GSaaS: A Service to Cloudify and Schedule GPUs
| Autor: | Rafael Mayo, Juan Gutierrez-Aguado, Sergio Iserte, Raúl Peña-Ortiz, José M. Claver |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Schedule General Computer Science Computer science Distributed computing networking Cloud computing 02 engineering and technology computer.software_genre 03 medical and health sciences GPU resource management 020204 information systems 0202 electrical engineering electronic engineering information engineering General Materials Science Resource management platform virtualization business.industry cloud computing General Engineering Virtualization Shared resource 030104 developmental biology Virtual machine Scalability GPU cloudification lcsh:Electrical engineering. Electronics. Nuclear engineering General-purpose computing on graphics processing units business computer lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 6, Pp 39762-39774 (2018) Repositori Universitat Jaume I Universitat Jaume I |
| ISSN: | 2169-3536 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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