GSaaS: A service to cloudify and schedule GPUs
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Otros documentos de la autoría: Iserte, Sergio; Peña-Ortiz, Raúl; Gutiérrez-Aguado, Juan; Claver, José M.; Mayo, Rafael
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/8620
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Título
GSaaS: A service to cloudify and schedule GPUsFecha de publicación
2018-07Editor
IEEECita bibliográfica
ISERTE, Sergio, et al. GSaaS: A Service to Cloudify and Schedule GPUs. IEEE Access, 2018, 6: 39762-39774.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://ieeexplore.ieee.org/abstract/document/8410512Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Cloud technology is an attractive infrastructure solution that provides customers with an almost unlimited on-demand computational capacity using a pay-per-use approach, and allows data centers to increase their energy ... [+]
Cloud technology is an attractive infrastructure solution that provides customers with an almost unlimited on-demand computational capacity using a pay-per-use approach, and allows data centers to increase their energy and economic savings by adopting a virtualized resource sharing model. However, resources such as graphics processing units (GPUs), have not been fully adapted to this model. Although, general-purpose computing on graphics processing units (GPGPU) is becoming more and more popular, cloud providers lack of flexibility to manage accelerators, because of the extended use of peripheral component interconnect (PCI) passthrough techniques to attach GPUs to virtual machines (VMs). For this reason, we design, develop, and evaluate a service that provides a complete management of cloudified GPUs (cGPUs) in public cloud platforms. Our solution enables an effective, anonymous, and transparent access from VMs to cGPUs that are previously scheduled and assigned by a full resource manager, taking into account new GPU selection policies and new working modes based on the locality of the physical accelerators and the exclusivity when accessing them. This easy-to-adopt tool improves the resource availability through different cGPUs configurations for end-users, whilst cloud providers are able to achieve a better utilization of their infrastructures and offer more competitive services. Scalability results in a real cloud environment demonstrate that our solution introduces a virtually null overhead in the deployment of VMs. Besides, performance experiments reveal that GPU-enabled clusters based on cloud infrastructures can benefit from our proposal not only exploiting better the accelerators, but also serving more jobs requests per unit of time. [-]
Proyecto de investigación
MINECO (TIN2014-53495-R) ; FEDER (TIN2017-82972-R)Derechos de acceso
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