| Texto completo | |
| Autor(es): |
Da Costa, Joahannes B. D.
;
de Souza, Allan M.
;
Rosario, Denis
;
Sommer, Christoph
;
Villas, Leandro A.
;
IEEE
Número total de Autores: 6
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2022 IEEE 96TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2022-FALL); v. N/A, p. 6-pg., 2022-01-01. |
| Resumo | |
Vehicular Edge Computing is a promising paradigm that provides cloud computing services closer to vehicular users. Vehicles and communication infrastructure can cooperatively provide vehicular services with low latency constraints through vehicular cloud formation and using these computational resources via task scheduling. An efficient task scheduler must decide which cloud will run the tasks, considering vehicular mobility and task requirements. This is important to minimize processing time and, consequently, monetary cost. However, the literature solutions do not consider these contextual aspects together, degrading the overall system efficiency. This work presents EFESTO, a task scheduling mechanism that considers contextual aspects in its decision process. The results show that, compared to state-of-the-art solutions, EFESTO can schedule more tasks while minimizing monetary cost and system latency. (AU) | |
| Processo FAPESP: | 21/13780-0 - Controle tolerante a falhas para computação de borda veicular |
| Beneficiário: | Joahannes Bruno Dias da Costa |
| Modalidade de apoio: | Bolsas no Exterior - Estágio de Pesquisa - Doutorado |
| Processo FAPESP: | 18/16703-4 - Computação em nuvem veicular para gerenciamento de informação em sistemas de transporte inteligentes |
| Beneficiário: | Joahannes Bruno Dias da Costa |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |
| Processo FAPESP: | 15/24494-8 - Comunicação e processamento de big data em nuvens e névoas computacionais |
| Beneficiário: | Nelson Luis Saldanha da Fonseca |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |