Mobile Cloud computing architecture for massively parallelizable geometric computation

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Título: Mobile Cloud computing architecture for massively parallelizable geometric computation
Autor/es: Sánchez-Ribes, Víctor | Mora, Higinio | Sobecki, Andrzej | Mora Gimeno, Francisco José
Grupo/s de investigación o GITE: Arquitecturas Inteligentes Aplicadas (AIA)
Centro, Departamento o Servicio: Universidad de Alicante. Departamento de Tecnología Informática y Computación
Palabras clave: GPU computing | GPU | CUDA | Cloud offloading | Cloud computing
Área/s de conocimiento: Arquitectura y Tecnología de Computadores
Fecha de publicación: dic-2020
Editor: Elsevier
Cita bibliográfica: Computers in Industry. 2020, 123: 103336. https://doi.org/10.1016/j.compind.2020.103336
Resumen: Cloud Computing is one of the most disruptive technologies of this century. This technology has been widely adopted in many areas of the society. In the field of manufacturing industry, it can be used to provide advantages in the execution of the complex geometric computation algorithms involved on CAD/CAM processes. The idea proposed in this research consists in outsourcing part of the load to be computed in the client machines to the cloud through the Mobile Cloud Computing paradigm. This practice gives substantial benefits to both the clients and the software-provider in terms of costs, flexibility, ubiquity and performance. In this document, an outsourcing architecture is proposed based on this paradigm. Extensive experiments have been done using highly parallelizable computational geometry operations to show the strengths and weaknesses of the proposal in combination of specialized computing platforms in the cloud. The results suggest that there are some issues that affect the overall performance and the stability of the QoS: the network communication delay, and the number of simultaneous clients and multiple requests. Some solutions have been proposed to face these challenges.
Patrocinador/es: This work was supported by the Spanish Research Agency (AEI) and the European Regional Development Fund (ERDF) under project CloudDriver4Industry TIN2017-89266-R, and by the Conselleria of Innovation, Universities, Science and Digital Society of the Community of Valencia, Spain, within the program of support for research under project AICO/2020/206.
URI: http://hdl.handle.net/10045/110154
ISSN: 0166-3615 (Print) | 1872-6194 (Online)
DOI: 10.1016/j.compind.2020.103336
Idioma: eng
Tipo: info:eu-repo/semantics/article
Derechos: © 2020 Elsevier B.V.
Revisión científica: si
Versión del editor: https://doi.org/10.1016/j.compind.2020.103336
Aparece en las colecciones:INV - AIA - Artículos de Revistas

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