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Título: HYBRID CLOUD RENDERING FOR INDUSTRIAL-PLANT CAD MODELS
Autor: ANDRE DE SOUZA MOREIRA
Colaborador(es): WALDEMAR CELES FILHO - Orientador
Catalogação: 14/AGO/2020 Língua(s): ENGLISH - UNITED STATES
Tipo: TEXT Subtipo: THESIS
Notas: [pt] Todos os dados constantes dos documentos são de inteira responsabilidade de seus autores. Os dados utilizados nas descrições dos documentos estão em conformidade com os sistemas da administração da PUC-Rio.
[en] All data contained in the documents are the sole responsibility of the authors. The data used in the descriptions of the documents are in conformity with the systems of the administration of PUC-Rio.
Referência(s): [pt] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=49073&idi=1
[en] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=49073&idi=2
DOI: https://doi.org/10.17771/PUCRio.acad.49073
Resumo:
Industrial-plant CAD models play an important role in engineering project management. Despite the advances in computing power in past decades, rendering these models remains challenging due to their complexity and large data volume. Different areas of computing have succeeded in adopting cloud services to process massive data. However, when it comes to cloud rendering, there is still a lack of cloud rendering services for CAD models. In this paper, we propose a hybrid cloud rendering architecture for CAD models, dividing the rendering task between client and server. In addition to reducing server overhead, this approach affords greater resilience to the system against variations of network latency. Finally, this work also introduces a metaheuristic-based workload selection algorithm to determine the set of objects to be drawn on the client side. Our results demonstrate that the proposed methodology allows efficient visualization of massive CAD models even under adverse conditions such as clients with limited devices and high connection latency. Lastly, we discuss remaining research opportunities for cloud rendering, opening avenues for future improvements.
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