Optimizing machine learning operations in multi-cloud infrastructure: a framework for unified deployment management and topology discovery

Machine learning (ML) transitioned from a purely academic discipline to an applied field, gaining strategic importance in various industries. Meanwhile, Machine Learning Operations (MLOps) has been widely adopted by enterprises as a comprehensive approach for developing and managing machine learning...

ver descrição completa

Detalhes bibliográficos
Autores: Wei,Hao, García Pañeda, Xicu Xabiel|||0000-0001-6381-5459, Rodriguez,J.S.
Tipo de documento: artigo
Data de publicação:2025
País:España
Recursos:Universidad de Oviedo (UNIOVI)
Repositório:RUO. Repositorio Institucional de la Universidad de Oviedo
Idioma:inglês
OAI Identifier:oai:dnet:ruo_________::2c64343a5e3d652adad60b123d1d4beb
Acesso em linha:https://hdl.handle.net/10651/83312
https://dx.doi.org/10.1007/S10586-025-05584-7
Access Level:Acceso aberto
Palavra-chave:Cloud computing
DevOps
MLOps
Policy-base control
id ES_2aa3f97cdcf95b36debd1b501b10d86c
oai_identifier_str oai:dnet:ruo_________::2c64343a5e3d652adad60b123d1d4beb
network_acronym_str ES
network_name_str España
repository_id_str
spelling Optimizing machine learning operations in multi-cloud infrastructure: a framework for unified deployment management and topology discoveryWei,HaoGarcía Pañeda, Xicu Xabiel|||0000-0001-6381-5459Rodriguez,J.S.Cloud computingDevOpsMLOpsPolicy-base controlMachine learning (ML) transitioned from a purely academic discipline to an applied field, gaining strategic importance in various industries. Meanwhile, Machine Learning Operations (MLOps) has been widely adopted by enterprises as a comprehensive approach for developing and managing machine learning applications. Despite its advantages, challenges remain. The rising demand for flexibility and scalability has led organizations to embrace multi-cloud and hybrid cloud architectures as preferred solutions. However, the autonomous and distributed nature of modern application development, combined with the complexity of training and deploying machine learning models, makes unified operational management impractical, and this will further affect application quality and efficiency. To address these challenges, this paper proposes a framework to manage model training and deployment in a multi-cloud environment. This framework uses a policy-based resource provisioning approach, agent-based application topology reconstruction, and a visualization dashboard. It aims to provide a cloud provider-neutral solution that enhances the quality of application operations. The framework design is introduced, followed by the implementation of a proof-of-concept prototype. Experiments conducted in various empirical scenarios demonstrate that the proposed framework effectively manages deployment resources while providing clear visibility and control across multiple clouds. The results confirm that this framework enhances control over deployment resources and optimizes model deployment efficiency in multi-cloud infrastructure.Springer20252025-11-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articlehttps://hdl.handle.net/10651/83312https://dx.doi.org/10.1007/S10586-025-05584-7reponame:RUO. Repositorio Institucional de la Universidad de Oviedoinstname:Universidad de Oviedo (UNIOVI)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:dnet:ruo_________::2c64343a5e3d652adad60b123d1d4beb2026-06-07T06:38:51Z
dc.title.none.fl_str_mv Optimizing machine learning operations in multi-cloud infrastructure: a framework for unified deployment management and topology discovery
title Optimizing machine learning operations in multi-cloud infrastructure: a framework for unified deployment management and topology discovery
spellingShingle Optimizing machine learning operations in multi-cloud infrastructure: a framework for unified deployment management and topology discovery
Wei,Hao
Cloud computing
DevOps
MLOps
Policy-base control
title_short Optimizing machine learning operations in multi-cloud infrastructure: a framework for unified deployment management and topology discovery
title_full Optimizing machine learning operations in multi-cloud infrastructure: a framework for unified deployment management and topology discovery
title_fullStr Optimizing machine learning operations in multi-cloud infrastructure: a framework for unified deployment management and topology discovery
title_full_unstemmed Optimizing machine learning operations in multi-cloud infrastructure: a framework for unified deployment management and topology discovery
title_sort Optimizing machine learning operations in multi-cloud infrastructure: a framework for unified deployment management and topology discovery
dc.creator.none.fl_str_mv Wei,Hao
García Pañeda, Xicu Xabiel|||0000-0001-6381-5459
Rodriguez,J.S.
author Wei,Hao
author_facet Wei,Hao
García Pañeda, Xicu Xabiel|||0000-0001-6381-5459
Rodriguez,J.S.
author_role author
author2 García Pañeda, Xicu Xabiel|||0000-0001-6381-5459
Rodriguez,J.S.
author2_role author
author
dc.subject.none.fl_str_mv Cloud computing
DevOps
MLOps
Policy-base control
topic Cloud computing
DevOps
MLOps
Policy-base control
description Machine learning (ML) transitioned from a purely academic discipline to an applied field, gaining strategic importance in various industries. Meanwhile, Machine Learning Operations (MLOps) has been widely adopted by enterprises as a comprehensive approach for developing and managing machine learning applications. Despite its advantages, challenges remain. The rising demand for flexibility and scalability has led organizations to embrace multi-cloud and hybrid cloud architectures as preferred solutions. However, the autonomous and distributed nature of modern application development, combined with the complexity of training and deploying machine learning models, makes unified operational management impractical, and this will further affect application quality and efficiency. To address these challenges, this paper proposes a framework to manage model training and deployment in a multi-cloud environment. This framework uses a policy-based resource provisioning approach, agent-based application topology reconstruction, and a visualization dashboard. It aims to provide a cloud provider-neutral solution that enhances the quality of application operations. The framework design is introduced, followed by the implementation of a proof-of-concept prototype. Experiments conducted in various empirical scenarios demonstrate that the proposed framework effectively manages deployment resources while providing clear visibility and control across multiple clouds. The results confirm that this framework enhances control over deployment resources and optimizes model deployment efficiency in multi-cloud infrastructure.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-11-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10651/83312
https://dx.doi.org/10.1007/S10586-025-05584-7
url https://hdl.handle.net/10651/83312
https://dx.doi.org/10.1007/S10586-025-05584-7
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:RUO. Repositorio Institucional de la Universidad de Oviedo
instname:Universidad de Oviedo (UNIOVI)
instname_str Universidad de Oviedo (UNIOVI)
reponame_str RUO. Repositorio Institucional de la Universidad de Oviedo
collection RUO. Repositorio Institucional de la Universidad de Oviedo
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869405085360979968
score 15,812455

Registros relacionados