| Autor: |
Fernández Cerero, Damián, Troyano Jiménez, José Antonio, Jakóbik, Agnieszka, Fernández Montes González, Alejandro |
| Přispěvatelé: |
Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos, Universidad de Sevilla. TIC134: Sistemas Informáticos, Ministerio de Ciencia e Innovación (MICIN). España |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Popis: |
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 Ministerio de Ciencia e Innovación RTI2018-098062-A-I00 |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
