Resource management systems like YARN or Mesos allow sharing cluster resources by running dataparallel processing jobs in temporarily reserved containers. Containers, in this context, are logical leases of resources as, for instance, a number of cores and main memory, allocated on a particular node. Typically, containers are used without resource isolation to achieve high degrees of overall resource utilization despite the often fluctuating resource usage of single analytic jobs. However, some combinations of jobs utilize the resources better and interfere less with each other when running on the same nodes than others. This paper presents an approach for improving the resource utilization and job throughput when scheduling recurring distributed data-parallel processing jobs in shared cluster environments. Using a reinforcement learning algorithm, the scheduler continuously learns which jobs are best executed simultaneously on the cluster. We evaluated a prototype implementation of our approach with Hadoop YARN, exemplary Flink jobs from different application domains, and a cluster of commodity nodes. Even though the measure we use to assess the goodness of schedules can still be improved, the results of our evaluation show that our approach increases resource utilization and job throughput.
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