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HAT: history-based auto-tuning MapReduce in heterogeneous environments
2011
Journal of Supercomputing
In MapReduce model, a job is divided into a series of map tasks and reduce tasks. The execution time of the job is prolonged by some slow tasks seriously, especially in heterogeneous environments. To finish the slow tasks as soon as possible, current MapReduce schedulers launch a backup task on other nodes for each of the slow tasks. However, traditional MapReduce schedulers cannot detect slow tasks correctly since they cannot estimate the progress of tasks accurately (Hadoop home page
doi:10.1007/s11227-011-0682-5
fatcat:sde2umsk2fattowxnmzldp4tfm