Maestro: Replica-Aware Map Scheduling for MapReduce

Abstract

MapReduce has emerged as a leading programming model for data-intensive computing. Many recent research efforts have focused on improving the performance of the distributed frameworks supporting this model. Many optimizations are network-oriented and most of them mainly address the data shuffling stage of MapReduce. Our studies with Hadoop demonstrate that, apart from the shuffling phase, another source of excessive network traffic is the high number of map task executions which process remote data. That leads to an excessive number of useless speculative executions of map tasks and to an unbalanced execution of map tasks across different machines. All these factors produce a noticeable performance degradation. We propose a novel scheduling algorithm for map tasks, named Maestro, to improve the overall performance of the MapReduce computation. Maestro schedules the map tasks in two waves: first, it fills the empty slots of each data node based on the number of hosted map tasks and on the replication scheme for their input data, second, runtime scheduling takes into account the probability of scheduling a map task on a given machine depending on the replicas of the task's input data. These two waves lead to a higher locality in the execution of map tasks and to a more balanced intermediate data distribution for the shuffling phase. In our experiments on a 100-node cluster, Maestro achieves around 95% local map executions, reduces speculative map tasks by 80% and results in an improvement of up to 34% in the execution time.