Artículo
Machine learning regression to boost scheduling performance in hyper-scale cloud-computing data centres
Autor/es | Fernández Cerero, Damián
Troyano Jiménez, José Antonio Jakóbik, Agnieszka Fernández Montes González, Alejandro |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2022 |
Fecha de depósito | 2022-07-04 |
Publicado en |
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Resumen | 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 ... 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 |
Agencias financiadoras | Ministerio de Ciencia e Innovación (MICIN). España |
Identificador del proyecto | RTI2018-098062-A-I00 |
Cita | 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. |
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