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 patternsMinisterio de Ciencia e Innovación RTI2018-098062-A-I0
