2026-04-14T17:43:25Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/623662025-02-07T13:55:20Zcom_10324_1191com_10324_931com_10324_894col_10324_1381

Ferens Michalek, Mieszko Jan Hortelano Haro, Diego Miguel Jiménez, Ignacio de Durán Barroso, Ramón José Aguado Manzano, Juan Carlos Ruiz Pérez, Lidia Merayo Álvarez, Noemí Fernández Reguero, Patricia Lorenzo Toledo, Rubén Mateo Abril Domingo, Evaristo José 2023-10-26T10:05:40Z 2023-10-26T10:05:40Z 2022 2022 International Balkan Conference on Communications and Networking (BalkanCom), Sarajevo, Bosnia and Herzegovina, 2022, pp. 31-35 https://uvadoc.uva.es/handle/10324/62366 10.1109/BalkanCom55633.2022.9900545 Producción Científica An observable trend in recent years is the increasing demand for more complex services designed to be used with portable or automotive embedded devices. The problem is that these devices may lack the computational resources necessary to comply with service requirements. To solve it, cloud and edge computing, and in particular, the recent multi-access edge computing (MEC) paradigm, have been proposed. By offloading the processing of computational tasks from devices or vehicles to an external network, a larger amount of computational resources, placed in different locations, becomes accessible. However, this in turn creates the issue of deciding where each task should be executed. In this paper, we model the problem of computation offloading of vehicular applications to solve it using deep reinforcement learning (DRL) and evaluate the performance of different DRL algorithms and heuristics, showing the advantages of the former methods. Moreover, the impact of two scheduling techniques in computing nodes and two reward strategies in the DRL methods are also analyzed and discussed. Consejería de Educación de la Junta de Castilla y León y FEDER (VA231P20) Ministerio de Ciencia e Innovación y Agencia Estatal de Investigación (Proyecto PID2020-112675RB-C42 financiado por MCIN/AEI/10.13039/501100011033 y RED2018-102585-T); EU H2020 GA no. 856967. 5 p. application/pdf eng Institute of Electrical and Electronics Engineers (IEEE) info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional Deep Reinforcement Learning Vehicular Applications Computation Offloading Edge Computing Deep Reinforcement Learning Applied to Computation Offloading of Vehicular Applications: A Comparison 2022 International Balkan Conference on Communications and Networking (BalkanCom) info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/acceptedVersion https://ieeexplore.ieee.org/abstract/document/9900545