Map/Reduce　is　a　popular　parallel　processing　framework　for　data　intensive　computing.　For　overlapping　the　Map　task’s　execution　phase　and　the　Reduce　task’s　intermediate　data　fetching　and　merging　phase,　existing　Map/Reduce　schedulers　always　pre-launch　the　Reduce　task　at　the　specific　threshold　where　its　map　tasks　have　been　launched,　and　this　pattern　incurs　the　occupation　of　the　consuming　resources　of　the　reduce　task　during　its　idle　time　on　waiting　for　fetching　the　intermediate　data　from　map　tasks.　To　address　this　issue,　we　propose　an　extension　version　of　Hadoop　map/reduce　framework,　called　Predoop,　in　this　paper.　The　basic　idea　of　Predoop　is　to　preempt　the　reduce　task　during　its　idle　time　and　allocate　the　released　resource　to　the　map　tasks　on　schedule.　To　achieve　this　goal,　first,　we　introduce　the　preemptive　mechanism　for　reduce　tasks　and　map　tasks　respectively　to　enable　Map/Reduce　tasks　to　be　preempted　or　resumed　with　correct　status;　second,　we　adopt　the　preempting-resuming　model　for　the　reduce　task　with　the　consideration　of　the　progress　of　Reduce　task　data　fetching　&　merging　and　the　Map　task　execution　so　as　to　determine　the　timing　of　Reduce　task　preemption　and　resuming;　third,　we　introduce　the　preemption-aware　task　scheduling　strategy　to　allocate　the　released　resources　to　the　on-schedule　Map　tasks　with　the　consideration　of　data　locality.　Experimental　result　demonstrates　that　Predoop　outperforms　Hadoop　on　various　workload　and　the　average　job　turnaround　time　can　be　reduced　by　maximum　of　66.57%.　©　Springer　International　Publishing　Switzerland　2014.