Medea: scheduling of long running applications in shared production clusters
Medea: scheduling of long running applications in shared production clusters Garefalakis et al., EuroSys’18
(If you don’t have ACM Digital Library access, the paper can be accessed either by following the link above directly from The Morning Paper blog site).
We’re sticking with schedulers today, and a really interesting system called Medea which is designed to support the common real world use case of mixed long running applications (LRAs) and shorter duration tasks within the same cluster. The work is grounded in production cluster workloads at Microsoft and is now part of the Apache Hadoop 3.1 release. In the evaluation, when compared to the Kubernetes’ scheduling algorithm Medea reduces median runtimes by up to 32%, and by 2.1x compared to the previous generation YARN scheduler.
…a substantial portion of production clusters today is dedicated to LRAs…. placing LRAs, along with batch jobs, in shared clusters is appealing to reduce cluster operational costs, avoid unnecessary data movement, and enable pipelines involving both classes of applications. Despite these observations, support for LRAs in existing schedulers is rudimentary.
The challenges of scheduling long-running applications
Example uses of long running application containers include streaming systems, interactive data-intensive applications (maintaining Continue reading
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