sponsored byACMIEEE The International Conference for High Performance 
Computing, Networking, Storage and Analysis
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SCHEDULE: NOV 15-20, 2015

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Improving Backfilling by using Machine Learning to Predict Running Times

SESSION: Resource Management


EVENT TAG(S): System Software, Resource Management

TIME: 10:30AM - 11:00AM


AUTHOR(S):Eric Gaussier, David Glesser, Valentin Reis, Denis Trystram



The job management system is the HPC middleware responsible for distributing computing power to applications. While such systems generate an ever increasing amount of data, they are characterized by uncertainties on some parameters like the job running times. The question raised in this work is: To what extent is it possible/useful to take into account predictions on the job running times for improving the global scheduling?

We present a comprehensive study for answering this question assuming the popular EASY backfilling policy. More precisely, we rely on some classical methods in machine learning and propose new cost functions well-adapted to the problem. Then, we assess our proposed solutions through intensive simulations using several production logs. Finally, we propose a new scheduling algorithm that outperforms the
popular EASY backfilling algorithm by 28% considering the average bounded slowdown objective.

Chair/Author Details:

Kim Cupps (Chair) - Lawrence Livermore National Laboratory|

Eric Gaussier - University Grenoble Alpes

David Glesser - BULL

Valentin Reis - University Grenoble Alpes

Denis Trystram - University Grenoble Alpes

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Paper provided by the ACM Digital Library

Paper also available from IEEE Computer Society