Large Hadron Collider (LHC) at CERN. It was conceived to study proton-proton (and lead-lead) collisions at a centre-of-mass energy of 14 TeV (5.5 TeV nucleon-nucleon) and at luminosities up to 10(34)cm(-2)s(-1) (10(27)cm(-2)s(-1)). At the core of the CMS detector sits a high-magnetic-field and large-bore superconducting solenoid surrounding an all-silicon pixel and strip tracker, a lead-tungstate scintillating-crystals electromagnetic calorimeter, and a brass-scintillator sampling hadron calorimeter. The iron yoke of the flux-return is instrumented with four stations of muon detectors covering most of the 4 pi solid angle. Forward sampling calorimeters extend the pseudo-rapidity coverage to high values (vertical bar eta vertical bar <= 5) assuring very good hermeticity. The overall dimensions of the CMS detector are a length of 21.6 m, a diameter of 14.6 m and a total weight of 12500 t

The CMS experiment at the CERN LHC

0info:eu-repo/semantics/publishe

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

This report presents the capabilities of the CMS experiment to explore the rich heavy-ion physics programme offered by the CERN Large Hadron Collider (LHC). The collisions of lead nuclei at energies ,will probe quark and gluon matter at unprecedented values of energy density. The prime goal of this research is to study the fundamental theory of the strong interaction - Quantum Chromodynamics (QCD) - in extreme conditions of temperature, density and parton momentum fraction (low-x). This report covers in detail the potential of CMS to carry out a series of representative Pb-Pb measurements. These include "bulk" observables, (charged hadron multiplicity, low pT inclusive hadron identified spectra and elliptic flow) which provide information on the collective properties of the system, as well as perturbative probes such as quarkonia, heavy-quarks, jets and high pT hadrons which yield "tomographic" information of the hottest and densest phases of the reaction.0info:eu-repo/semantics/publishe