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The efficiency of MapReduce is closely related to its load balance. Existingworks on MapReduce load balance focus on coarse-grained scheduling. This studyconcerns fine-grained scheduling on MapReduce operations, with each operationrepresenting one invocation of the Map or Reduce function. By default,MapReduce adopts the hash-based method to schedule Reduce operations, whichoften leads to poor load balance. In addition, the copy phase of Reduce tasksoverlaps with Map tasks, which significantly hinders the progress of Map tasksdue to I/O contention. Moreover, the three phases of Reduce tasks run insequence, while consuming different resources, thereby under-utilizingresources. To overcome these problems, we introduce a set of mechanisms namedOS4M (Operation Scheduling for MapReduce) to improve MapReduce's performance.OS4M achieves load balance by collecting statistics of all Map operations, andcalculates a globally optimal schedule to distribute Reduce operations. WithOS4M, the copy phase of Reduce tasks no longer overlaps with Map tasks, and thethree phases of Reduce tasks are pipelined based on their operation loads. OS4Mhas been transparently incorporated into MapReduce. Evaluations on standardbenchmarks show that OS4M's job duration can be shortened by up to 42%,compared with a baseline of Hadoop.

OS4M: Achieving Global Load Balance of MapReduce Workload by Scheduling at the Operation Level