Accelerated serverless computing based on GPU virtualization
RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia
JavaScript is disabled for your browser. Some features of this site may not work without it.
Buscar en RiuNet
Listar
Mi cuenta
Estadísticas
Ayuda RiuNet
Admin. UPV
Accelerated serverless computing based on GPU virtualization
Mostrar el registro sencillo del ítem
Ficheros en el ítem
![[Cerrado]](/themes/UPV/images/candado.png)
| dc.contributor.author | Naranjo-Delgado, Diana María
|
es_ES |
| dc.contributor.author | Risco, Sebastián
|
es_ES |
| dc.contributor.author | Alfonso Laguna, Carlos De
|
es_ES |
| dc.contributor.author | Pérez-González, Alfonso María
|
es_ES |
| dc.contributor.author | Blanquer Espert, Ignacio
|
es_ES |
| dc.contributor.author | Moltó, Germán
|
es_ES |
| dc.date.accessioned | 2021-02-19T04:33:37Z | |
| dc.date.available | 2021-02-19T04:33:37Z | |
| dc.date.issued | 2020-05 | es_ES |
| dc.identifier.issn | 0743-7315 | es_ES |
| dc.identifier.uri | http://hdl.handle.net/10251/161846 | |
| dc.description.abstract | [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. | es_ES |
| dc.description.sponsorship | 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. | es_ES |
| dc.language | Inglés | es_ES |
| dc.publisher | Elsevier | es_ES |
| dc.relation.ispartof | Journal of Parallel and Distributed Computing | es_ES |
| dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
| dc.subject | Serverless computing | es_ES |
| dc.subject | GPUs | es_ES |
| dc.subject | GPU virtualization | es_ES |
| dc.subject.classification | CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL | es_ES |
| dc.title | Accelerated serverless computing based on GPU virtualization | es_ES |
| dc.type | Artículo | es_ES |
| dc.identifier.doi | 10.1016/j.jpdc.2020.01.004 | es_ES |
| dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/777154/EU/Adaptive, Trustworthy, Manageable, Orchestrated, Secure, Privacy-assuring, Hybrid Ecosystem for REsilient Cloud Computing/ | es_ES |
| dc.relation.projectID | info:eu-repo/grantAgreement/MCTIC//51119/BR/Adaptive, Trustworthy, Manageable, Orchestrated, Secure, Privacy-assuring Hybrid, Ecosystem for Resilient Cloud Computing(ATMOSPHERE)/ | es_ES |
| dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//TIN2016-79951-R/ES/COMPUTACION BIG DATA Y DE ALTAS PRESTACIONES SOBRE MULTI-CLOUDS ELASTICOS/ | es_ES |
| dc.relation.projectID | info:eu-repo/grantAgreement/GVA//GRISOLIAP%2F2017%2F071/ | es_ES |
| dc.relation.projectID | info:eu-repo/grantAgreement/GVA//IDIFEDER%2F2018%2FA%2F032/ES/ALGORITMOS DE ALTAS PRESTACIONES PARA EL MODELADO, SIMULACIÓN Y DETECCIÓN TEMPRANA DE ENFERMEDADES EN UN ESCENARIO DE MEDICINA PERSONALIZADA/ | es_ES |
| dc.rights.accessRights | Abierto | es_ES |
| dc.contributor.affiliation | Universitat Politècnica de València. Instituto de Instrumentación para Imagen Molecular - Institut d'Instrumentació per a Imatge Molecular | es_ES |
| dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació | es_ES |
| dc.description.bibliographicCitation | Naranjo-Delgado, DM.; Risco, S.; Alfonso Laguna, CD.; Pérez-González, AM.; Blanquer Espert, I.; Moltó, G. (2020). Accelerated serverless computing based on GPU virtualization. Journal of Parallel and Distributed Computing. 139:32-42. https://doi.org/10.1016/j.jpdc.2020.01.004 | es_ES |
| dc.description.accrualMethod | S | es_ES |
| dc.relation.publisherversion | https://doi.org/10.1016/j.jpdc.2020.01.004 | es_ES |
| dc.description.upvformatpinicio | 32 | es_ES |
| dc.description.upvformatpfin | 42 | es_ES |
| dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
| dc.description.volume | 139 | es_ES |
| dc.relation.pasarela | S\401990 | es_ES |
| dc.contributor.funder | Generalitat Valenciana | es_ES |
| dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
| dc.contributor.funder | European Regional Development Fund | es_ES |
| dc.contributor.funder | European Commission | es_ES |
| dc.contributor.funder | Ministerio da Ciencia, Tecnologia, Inovacoes e Comunicacoes, Brasil | es_ES |
| dc.contributor.funder | Ministerio de Economía y Competitividad | es_ES |
| dc.description.references | De Alfonso, C., Caballer, M., Alvarruiz, F., & Hernández, V. (2013). An energy management system for cluster infrastructures. Computers & Electrical Engineering, 39(8), 2579-2590. doi:10.1016/j.compeleceng.2013.05.004 | es_ES |
| dc.description.references | . Alibaba, Alibaba Cloud Function Compute, URL https://www.alibabacloud.com/products/function-compute. | es_ES |
| dc.description.references | . Amazon, Amazon Web Services (AWS), URL http://aws.amazon.com. | es_ES |
| dc.description.references | . Amazon, Amazon Simple Storage Service (Amazon S3), URL http://aws.amazon.com/s3/. | es_ES |
| dc.description.references | . Amazon Web Services, AWS Lambda, URL https://aws.amazon.com/lambda. | es_ES |
| dc.description.references | . Apache, OpenWhisk, URL https://openwhisk.apache.org/. | es_ES |
| dc.description.references | Apache Mesos, URL http://mesos.apache.org/. | es_ES |
| dc.description.references | Caballer, M., Blanquer, I., Moltó, G., & de Alfonso, C. (2014). Dynamic Management of Virtual Infrastructures. Journal of Grid Computing, 13(1), 53-70. doi:10.1007/s10723-014-9296-5 | es_ES |
| dc.description.references | E. Camacho-Ramos, A. Jimenez-Pastor, I. Blanquer, F. García-Castro, A. Alberich-Bayarri, Computer Aided Diagnosis for Rheumatic Heart Disease by AI Applied to Features Extraction from Echocardiography. | es_ES |
| dc.description.references | A. Ellis, OpenFaaS, URL https://www.openfaas.com/. | es_ES |
| dc.description.references | Fission, URL https://fission.io/. | es_ES |
| dc.description.references | Giménez-Alventosa, V., Moltó, G., & Caballer, M. (2019). A framework and a performance assessment for serverless MapReduce on AWS Lambda. Future Generation Computer Systems, 97, 259-274. doi:10.1016/j.future.2019.02.057 | es_ES |
| dc.description.references | . Google, Knative, URL https://github.com/knative/. | es_ES |
| dc.description.references | . Google, Google Cloud Functions, URL https://cloud.google.com/functions/. | es_ES |
| dc.description.references | . Google, Tensorflow, URL https://www.tensorflow.org/. | es_ES |
| dc.description.references | . IBM, IBM Cloud Functions, URL https://www.ibm.com/cloud/functions. | es_ES |
| dc.description.references | A. Jimenez-Pastor, A. Alberich-Bayarri, F. Garcia-Castro, L. Marti-Bonmati, Automatic visceral fat characterisation on CT scans through deep learning and CNN for the assessment of metabolic syndrome, in: ECR 2019: Book of Abstracts. Insights Into Imaging, Vol. 10 (S1), , 2019. | es_ES |
| dc.description.references | . Keras, Keras, URL https://keras.io/. | es_ES |
| dc.description.references | . Kubernetes, Kubernetes, URL https://kubernetes.io/. | es_ES |
| dc.description.references | . Microsoft, Microsoft Azure Functions, URL https://azure.microsoft.com/en-us/services/functions/. | es_ES |
| dc.description.references | . Microsoft, Microsoft Cognitive Toolkit, URL https://www.microsoft.com/en-us/cognitive-toolkit/. | es_ES |
| dc.description.references | MinIO, URL https://min.io. | es_ES |
| dc.description.references | Nascimento, B. R., Beaton, A. Z., Nunes, M. C. P., Diamantino, A. C., Carmo, G. A. L., Oliveira, K. K. B., … Sable, C. (2016). Echocardiographic prevalence of rheumatic heart disease in Brazilian schoolchildren: Data from the PROVAR study. International Journal of Cardiology, 219, 439-445. doi:10.1016/j.ijcard.2016.06.088 | es_ES |
| dc.description.references | Nuclio, URL https://nuclio.io/. | es_ES |
| dc.description.references | Open Container Initiative, URL https://www.opencontainers.org/. | es_ES |
| dc.description.references | . Oracle, Fn Project, URL https://fnproject.io/. | es_ES |
| dc.description.references | Pérez, A., Moltó, G., Caballer, M., & Calatrava, A. (2018). Serverless computing for container-based architectures. Future Generation Computer Systems, 83, 50-59. doi:10.1016/j.future.2018.01.022 | es_ES |
| dc.description.references | A. Pérez, S. Risco, D.M. Naranjo, M. Caballer, G. Moltó, Serverless computing for event-driven data processing applications, in: 2019 IEEE International Conference on Cloud Computing, CLOUD 2019, , 2019. | es_ES |
| dc.description.references | . Pivotal, Project riff, URL https://projectriff.io/. | es_ES |
| dc.description.references | A.W. Services, API Gateway, URL https://aws.amazon.com/api-gateway. | es_ES |
| dc.description.references | Shi, L., Chen, H., Sun, J., & Li, K. (2012). vCUDA: GPU-Accelerated High-Performance Computing in Virtual Machines. IEEE Transactions on Computers, 61(6), 804-816. doi:10.1109/tc.2011.112 | es_ES |
| dc.description.references | Spillner, J., Mateos, C., & Monge, D. A. (2017). FaaSter, Better, Cheaper: The Prospect of Serverless Scientific Computing and HPC. High Performance Computing, 154-168. doi:10.1007/978-3-319-73353-1_11 | es_ES |
| dc.description.references | Suzuki, Y., Kato, S., Yamada, H., & Kono, K. (2016). GPUvm: GPU Virtualization at the Hypervisor. IEEE Transactions on Computers, 65(9), 2752-2766. doi:10.1109/tc.2015.2506582 | es_ES |
| dc.description.references | Tan, H., Tan, Y., He, X., Li, K., & Li, K. (2019). A Virtual Multi-Channel GPU Fair Scheduling Method for Virtual Machines. IEEE Transactions on Parallel and Distributed Systems, 30(2), 257-270. doi:10.1109/tpds.2018.2865341 | es_ES |
Este ítem aparece en la(s) siguiente(s) colección(ones)
Universitat Politècnica de València. Unidad de Documentación Científica de la Biblioteca (+34) 96 387 70 85 · RiuNet@bib.upv.es
El contenido de este sitio está bajo una licencia Creative Commons Reconocimiento – No Comercial – Sin Obra Derivada (by-nc-nd), salvo que se indique lo contrario.
Los metadatos de este sitio están bajo una licencia Dominio Público.
