期刊名称：Journal of King Saud University @?C Computer and Information Sciences
印刷版ISSN：1319-1578
出版年度：2022
卷号：34
期号：6
页码：3191-3203
语种：English
出版社：Elsevier
摘要：Data centres increase their size and complexity due to the increasing amount of heterogeneous workloads 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 performance 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 predict 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 evaluated by simulating a hyper-scale data centre that executes a realistic synthetically generated workload that follows real-world trace patterns.
