Joint optimization of overlapping phases in MapReduce

  • Authors:
  • Minghong Lin;Li Zhang;Adam Wierman;Jian Tan

  • Affiliations:
  • -;-;-;-

  • Venue:
  • Performance Evaluation
  • Year:
  • 2013

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Abstract

MapReduce is a scalable parallel computing framework for big data processing. It exhibits multiple processing phases, and thus an efficient job scheduling mechanism is crucial for ensuring efficient resource utilization. There are a variety of scheduling challenges within the MapReduce architecture, and this paper studies the challenges that result from the overlapping of the ''map'' and ''shuffle'' phases. We propose a new, general model for this scheduling problem, and validate this model using cluster experiments. Further, we prove that scheduling to minimize average response time in this model is strongly NP-hard in the offline case and that no online algorithm can be constant-competitive. However, we provide two online algorithms that match the performance of the offline optimal when given a slightly faster service rate (i.e., in the resource augmentation framework). Finally, we validate the algorithms using a workload trace from a Google cluster and show that the algorithms are near optimal in practical settings.