Deep Reinforcement Learning techniques for dynamic task offloading in the 5G edge-cloud continuum
dc.contributor.author | Nieto, Gorka | |
dc.contributor.author | De la Iglesia, Idoia | |
dc.contributor.author | López Novoa, Unai | |
dc.contributor.author | Perfecto del Amo, Cristina Begoña | |
dc.date.accessioned | 2024-09-17T17:21:44Z | |
dc.date.available | 2024-09-17T17:21:44Z | |
dc.date.issued | 2024-05-03 | |
dc.identifier.citation | Journal of Cloud Computing 13 : (2024) // Article ID 94 | es_ES |
dc.identifier.issn | 2192-113X | |
dc.identifier.uri | http://hdl.handle.net/10810/69489 | |
dc.description.abstract | The integration of new Internet of Things (IoT) applications and services heavily relies on task offloading to external devices due to the constrained computing and battery resources of IoT devices. Up to now, Cloud Computing (CC) paradigm has been a good approach for tasks where latency is not critical, but it is not useful when latency matters, so Multi-access Edge Computing (MEC) can be of use. In this work, we propose a distributed Deep Reinforcement Learning (DRL) tool to optimize the binary task offloading decision, this is, the independent decision of where to execute each computing task, depending on many factors. The optimization goal in this work is to maximize the Quality-of-Experience (QoE) when performing tasks, which is defined as a metric related to the battery level of the UE, but subject to satisfying tasks’ latency requirements. This distributed DRL approach, specifically an Actor-Critic (AC) algorithm running on each User Equipment (UE), is evaluated through the simulation of two distinct scenarios and outperforms other analyzed baselines in terms of QoE values and/or energy consumption in dynamic environments, also demonstrating that decisions need to be adapted to the environment’s evolution. | es_ES |
dc.description.sponsorship | This work was partially supported by the Basque Government under the Elkartek funding program through the project SONETO: La red social de los Activos (grant KK-2023/00038) and the Spanish Ministerio de Asuntos Económicos y Transformación Digital and the European Union NextGenerationEU through the project LocoForge: Mimbres instantiation for railways and Industry 5.0 vertical sectors (grant TSI-063000- 2021-47), funded by the Plan for Recovery, ransformation and Resilience. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Springer | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.subject | task offloading | es_ES |
dc.subject | performance evaluation | es_ES |
dc.subject | energy consumption | es_ES |
dc.subject | reinforcement Learning | es_ES |
dc.title | Deep Reinforcement Learning techniques for dynamic task offloading in the 5G edge-cloud continuum | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | es_ES |
dc.relation.publisherversion | https://journalofcloudcomputing.springeropen.com/articles/10.1186/s13677-024-00658-0 | es_ES |
dc.identifier.doi | 10.1186/s13677-024-00658-0 | |
dc.departamentoes | Ingeniería de comunicaciones | es_ES |
dc.departamentoes | Lenguajes y sistemas informáticos | es_ES |
dc.departamentoeu | Hizkuntza eta sistema informatikoak | es_ES |
dc.departamentoeu | Komunikazioen ingeniaritza | es_ES |
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Except where otherwise noted, this item's license is described as © The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.