Scheduling Recurring Distributed Dataflow Jobs Based on Resource Utilization and Interference

Resource management systems like YARN or Mesos enable users to share cluster infrastructures by running analytics jobs in temporarily reserved containers. These containers are typically not isolated to achieve high degrees of overall resource utilizations despite the often fluctuating resource usage of single analytic jobs. However, some combinations of jobs utilize the resources better and interfere less with each others when running on the same nodes than others. This paper presents an approach for improving the resource utilization and job throughput when scheduling recurring data analysis jobs in shared cluster environments. Using a reinforcement learning algorithm, the scheduler continuously learns which jobs are best executed simultaneously on the cluster. Our evaluation of an implementation built on Hadoop YARN shows that this approach can increase resource utilization and decrease job runtimes. While interference between jobs can be avoided, co-locations of jobs with complementary resource usage are not yet always fully recognized. However, with a better measure of co-location goodness, our solution can be used to automatically adapt the scheduling to workloads with recurring batch jobs.