In　MapReduce　model,　the　job　execution　time　was　prolonged　by　the　straggler　tasks　in　heterogeneity　environments　.　The　LATE　scheduler　has　introduced　the　longest　remaining　time　strategy　,　but　it　also　has　some　drawbacks　such　as　inaccurate　estimated　time　and　the　wasting　of　system　resources.　In　order　to　solve　these　problem,　we　propose　two　main　algorithm　:　The　parameter　dynamic-tuning　algorithm　based　history　estimates　progress　of　a　task　accurately　since　it　dynamically　tunes　the　weight　of　each　phase　of　a　map　task　and　a　reduce　task　according　to　the　historical　values　of　the　weights;　The　evaluation-scheduling　algorithm　reduce　the　wasting　of　system　resources　by　evaluating　the　free　slot　before　launching　a　straggler　task　on　this　node.　The　two　main　algorithm　are　implemented　in　hadoop　0.20.1.　The　environment　results　are　satisfaction　to　our　expects　and　significantly　reduce　the　wasting　of　system　resources.　©　2012　IEEE.