Artificial intelligence-based Kubernetes container for scheduling nodes of energy composition

dc.creatorDiaz Martinez, Jorge Luis
dc.creatorAziz Butt, Shariq
dc.creatorMichael Onyema, Edeh
dc.creatorChakraborty, Dr. Chinmay
dc.creatorShaheen, Qaisar
dc.creatorDe-La-Hoz-Franco, Emiro
dc.creatorAriza Colpas, Paola Patricia
dc.date2021-08-24T16:57:13Z
dc.date2021-08-24T16:57:13Z
dc.date2021-07-17
dc.date2022-07-17
dc.date.accessioned2023-10-03T19:12:07Z
dc.date.available2023-10-03T19:12:07Z
dc.identifier09756809
dc.identifierhttps://hdl.handle.net/11323/8586
dc.identifierhttps://doi.org/10.1007/s13198-021-01195-8
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9168824
dc.descriptionKubernetes is a portable, extensible, open-source platform for managing containerized workloads and services that facilitates both declarative configuration and automation. This study presents Kubernetes Container Scheduling Strategy (KCSS) based on Artificial Intelligence (AI) that can assist in decision making to control the scheduling and shifting of load to nodes. The aim is to improve the container’s schedule requested digitally from users to enhance the efficiency in scheduling and reduce cost. The constraints associated with the existing container scheduling techniques which often assign a node to every new container based on a personal criterion by relying on individual terms has been greatly improved by the new system presented in this study. The KCSS presented in this study provides multicriteria node selection based on artificial intelligence in terms of decision making systems thereby giving the scheduler a broad picture of the cloud's condition and the user's requirements. AI Scheduler allows users to easily make use of fractional Graphics Processing Units (GPUs), integer GPUs, and multiple-nodes of GPUs, for distributed training on Kubernetes. © 2021, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherInternational Journal of Systems Assurance Engineering and Management
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dc.rightsCC0 1.0 Universal
dc.rightshttp://creativecommons.org/publicdomain/zero/1.0/
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.rightshttp://purl.org/coar/access_right/c_f1cf
dc.sourceInternational Journal of Systems Assurance Engineering and Management
dc.sourcehttps://link.springer.com/article/10.1007%2Fs13198-021-01195-8
dc.subjectArtificial intelligence
dc.subjectAutomated scheduling
dc.subjectCloud infrastructure
dc.subjectKubernetes
dc.subjectMulti-criteria scheduler
dc.subjectScheduling strategy
dc.titleArtificial intelligence-based Kubernetes container for scheduling nodes of energy composition
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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