dacosta2026felicia
Abstract
Modern vehicles generate large volumes of data and support latency-sensitive applications that require real-time or near-real-time processing. Vehicular Edge Computing (VEC) addresses this demand by aggregating computational resources from vehicles and edge infrastructure into Vehicular Clouds (VCs). However, task scheduling in VEC remains challenging due to factors such as vehicular mobility, deadline constraints, heterogeneous resources, and the need to balance performance and fairness in resource utilization. To address these challenges, this paper proposes FELICIA, a multiobjective task scheduling mechanism for VEC environments. FELICIA runs on VEC controllers and employs a multiobjective optimization algorithm to generate scheduling solutions that maximize the number of scheduled tasks and fairness in resource utilization while minimizing resource usage costs. The algorithm organizes candidate solutions into a Pareto front and selects feasible solutions by considering contextual information such as resource variability and task requirements. In addition, FELICIA incorporates a load balancing strategy to ensure fair utilization of computational resources. Experimental results using realistic vehicular mobility traces demonstrate that FELICIA schedules more tasks, reduces monetary cost and system latency, and achieves better load balancing than state-of-the-art approaches.
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Contact
- Joahannes B. D. da Costa
- Wellington Viana Lobato Junior
- Allan Mariano de Souza
- Denis Lima do Rosário
- Christoph Sommer
- Leandro Aparecido Villas
BibTeX reference
@article{dacosta2026felicia,
author = {da Costa, Joahannes B. D. and Lobato Junior, Wellington Viana and de Souza, Allan Mariano and do Ros{\'{a}}rio, Denis Lima and Sommer, Christoph and Aparecido Villas, Leandro},
title = {{FELICIA: Fair and efficient task scheduling mechanism for Vehicular Edge Computing}},
journal = {Elsevier Vehicular Communications},
doi = {10.1016/j.vehcom.2026.101089},
issn = {2214-2096},
publisher = {Elsevier},
year = {2026},
}
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