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[EN] This paper introduces an open-source platform to support serverless computing for scientific data-processing workflow-based applications across the Cloud continuum (i.e. simultaneously involving both on-premises and public Cloud platforms to process data captured at the edge). This is achieved via dynamic resource provisioning for FaaS platforms compatible with scale-to-zero approaches that minimise resource usage and cost for dynamic workloads with different elasticity requirements. The platform combines the usage of dynamically deployed auto-scaled Kubernetes clusters on on-premises Clouds and automated Cloud bursting into AWS Lambda to achieve higher levels of elasticity. A use case in public health for smart cities is used to assess the platform, in charge of detecting people not wearing face masks from captured videos. Faces are blurred for enhanced anonymity in the on-premises Cloud and detection via Deep Learning models is performed in AWS Lambda for this data-driven containerised workflow. The results indicate that hybrid workflows across the Cloud continuum can efficiently perform local data processing for enhanced regulations compliance and perform Cloud bursting for increased levels of elasticity. ; The authors would like to thank the European Union for the project "Artificial Intelligence in Secure PRIvacy-preserving computing coNTinuum" (AI-SPRINT), with code 101016577, funded under the H2020 Framework Programme and also the regional government of the Comunitat Valenciana (Spain) for the project IDIFEDER/2018/032 (High-Performance Algorithms for the Modeling, Simulation and early Detection of diseases in Personalized Medicine), co-funded by the European Union ERDF funds (European Regional Development Fund) of the Comunitat Valenciana 2014-2020. ; Risco, S.; Moltó, G.; Naranjo-Delgado, DM.; Blanquer Espert, I. (2021). Serverless Workflows for Containerised Applications in the Cloud Continuum. Journal of Grid Computing. 19(3):1-18. https://doi.org/10.1007/s10723-021-09570-2 ; S ; 1 ; 18 ; 19 ; 3 ...

[EN] It is widely acknowledged that the development of sprays in the near-field is of primary importance for the spray formation downstream, as it affects both the spray angle, as well as the intact core length. In this frame, the present work aims to study the effects of turbulence inlet boundary condition on the spray formation by means of Direct Numerical Simulations on a real condition at low Reynolds number. To this extent, the code Paris-Simulator has been used, while a digital filter-based algorithm was used in order to generate synthetic turbulence at the inlet boundary condition. The influence of turbulence intensity and lengthscale on the atomization process has been studied and analyzed through 3 simulation for which these parameters have been varied. The results clearly highlight how the atomization is heavily affected by the inlet turbulence configuration. An analysis of the different atomizing conditions has been conducted, aiming to understand how the variation introduced by the inlet boundary condition on the velocity field is affecting the local atomization dynamics. ; This work was partly sponsored by "Ministerio de Economia y Competitividad", of the Spanish Government, in the frame of the Project "Estudio de la interaccion chorro-pared en condiciones realistas de motor", Reference TRA2015-67679-c2-1-R. The author thankfully acknowledges the computer resources at MareNostrum (BSC) and the technical support provided by FI-2016-3-0031. ; Salvador, FJ.; Ruiz, S.; Crialesi Esposito, M.; Blanquer Espert, I. (2018). Analysis on the efects of turbulent inflow conditions on spray primary atomization in the near-field by direct numerical simulation. International Journal of Multiphase Flow. 102:49-63. https://doi.org/10.1016/j.ijmultiphaseflow.2018.01.019 ; S ; 49 ; 63 ; 102

53 70 13 1 ; StratusLab: Claudia Platform (2013). http://stratuslab.eu/doku.php/claudia ; S ; The final publication is available at Springer via http://dx.doi.org/10.1007/s10723-014-9296-5 Cloud infrastructures are becoming an appropriate solution to address the computational needs of scientific applications. However, the use of public or on-premises Infrastructure as a Service (IaaS) clouds requires users to have non-trivial system administration skills. Resource provisioning systems provide facilities to choose the most suitable Virtual Machine Images (VMI) and basic configuration of multiple instances and subnetworks. Other tasks such as the configuration of cluster services, computational frameworks or specific applications are not trivial on the cloud, and normally users have to manually select the VMI that best fits, including undesired additional services and software packages. This paper presents a set of components that ease the access and the usability of IaaS clouds by automating the VMI selection, deployment, configuration, software installation, monitoring and update of Virtual Appliances. It supports APIs from a large number of virtual platforms, making user applications cloud-agnostic. In addition it integrates a contextualization system to enable the installation and configuration of all the user required applications providing the user with a fully functional infrastructure. Therefore, golden VMIs and configuration recipes can be easily reused across different deployments. Moreover, the contextualization agent included in the framework supports horizontal (increase/decrease the number of resources) and vertical (increase/decrease resources within a running Virtual Machine) by properly reconfiguring the software installed, considering the configuration of the multiple resources running. This paves the way for automatic virtual infrastructure deployment, customization and elastic modification at runtime for IaaS clouds. The authors would like to thank to thank the financial support received from ...

[EN] This paper describes an approach to integrate the jobs management of High Performance Computing (HPC) infrastructures in cloud architectures by managing HPC workloads seamlessly from the cloud job scheduler. The paper presents hpc-connector, an open source tool that is designed for managing the full life cycle of jobs in the HPC infrastructure from the cloud job scheduler interacting with the workload manager of the HPC system. The key point is that, thanks to running hpc-connector in the cloud infrastructure, it is possible to reflect in the cloud infrastructure, the execution of a job running in the HPC infrastructure managed by hpc-connector. If the user cancels the cloud-job, as hpc-connector catches Operating System (OS) signals (for example, SIGINT), it will cancel the job in the HPC infrastructure too. Furthermore, it can retrieve logs if requested. Therefore, by using hpc-connector, the cloud job scheduler can manage the jobs in the HPC infrastructure without requiring any special privilege, as it does not need changes on the Job scheduler. Finally, we perform an experiment training a neural network for automated segmentation of Neuroblastoma tumours in the Prometheus supercomputer using hpc-connector as a batch job from a Kubernetes infrastructure. ; The work presented in this article has been partially funded by the regional government of the Comunitat Valenciana (Spain), co-funded by the European Union ERDF funds (European Regional Development Fund) of the Comunitat Valenciana 2014¿2020, with reference IDIFEDER/2018/032 (High-Performance Algorithms for the Modeling, Simulation and early Detection of diseases in Personalized Medicine). The work is also co-funded by PRIMAGE (PRedictive In-silico Multiscale Analytics to support cancer personalised diaGnosis and prognosis, empowered by imaging biomarkers) a Horizon 2020 RIA project funded under the topic SC1-DTH-07-2018 by the European Commission, with grant agreement no: 826494. ; López-Huguet, S.; Segrelles Quilis, JD.; Kasztelnik, M.; Bubak, M.; ...

191 204 17 1 ; S ; [EN] Computer clusters are widely used platforms to execute different computational workloads. Indeed, the advent of virtualization and Cloud computing has paved the way to deploy virtual elastic clusters on top of Cloud infrastructures, which are typically backed by physical computing clusters. In turn, the advances in Green computing have fostered the ability to dynamically power on the nodes of physical clusters as required. Therefore, this paper introduces an open-source framework to deploy elastic virtual clusters running on elastic physical clusters where the computing capabilities of the virtual clusters are dynamically changed to satisfy both the user application's computing requirements and to minimise the amount of energy consumed by the underlying physical cluster that supports an on-premises Cloud. For that, we integrate: i) an elasticity manager both at the infrastructure level (power management) and at the virtual infrastructure level (horizontal elasticity); ii) an automatic Virtual Machine (VM) consolidation agent that reduces the amount of powered on physical nodes using live migration and iii) a vertical elasticity manager to dynamically and transparently change the memory allocated to VMs, thus fostering enhanced consolidation. A case study based on real datasets executed on a production infrastructure is used to validate the proposed solution. The results show that a multi-elastic virtualized datacenter provides users with the ability to deploy customized scalable computing clusters while reducing its energy footprint. The results of this work have been partially supported by ATMOSPHERE (Adaptive, Trustworthy, Manageable, Orchestrated, Secure, Privacy-assuring Hybrid, Ecosystem for Resilient Cloud Computing), funded by the European Commission under the Cooperation Programme, Horizon 2020 grant agreement No 777154. Alfonso Laguna, CD.; Caballer Fernández, M.; Calatrava Arroyo, A.; Moltó, G.; Blanquer Espert, I. (2018). Multi-elastic Datacenters: Auto-scaled Virtual Clusters ...

[EN] This paper introduces a platform to support serverless computing for scalable event-driven data processing that features a multi-level elasticity approach combined with virtualization of GPUs. The platform supports the execution of applications based on Docker containers in response to file uploads to a data storage in order to perform the data processing in parallel. This is managed by an elastic Kubernetes cluster whose size automatically grows and shrinks depending on the number of files to be processed. To accelerate the processing time of each file, several approaches involving virtualized access to GPUs, either locally or remote, have been evaluated. A use case that involves the inference based on deep learning techniques on transtoracic echocardiography imaging has been carried out to assess the benefits and limitations of the platform. The results indicate that the combination of serverless computing and GPU virtualization introduce an efficient and cost-effective event-driven accelerated computing approach that can be applied for a wide variety of scientific applications. ; The work presented in this article has been partially funded by a research grant from the regional government of the Comunitat Valenciana (Spain), co-funded by the European Union ERDF funds (European Regional Development Fund) of the Comunitat Valenciana 2014-2020, with reference IDIFEDER/2018/032 (High-Performance Algorithms for the Modeling, Simulation and early Detection of diseases in Personalized Medicine). The authors would also like to thank the Spanish "Ministerio de Economia, Industria y Competitividad" for the project "BigCLOE" with reference number TIN2016-79951-R and the project ATMOSPHERE, funded jointly by the European Commission under the Cooperation Programme, Horizon 2020 grant agreement No 777154 and the Brazilian Ministerio de Ciencia, Tecnologia e Inovacao (MCI-I), number 51119. D.M.N would like to thank the "Generalitat Valenciana, Spain" for the grant GrisoliaP/2017/071. ; Naranjo-Delgado, DM.; Risco, S.; ...

[EN] This article describes the development of an automated configuration of a software platform for Data Analytics that supports horizontal and vertical elasticity to guarantee meeting a specific deadline. It specifies all the components, software dependencies and configurations required to build up the cluster, and analyses the deployment times of different instances, as well as the horizontal and vertical elasticity. The approach followed builds up self-managed hybrid clusters that can deal with different workloads and network requirements. The article describes the structure of the recipes, points out to public repositories where the code is available and discusses the limitations of the approach as well as the results of several experiments. ; The work presented in this article has been partially funded by a research grant from the regional government of the Comunitat Valenciana (Spain), co-funded by the European Union ERDF funds (European Regional Development Fund) of the Comunitat Valenciana 2014-2020, with reference IDIFEDER/2018/032 (High-Performance Algorithms for the Modelling, Simulation and early Detection of diseases in Personalized Medicine). The authors would also like to thank the Spanish "Ministerio de Economia, Industria y Competitividad" for the project "BigCLOE" with reference number TIN2016-79951-R. ; López-Huguet, S.; Pérez-González, AM.; Calatrava Arroyo, A.; Alfonso Laguna, CD.; Caballer Fernández, M.; Moltó, G.; Blanquer Espert, I. (2019). A Self-managed Mesos Cluster for Data Analytics with QoS Guarantees. Future Generation Computer Systems. 96:449-461. https://doi.org/10.1016/j.future.2019.02.047 ; S ; 449 ; 461 ; 96

[EN] This paper describes the development of applications in the frame of the ATMOSPHERE platform. ATMOSPHERE provides means for developing container-based applications over a federated cloud offering measurin he trustworthiness of the applications. In this paper we show the design of a transcontinental application in the frame of medical imaging that keeps the data at one end and uses the processing capabilities of the resources available at the other end. The applications are described using TOSCA blueprints and the federation of IaaS resources is performed by the Fogbow middleware. Privacy guarantees are provided by means of SCONE and intensive computing resources are integrated through the use of GPUs directly mounted on the containers. ; The work in this article has been co-funded by project ATMOSPHERE, funded jointly by the European Commission under the Cooperation Programme, Horizon 2020 grant agreement No 777154 and the Brazilian Ministerio de Ci ¿ encia, Tecnologia e ¿ Inovac¿ao (MCTI), number 51119. ¿ The authors also want to acknowledge the research grant from the regional government of the Comunitat Valenciana (Spain), co-funded by the European Union ERDF funds (European Regional Development Fund) of the Comunitat Valenciana 2014- 2020, with reference IDIFEDER/2018/032 (HighPerformance Algorithms for the Modelling, Simulation and early Detection of diseases in Personalized Medicine). ; Blanquer Espert, I.; Alberich-Bayarri, Á.; García-Castro, F.; Teodoro, G.; Meirelles, A.; Nascimento, B.; Meira Jr., W. (2019). Medical Imaging Processing Architecture on ATMOSPHERE Federated Platform. ScitePress. 589-594. http://hdl.handle.net/10251/181077 ; S ; 589 ; 594

376 394 27 2 ; Brazilian Virtual Herbarium Consortium Brazilian Virtual Herbarium 2013 http://biogeo.inct.florabrasil.net/ ; S Hirzel, A. H., Hausser, J., Chessel, D., & Perrin, N. (2002). ECOLOGICAL-NICHE FACTOR ANALYSIS: HOW TO COMPUTE HABITAT-SUITABILITY MAPS WITHOUT ABSENCE DATA? Ecology, 83(7), 2027-2036. doi:10.1890/0012-9658(2002)083[2027:enfaht]2.0.co;2 ; Brazilian Ministry of Environment Instrução normativa no. 6, 23 de setembro de 2008 2008 http://www.mma.gov.br/estruturas/179/_arquivos/179_05122008033615.pdf ; [EN] EUBrazilOpenBio is a collaborative initiative addressing strategic barriers in biodiversity research by integrating open access data and user-friendly tools widely available in Brazil and Europe. The project deploys the EU-Brazil Hybrid Data Infrastructure that allows the sharing of hardware, software and data on-demand. This infrastructure provides access to several integrated services and resources to seamlessly aggregate taxonomic, biodiversity and climate data, used by processing services implementing checklist cross-mapping and ecological niche modelling. A Virtual Research Environment was created to provide users with a single entry point to processing and data resources. This article describes the architecture, demonstration use cases and some experimental results and validation. EUBrazilOpenBio - Open Data and Cloud Computing e-Infrastructure for Biodiversity (2011-2013) is a Small or medium-scale focused research project (STREP) funded by the European Commission under the Cooperation Programme, Framework Programme Seven (FP7) Objective FP7-ICT-2011- EU-Brazil Research and Development cooperation, and the National Council for Scientific and Technological Development of Brazil (CNPq) of the Brazilian Ministry of Science, Technology and Innovation (MCTI) under the corresponding matching Brazilian Call for proposals MCT/CNPq 066/2010. BSC authors also acknowledge the support of the grant SEV-2011-00067 of Severo Ochoa Program, awarded by the Spanish Government and the Spanish ...

[EN] EUBrazilOpenBio is a collaborative initiative addressing strategic barriers in biodiversity research by integrating open access data and user-friendly tools widely available in Brazil and Europe. The project deploys the EU-Brazil Hybrid Data Infrastructure that allows the sharing of hardware, software and data on-demand. This infrastructure provides access to several integrated services and resources to seamlessly aggregate taxonomic, biodiversity and climate data, used by processing services implementing checklist cross-mapping and ecological niche modelling. A Virtual Research Environment was created to provide users with a single entry point to processing and data resources. This article describes the architecture, demonstration use cases and some experimental results and validation. ; EUBrazilOpenBio - Open Data and Cloud Computing e-Infrastructure for Biodiversity (2011-2013) is a Small or medium-scale focused research project (STREP) funded by the European Commission under the Cooperation Programme, Framework Programme Seven (FP7) Objective FP7-ICT-2011- EU-Brazil Research and Development cooperation, and the National Council for Scientific and Technological Development of Brazil (CNPq) of the Brazilian Ministry of Science, Technology and Innovation (MCTI) under the corresponding matching Brazilian Call for proposals MCT/CNPq 066/2010. BSC authors also acknowledge the support of the grant SEV-2011-00067 of Severo Ochoa Program, awarded by the Spanish Government and the Spanish Ministry of Science and Innovation under contract TIN2012-34557 and the Generalitat de Catalunya (contract 2009-SGR-980). ; Amaral, R.; Badia, RM.; Blanquer Espert, I.; Braga-Neto, R.; Candela, L.; Castelli, D.; Flann, C. (2015). Supporting biodiversity studies with the EUBrazilOpenBio Hybrid Data Infrastructure. Concurrency and Computation: Practice and Experience. 27(2):376-394. https://doi.org/10.1002/cpe.3238 ; S ; 376 ; 394 ; 27 ; 2 ; EUBrazilOpenBio Consortium EU-Brazil Open Data and Cloud Computing e-Infrastructure for ...