dacosta2023improving
Abstract
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.
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Contact
- Joahannes B. D. da Costa
- Allan Mariano de Souza
- Wellington Viana Lobato Junior
- Denis Rosário
- Christoph Sommer
- Leandro Aparecido Villas
BibTeX reference
@inproceedings{dacosta2023improving,
author = {da Costa, Joahannes B. D. and de Souza, Allan Mariano and Lobato Junior, Wellington Viana and Ros{\'{a}}rio, Denis and Sommer, Christoph and Aparecido Villas, Leandro},
title = {{Improving Fairness and Performance in Resource Usage for Vehicular Edge Computing}},
booktitle = {98th IEEE Vehicular Technology Conference (VTC 2023-Fall)},
address = {Hong Kong, China},
doi = {10.1109/VTC2023-Fall60731.2023.10333418},
month = {October},
publisher = {IEEE},
year = {2023},
}
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