Managing Data Operating System
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Use CPU Scheduling

Cgroups with CPU scheduling helps you effectively manage mixed workloads.

MapReduce jobs only

If you primarily run MapReduce jobs on your cluster, enabling CPU scheduling does not change performance much. The dominant resource for MapReduce is memory, so the DRF scheduler continues to balance MapReduce jobs in a manner similar to the default resource calculator. In the case of a single resource, the DRF reduces to max-min fairness for that resource.

Mixed workloads

An example of a mixed workload is a cluster that runs both MapReduce and Storm on YARN. MapReduce is not CPU-constrained, but Storm on YARN is; its containers require more CPU than memory. As you add Storm jobs along with MapReduce jobs, the DRF scheduler tries to balance memory and CPU resources, but you might see some performance degradation in as a result. As you add more CPU-intensive Storm jobs, individual jobs start to take longer to run as the cluster CPU resources are consumed.

To solve this problem, you can use cgroups along with CPU scheduling. Using cgroups provides isolation for CPU-intensive processes such as Storm on YARN, thereby enabling you to predictably plan and constrain the CPU-intensive Storm containers.

You can also use node labels in conjunction with CPU scheduling and cgroups to restrict Storm on YARN jobs to a subset of cluster nodes.

You should use CPU scheduling only in a Linux environment, because there is no isolation mechanism (cgroups equivalent) for Windows.