HFSP: size-based scheduling for Hadoop

Pastorelli, Mario; Barbuzzi, Antonio; Carra, Damiano; Dell'Amico, Matteo; Michiardi, Pietro
BIGDATA 2013, IEEE International Conference on BigData, October 6-9, 2013, Santa-Clara, CA, USA

Size-based scheduling with aging has, for long, been recognized as an effective approach to guarantee fairness and near-optimal system response times. We present HFSP, a scheduler introducing this technique to a real, multi-server, complex and widely used system such as Hadoop. Size-based scheduling requires a priori job size information, which is not available in Hadoop: HFSP builds such knowledge by estimating it on-line during job execution.
Our experiments, which are based on realistic workloads generated via a standard benchmarking suite, pinpoint at a significant decrease in system response times with respect to
the widely used Hadoop Fair scheduler, and show that HFSP is largely tolerant to job size estimation errors.

DOI
Type:
Conférence
City:
Santa-Clara
Date:
2013-10-06
Department:
Data Science
Eurecom Ref:
4106
Copyright:
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