| Texto completo | |
| Autor(es): |
da Costa, Joahannes B. D.
;
de Souza, Allan M.
;
Lobato, Wellington
;
Rosario, Denis
;
Sommer, Christoph
;
Villas, Leandro
Número total de Autores: 6
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2023 IEEE 98TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2023-FALL; v. N/A, p. 6-pg., 2023-01-01. |
| Resumo | |
Vehicular Edge Computing (VEC) has emerged to offer cloud computing services closer to vehicular users by combining vehicles and edge computing nodes into Vehicular Clouds (VCs). In this scenario, an intelligent task scheduler must decide which VC will run which tasks, considering contextual aspects like vehicular mobility and tasks' requirements. This is important to minimize both processing time and monetary costs. However, such direct optimization can lead to unfairness in resource usage, easily leading to (as we will show) decreased performance. Towards this end, in this work, we propose FARID, a task scheduling mechanism that considers contextual aspects of its decision process and applies a probabilistic selection function on VCs to balance the processing load and increase the fairness in the use of vehicular resources. Compared to state-of-the-art solutions, FARID has a higher level of fairness and can schedule more tasks while minimizing monetary costs and system latency. (AU) | |
| 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: | 19/19105-3 - Nuvem veicular dinâmica para suporte de aplicações para veículos autônomos |
| Beneficiário: | Wellington Viana Lobato Junior |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |