The problem of high energy consumption producing from big data processing is an important issue that needs to be solved

especially under the background of data explosion. Based on analyzing problems of the existing data layout policy

the problems of the in adaptation of energy-saving mode based on storage area division and heterogeneous HDFS cluster

the inflexibility of data block segmentation algorithm

the randomness of storage node selection

  proposing a data layout strategy orienting to energy conservation are analyzed. Firstly

the new strategy divides the cluster into two different storage areas to meet the needs of saving energy: Active-Zone and Sleep-Zone; secondly

the new strategy has made improvements on traditional data block computing method

proposes a minimum number of jobs calculation method to determine the number of data blocks; at last

the new strategy can increase the adaptability of the heterogeneous cluster environment and can choose the appropriate storage nodes according to different job types. Experimental results show that the new data layout strategy can adapt to the heterogeneous cluster environment and reach the goal of reducing energy consumption for MapReduce jobs.