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dc.contributor.authorTervydis, Paulius-
dc.contributor.authorSvilainis, Linas-
dc.contributor.authorNakutis, Žilvinas-
dc.contributor.authorRodríguez-Martínez, Alberto-
dc.contributor.otherDepartamentos de la UMH::Ingeniería de Comunicacioneses_ES
dc.date.accessioned2025-01-08T09:39:09Z-
dc.date.available2025-01-08T09:39:09Z-
dc.date.created2024-05-
dc.identifier.citationSoftwareX, Volume 26, May 2024es_ES
dc.identifier.issn2352-7110-
dc.identifier.urihttps://hdl.handle.net/11000/34212-
dc.description.abstractCloudEdgeAssetsOptimizer is a software tool designed to evaluate and optimize the assets within a Cloud and Edge computing network. Its primary purpose is to provide insights and estimations to ensure efficient resource allocation and decision-making. The developed software simulates the queues in Cloud and Edge devices, providing waiting times, battery consumption of Edge devices, and Servers’ load. With aforementioned parameters available, valuable insights for decision-making can be obtained to optimize the network. Alternatively, automated optimization can be performed using the embedded functions. CloudEdgeAssetsOptimizer aims to optimize the operational efficiency, enhance resource utilization, and ultimately improve the overall system performance. CloudEdgeAssetsOptimizer functions utilize the queueing theory principles, therefore it also enables users to explore and fine-tune the system parameters across diverse domains, including telecommunication networks, transportation and manufacturing systems.es_ES
dc.formatapplication/pdfes_ES
dc.format.extent10es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectEdgees_ES
dc.subjectCloud computinges_ES
dc.subjectLoad balancinges_ES
dc.subjectOptimizationes_ES
dc.subject.otherCDU::6 - Ciencias aplicadas::62 - Ingeniería. Tecnologíaes_ES
dc.titleCloudEdgeAssetOptimizer: Tool to optimize the Cloud-Edge computing network resources at given requirements of processing delay, battery capacity and costes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://doi.org/10.1016/j.softx.2024.101714es_ES
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