MapReduce has been widely used in data-intensive applications;it has several advantages

such as good programming interface

high availability and fault tolerance.It is suitable for distributed processing on large-scale data

for instance

network analysis

bioinformatics and high-performance computing.In shared cluster

there are many researches on scheduling issue.However

in multi-processers nodes

the scheduling method based on priority constrain which conclude multi MapReduce tasks is uncommon.This paper provides a scheduling method based on priority constrain which conclude multi MapReduce tasks. Workload applications have a priority dependency constraint (which can be represented by DAG).Meanwhile

for the limited bandwidth of the low cost of data transmission

data dependence limit should be concluded in the scheduling system.The proposed algorithm can provide:MapReduce task scheduling with precedence constraints;by considering the data dependencies restrict the data pre-placement strategy to reduce transmission costs.