A Context-Oriented Framework for Computation Offloading in Vehicular Edge Computing using WAVE and 5G Networks

Autor: Tiago Carneiro, Paulo Henrique Goncalves Rocha, Alisson Barbosa de Souza, José Neuman de Souza, Paulo A. L. Rego
Přispěvatelé: Universidade Federal do Ceará = Federal University of Ceará (UFC), Optimisation de grande taille et calcul large échelle (BONUS), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), University of Luxembourg [Luxembourg]
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Vehicular Communications
Vehicular Communications, 2021, ⟨10.1016/j.vehcom.2021.100389⟩
Vehicular Communications, Elsevier, 2021, ⟨10.1016/j.vehcom.2021.100389⟩
ISSN: 2214-2096
2214-210X
DOI: 10.1016/j.vehcom.2021.100389⟩
Popis: International audience; Despite technological advances, vehicles are still unable to meet the demands of some applications for massive computational resources in a feasible time. One way to deal with this situation is to integrate the computation offloading technique into a vehicular edge computing system. This integration allows application tasks to be executed on neighboring vehicles or edge servers coupled to base stations. However, the dynamic nature of vehicular networks, allied to over- loaded servers, can lead to failures and reduce the effectiveness of the offloading technique. Therefore, we propose a context-oriented framework for computation offloading to reduce the application execution time and maintain high reliabilityin vehicular edge computing. The framework modules perform computational resources discovery, contextual data gathering, computation tasks distribution, and failure recovery. Its main part is a task assignment algorithm that seeks the best possible server to execute each application task, using contextual information and WAVE and 5G networks. The results of extensive experiments in different vehicular environments show that our framework reduces up to 70.3% of total execution time compared to totally local execution and up to 42.9% compared to other literature approaches. Concerning reliability, our framework achieves to offload up to 89.4% of all tasks and needs to recover only 0.8% of them. Thus, our solution outperforms the totally local execution of the application and other existing computation offloading solutions.
Databáze: OpenAIRE
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