HFSP: Bringing Size-Based Scheduling To Hadoop

• Published in: IEEE transactions on cloud computing Vol. 5; no. 1; pp. 43 - 56
• Main Authors: Pastorelli, Mario, Carra, Damiano, DellAmico, Matteo, Michiardi, Pietro
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE Computer Society 01-01-2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aging; Batch processing; Cloud computing; data analysis; Estimation; MapReduce; Processor scheduling; Scheduling; Silicon; Time factors; Training
• ISSN: 2168-7161; 2168-7161; 2372-0018
• DOI: 10.1109/TCC.2015.2396056

Size-based scheduling with aging has 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, without impacting the fairness of the scheduler, and show that HFSP is largely tolerant to job size estimation errors.