YARN is a distributed resource management system of Hadoop. It can be used to improve the utilization of memory, I/O, network, disk and other resources of distributed cluster. However, there are many configuration parameters in YARN. Due to this reason, manual tuning of Hadoop performance to get the best performance is difficult and time-consuming. Based on the existing YARN resource scheduler, a successive approximation closed-loop feedback control method is proposed. This method can dynamically tune the parallel number of MapReduce (MR) jobs in the running state of the cluster, and eliminating the process of manual adjustment of parameters. Experiments show that the proposed approach reduces the MR operation time for 53% and 14% based on capacity scheduler and fair scheduler, respectively, compared with the default configuration.