dc.creator | Fernández Cerero, Damián | es |
dc.creator | Troyano Jiménez, José Antonio | es |
dc.creator | Jakóbik, Agnieszka | es |
dc.creator | Fernández Montes González, Alejandro | es |
dc.date.accessioned | 2022-07-04T09:13:19Z | |
dc.date.available | 2022-07-04T09:13:19Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Fernández Cerero, D., Troyano Jiménez, J.A., Jakóbik, A. y Fernández Montes González, A. (2022). Machine learning regression to boost scheduling performance in hyper-scale cloud-computing data centres. Journal of King Saud University - Computer and Information Sciences, 34 (6, part B), 3191-3203. | |
dc.identifier.issn | 1319-1578 | es |
dc.identifier.uri | https://hdl.handle.net/11441/134946 | |
dc.description.abstract | Data centres increase their size and complexity due to the increasing amount of heterogeneous work loads and patterns to be served. Such a mix of various purpose workloads makes the optimisation of
resource management systems according to temporal or application-level patterns difficult. Data centre operators have developed multiple resource-management models to improve scheduling perfor mance in controlled scenarios. However, the constant evolution of the workloads makes the utilisation
of only one resource-management model sub-optimal in some scenarios.
In this work, we propose: (a) a machine learning regression model based on gradient boosting to pre dict the time a resource manager needs to schedule incoming jobs for a given period; and (b) a resource
management model, Boost, that takes advantage of this regression model to predict the scheduling time
of a catalogue of resource managers so that the most performant can be used for a time span.
The benefits of the proposed resource-management model are analysed by comparing its scheduling
performance KPIs to those provided by the two most popular resource-management models: two level, used by Apache Mesos, and shared-state, employed by Google Borg. Such gains are empirically eval uated by simulating a hyper-scale data centre that executes a realistic synthetically generated workload
that follows real-world trace patterns | es |
dc.description.sponsorship | Ministerio de Ciencia e Innovación RTI2018-098062-A-I00 | es |
dc.format | application/pdf | es |
dc.format.extent | 13 | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Journal of King Saud University - Computer and Information Sciences, 34 (6, part B), 3191-3203. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Data centre | es |
dc.subject | Cloud computing | es |
dc.subject | Scheduling optimisation | es |
dc.subject | Machine Learning | es |
dc.subject | Gradient boosting | es |
dc.title | Machine learning regression to boost scheduling performance in hyper-scale cloud-computing data centres | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | RTI2018-098062-A-I00 | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S1319157822001367?via%3Dihub | es |
dc.identifier.doi | 10.1016/j.jksuci.2022.04.008 | es |
dc.contributor.group | Universidad de Sevilla. TIC134: Sistemas Informáticos | es |
dc.journaltitle | Journal of King Saud University - Computer and Information Sciences | es |
dc.publication.volumen | 34 | es |
dc.publication.issue | 6, part B | es |
dc.publication.initialPage | 3191 | es |
dc.publication.endPage | 3203 | es |
dc.contributor.funder | Ministerio de Ciencia e Innovación (MICIN). España | es |