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. © 2013 IEEE.

HFSP: Size-based scheduling for Hadoop

Dell'Amico M.;
2013

Abstract

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. © 2013 IEEE.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/1070998
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