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Attention-based model and deep reinforcement learning for distribution of event processing tasks

2022-07Journal article Open Access
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dc.contributor.authorMazayev, Andriy
dc.contributor.authorAl-Tam, Faroq
dc.contributor.authorCorreia, Noélia
dc.date.accessioned2022-12-06T14:30:18Z
dc.date.available2022-12-06T14:30:18Z
dc.date.issued2022-07
dc.description.abstractEvent processing is the cornerstone of the dynamic and responsive Internet of Things (IoT). Recent approaches in this area are based on representational state transfer (REST) principles, which allow event processing tasks to be placed at any device that follows the same principles. However, the tasks should be properly distributed among edge devices to ensure fair resources utilization and guarantee seamless execution. This article investigates the use of deep learning to fairly distribute the tasks. An attention-based neural network model is proposed to generate efficient load balancing solutions under different scenarios. The proposed model is based on the Transformer and Pointer Network architectures, and is trained by an advantage actorcritic reinforcement learning algorithm. The model is designed to scale to the number of event processing tasks and the number of edge devices, with no need for hyperparameters re-tuning or even retraining. Extensive experimental results show that the proposed model outperforms conventional heuristics in many key performance indicators. The generic design and the obtained results show that the proposed model can potentially be applied to several other load balancing problem variations, which makes the proposal an attractive option to be used in real-world scenarios due to its scalability and efficiency.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.iot.2022.100563pt_PT
dc.identifier.eissn2542-6605
dc.identifier.urihttp://hdl.handle.net/10400.1/18589
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationCenter for Electronics, Optoelectronics and Telecommunications
dc.relationEdge assisted IoT orchestration
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectWeb of Things (WoT)pt_PT
dc.subjectRepresentational state transfer (REST)pt_PT
dc.subjectApplication programming interface (APIs)pt_PT
dc.subjectEdge computingpt_PT
dc.subjectLoad balancingpt_PT
dc.subjectResource placementpt_PT
dc.subjectDeep reinforcement leaningpt_PT
dc.subjectTransformer modelpt_PT
dc.subjectPointer networkspt_PT
dc.subjectActor criticpt_PT
dc.titleAttention-based model and deep reinforcement learning for distribution of event processing taskspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleCenter for Electronics, Optoelectronics and Telecommunications
oaire.awardTitleEdge assisted IoT orchestration
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00631%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT//SFRH%2FBD%2F138836%2F2018/PT
oaire.citation.startPage100563pt_PT
oaire.citation.titleInternet of Thingspt_PT
oaire.citation.volume19pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameMazayev
person.familyNameCorreia
person.givenNameAndriy
person.givenNameNoélia
person.identifierR-00G-XFD
person.identifierR-000-DJV
person.identifier.ciencia-idD91F-F08D-5380
person.identifier.ciencia-idDD19-1F35-B804
person.identifier.orcid0000-0003-0495-9801
person.identifier.orcid0000-0001-7051-7193
person.identifier.ridM-3554-2013
person.identifier.scopus-author-id56565451000
person.identifier.scopus-author-id8411596100
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication5bc05e4c-cb79-4954-87e8-ebacb4473268
relation.isAuthorOfPublicationfdbe5057-0478-46cd-9506-caa73ea79d9f
relation.isAuthorOfPublication.latestForDiscovery5bc05e4c-cb79-4954-87e8-ebacb4473268
relation.isProjectOfPublication6c1217d9-1340-45e8-91d8-e75348854f62
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