Mobility and Deadline-Aware Task Scheduling Mechanism for Vehicular Edge Computing

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung



Vehicular Edge Computing (VEC) is a promising paradigm that provides cloud computing services closer to vehicular users. In VEC, vehicles and communication infrastructures can form pools with computational resources to meet vehicular services with low-latency constraints. These resource pools are known as Vehicular Cloud (VC). The usage of VC resources requires a task scheduling process. In this case, depending on its complexity, a vehicular service can be divided into different tasks. An efficient task scheduling needs to orchestrate where and for how long such tasks will run, considering the available pools, the mobility of nodes, and the tasks deadline constraints. Thus, this article proposes an efficient VC task scheduler based on an approximation heuristic and resources prediction to select the best VC for each task, called MARINA. MARINA aims to analyze the behavior of vehicles that share their computational resources with the VC and make scheduling decisions based on the mobility (VC availability) of these vehicles. Simulation results under a realistic scenario demonstrate the efficiency of MARINA compared to existing state-of-the-art mechanisms in terms of the number of tasks scheduled, monetary cost, system latency, and Central Processing Unit (CPU) utilization.


Seiten (von - bis)11345-11359
FachzeitschriftIEEE Transactions on Intelligent Transportation Systems
PublikationsstatusVeröffentlicht - 31 Mai 2023

Externe IDs

Bibtex nsm-dacosta2023mobility
unpaywall 10.1109/tits.2023.3276823
Scopus 85161074097
WOS 001006785700001


Forschungsprofillinien der TU Dresden

Fächergruppen, Lehr- und Forschungsbereiche, Fachgebiete nach Destatis


  • Vehicular edge computing, recurrent neural network, resource prediction, task scheduling, Cloud computing, Schedules, Costs, Resource prediction, Task scheduling, Task analysis, Vehicle dynamics, Edge computing, Processor scheduling, Recurrent neural network