Minimizing active nodes in MEC environments: a distributed learning-driven framework for application placement

© 2025 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

Detalles Bibliográficos
Autores: Torres Pérez, Claudia|||0000-0002-4386-9318, Coronado Calero, Estefanía, Cervelló Pastor, Cristina|||0000-0002-8056-0774, Palomares, Javier, Carmona Cejudo, Estela, Siddiqui, Shuaib
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/426649
Acceso en línea:https://hdl.handle.net/2117/426649
https://dx.doi.org/10.1016/j.comnet.2024.111008
Access Level:acceso embargado
Palabra clave:Application placement
Distributed deep reinforcement learning
MEC
Scalability
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
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spelling Minimizing active nodes in MEC environments: a distributed learning-driven framework for application placementTorres Pérez, Claudia|||0000-0002-4386-9318Coronado Calero, EstefaníaCervelló Pastor, Cristina|||0000-0002-8056-0774Palomares, JavierCarmona Cejudo, EstelaSiddiqui, ShuaibApplication placementDistributed deep reinforcement learningMECScalabilityÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors© 2025 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Application placement in Multi-Access Edge Computing (MEC) must adhere to service level agreements (SLAs), minimize energy consumption, and optimize metrics based on specific service requirements. In distributed MEC system environments, the placement problem also requires consideration of various types of applications with different entry distribution rates and requirements, and the incorporation of varying numbers of hosts to enable the development of a scalable system. One possible way to achieve these objectives is to minimize the number of active nodes in order to avoid resource fragmentation and unnecessary energy consumption. This paper presents a Distributed Deep Reinforcement Learning-based Capacity-Aware Application Placement (DDRL-CAAP) approach aimed at reducing the number of active nodes in a multi-MEC system scenario that is managed by several orchestrators. Internet of Things (IoT) and Extended Reality (XR) applications are considered in order to evaluate close-to-real-world environments via simulation and on a real testbed. The proposed design is scalable for different numbers of nodes, MEC systems, and vertical applications. The performance results show that DDRL-CAAP achieves an average improvement of 98.3% in inference time compared with the benchmark Integer Linear Programming (ILP) algorithm, and a mean reduction of 4.35% in power consumption compared with a Random Selection (RS) algorithm.This work has been performed in the framework of the European Union’s H2020 project AI@EDGE, co-funded by the EU under grant agreement No 101015922. The authors would like to acknowledge CERCA Programme/ Generalitat de Catalunya for sponsoring part of this work. This work has also been supported by the EU “NextGenerationEU/PRTR”, MCIN, by AEI (Spain) under project IJC2020-043058-I, by MCIN/AEI/10.13039/501100011033 (FEDER “a way of making Europe”) under grant PID2022-142332OA-I00, and by the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union– NextGeneration EU, in the framework of the Recovery Plan, Transformation and Resilience (PRTR) (Call UNICO I+D 5G 2021, ref. number TSI-063000-2021-9-6GSMART-ICC). This work was also supported by the grant ONOFRE-3 PID2020-112675RB-C43, funded by MCIN/AEI/10.13039/501100011033.Peer Reviewed20252025-02-0120252025-03-1920272027-02-01journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/426649https://dx.doi.org/10.1016/j.comnet.2024.111008reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengEuropean Commission http://doi.org/10.13039/100010661 Horizon 2020 Framework Programme 101015922 A secure and reusable Artificial Intelligence platform for Edge computing in beyond 5G Networksembargoed accesshttp://purl.org/coar/access_right/c_f1cfAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/embargoedAccessoai:upcommons.upc.edu:2117/4266492026-05-27T15:37:01Z
dc.title.none.fl_str_mv Minimizing active nodes in MEC environments: a distributed learning-driven framework for application placement
title Minimizing active nodes in MEC environments: a distributed learning-driven framework for application placement
spellingShingle Minimizing active nodes in MEC environments: a distributed learning-driven framework for application placement
Torres Pérez, Claudia|||0000-0002-4386-9318
Application placement
Distributed deep reinforcement learning
MEC
Scalability
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
title_short Minimizing active nodes in MEC environments: a distributed learning-driven framework for application placement
title_full Minimizing active nodes in MEC environments: a distributed learning-driven framework for application placement
title_fullStr Minimizing active nodes in MEC environments: a distributed learning-driven framework for application placement
title_full_unstemmed Minimizing active nodes in MEC environments: a distributed learning-driven framework for application placement
title_sort Minimizing active nodes in MEC environments: a distributed learning-driven framework for application placement
dc.creator.none.fl_str_mv Torres Pérez, Claudia|||0000-0002-4386-9318
Coronado Calero, Estefanía
Cervelló Pastor, Cristina|||0000-0002-8056-0774
Palomares, Javier
Carmona Cejudo, Estela
Siddiqui, Shuaib
author Torres Pérez, Claudia|||0000-0002-4386-9318
author_facet Torres Pérez, Claudia|||0000-0002-4386-9318
Coronado Calero, Estefanía
Cervelló Pastor, Cristina|||0000-0002-8056-0774
Palomares, Javier
Carmona Cejudo, Estela
Siddiqui, Shuaib
author_role author
author2 Coronado Calero, Estefanía
Cervelló Pastor, Cristina|||0000-0002-8056-0774
Palomares, Javier
Carmona Cejudo, Estela
Siddiqui, Shuaib
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Application placement
Distributed deep reinforcement learning
MEC
Scalability
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
topic Application placement
Distributed deep reinforcement learning
MEC
Scalability
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
description © 2025 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-02-01
2025
2025-03-19
2027
2027-02-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/426649
https://dx.doi.org/10.1016/j.comnet.2024.111008
url https://hdl.handle.net/2117/426649
https://dx.doi.org/10.1016/j.comnet.2024.111008
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission http://doi.org/10.13039/100010661 Horizon 2020 Framework Programme 101015922 A secure and reusable Artificial Intelligence platform for Edge computing in beyond 5G Networks
dc.rights.none.fl_str_mv embargoed access
http://purl.org/coar/access_right/c_f1cf
Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/embargoedAccess
rights_invalid_str_mv embargoed access
http://purl.org/coar/access_right/c_f1cf
Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
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