Connection-Aware Heuristics for Scheduling and Distributing Jobs under Dynamic Dew Computing Environments

dc.article.number3206
dc.catalogadorgjm
dc.contributor.authorSanabria Quispe, Pablo
dc.contributor.authorMontoya Tapia, Sebastián Ignacio
dc.contributor.authorNeyem, Andrés
dc.contributor.authorToro Icarte, Rodrigo Andrés
dc.contributor.authorHirsch, Matías
dc.contributor.authorMateos, Cristian
dc.date.accessioned2024-04-15T14:59:15Z
dc.date.available2024-04-15T14:59:15Z
dc.date.issued2024
dc.description.abstractDue to the widespread use of mobile and IoT devices, coupled with their continually expanding processing capabilities, dew computing environments have become a significant focus for researchers. These environments enable resource-constrained devices to contribute computing power to a local network. One major challenge within these environments revolves around task scheduling, specifically determining the optimal distribution of jobs across the available devices in the network. This challenge becomes particularly pronounced in dynamic environments where network conditions constantly change. This work proposes integrating the “reliability” concept into cutting-edge human-design job distribution heuristics named ReleSEAS and RelBPA as a means of adapting to dynamic and ever-changing network conditions caused by nodes’ mobility. Additionally, we introduce a reinforcement learning (RL) approach, embedding both the notion of reliability and real-time network status into the RL agent. Our research rigorously contrasts our proposed algorithms’ throughput and job completion rates with their predecessors. Simulated results reveal a marked improvement in overall throughput, with our algorithms potentially boosting the environment’s performance. They also show a significant enhancement in job completion within dynamic environments compared to baseline findings. Moreover, when RL is applied, it surpasses the job completion rate of human-designed heuristics. Our study emphasizes the advantages of embedding inherent network characteristics into job distribution algorithms for dew computing. Such incorporation gives them a profound understanding of the network’s diverse resources. Consequently, this insight enables the algorithms to manage resources more adeptly and effectively.
dc.fechaingreso.objetodigital2024-04-15
dc.format.extent22 páginas
dc.fuente.origenORCID
dc.identifier.doi10.3390/app14083206
dc.identifier.urihttps://doi.org/10.3390/app14083206
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/85118
dc.identifier.wosidWOS:001210338700001
dc.information.autorucEscuela de Ingeniería; Sanabria Quispe, Pablo; 0000-0001-6493-3895; 212656
dc.information.autorucEscuela de Ingeniería; Montoya Tapia, Sebastián Ignacio; 0009-0005-8359-6341; 1025947
dc.information.autorucEscuela de Ingeniería; Neyem, Andrés; 0000-0002-5734-722X; 1007638
dc.information.autorucEscuela de Ingeniería; Toro Icarte, Rodrigo Andrés; 0000-0002-7734-099X; 170373
dc.issue.numero8
dc.language.isoen
dc.nota.accesocontenido completo
dc.revistaApplied Sciences
dc.rightsacceso abierto
dc.rights.licenseCC BY 4.0 DEED Attribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectDew computing
dc.subjectReinforcement learning
dc.subjectConnection-aware scheduling
dc.subjectMobility models
dc.subjectHeuristics
dc.subjectTransfer learning
dc.subjectSimulation
dc.subject.ddc000
dc.subject.deweyCiencias de la computaciónes_ES
dc.subject.ods03 Good health and well-being
dc.subject.ods11 Sustainable cities and communities
dc.subject.odspa03 Salud y bienestar
dc.subject.odspa11 Ciudades y comunidades sostenibles
dc.titleConnection-Aware Heuristics for Scheduling and Distributing Jobs under Dynamic Dew Computing Environments
dc.typeartículo
dc.volumen14
sipa.codpersvinculados212656
sipa.codpersvinculados1025947
sipa.codpersvinculados1007638
sipa.codpersvinculados170373
sipa.trazabilidadORCID;2024-04-15
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