A skewness-aware matrix factorization approach for mesh-structured cloud services
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to se...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/172372 |
| Acceso en línea: | https://hdl.handle.net/2117/172372 https://dx.doi.org/10.1109/TNET.2019.2923815 |
| Access Level: | acceso abierto |
| Palabra clave: | Cloud computing Web services Service mesh Matrix factorization Skewness Latent factor model Residual learning Computació en núvol Serveis web Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors::Arquitectures distribuïdes |
| Sumario: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
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