With　the　arrival　of　big　data　era,　distributed　computing　framework　Hadoop　has　become　the　main　solution　to　deal　with　big　data　now.　People　usually　promote　the　performance　of　distributed　computing　by　adding　new　computing　nodes　to　cluster.　With　the　expansion　of　the　scale　of　the　cluster,　it　produces　a　large　amount　of　power　consumption　because　of　lack　of　reasonable　management　strategy.　So　how　to　make　full　use　of　computing　resources　in　the　cluster　to　improve　the　performance　of　the　whole　system　and　reduce　the　power　consumption　has　become　the　main　research　direction　of　scholars　and　industrial　circles.　For　the　above,　in　order　to　make　best　use　of　computing　resources　and　reduce　the　power　consumption,　this　paper　firstly　proposes　to　optimize　a　reasonable　configuration　of　the　parameters　provided　by　Hadoop.　Comparing　with　the　default　configuration　of　Hadoop.　It　shows　we　can　get　better　performance　by　parameter　tuning.　This　paper　proposes　a　task　scheduling　mechanism　based　on　memory　usage　prediction.　In　this　task　schedule,　it　predicts　the　future　use　status　of　memory　in　the　computing　nodes　by　analyzing　the　use　status　before.　The　task　scheduling　mechanism　can　reduce　the　memory　pressure　by　reducing　the　allocation　of　tasks　when　the　computing　node　is　under　memory　pressure.　The　task　scheduling　mechanism　can　be　more　flexible　by　setting　the　threshold　of　memory　usage.　This　mechanism　based　on　predicting　memory　usage　can　improve　the　performance　of　the　system　by　making　full　use　of　the　computing　resources.　©　Springer　Nature　Singapore　Pte　Ltd.　2018.