A strategy for scheduling reduce task based on intermediate data locality of the MapReduce

In this paper, researching on task scheduling is a way from the perspective of resource allocation and management to improve performance of Hadoop system. In order to save the network bandwidth resources in Hadoop cluster environment and improve the performance of Hadoop system, a ReduceTask scheduling strategy that based on data-locality is improved. In MapReduce stage, there are two main data streams in cluster network, they are slow task migration and remote copies of data. The two overlapping burst data transfer can easily become bottlenecks of the cluster network. To reduce the amount of remote copies of data, combining with data-locality, we establish a minimum network resource consumption model (MNRC). MNRC is used to calculate the network resources consumption of ReduceTask. Based on this model, we design a delay priority scheduling policy for the ReduceTask which is based on the cost of network resource consumption. Finally, MNRC is verified by simulation experiments. Evaluation results show that MNRC outperforms the saving cluster network resource by an average of 7.5% in heterogeneous.