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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Forecasting Storms in Parallel File Systems

SESSION: Regular & ACM Student Research Competition Poster Reception

EVENT TYPE: Posters, Receptions, ACM Student Research Competition

EVENT TAG(S): HPC Beginner Friendly, ACM Student Research Competition Poster

TIME: 5:15PM - 7:00PM

SESSION CHAIR(S): Michela Becchi, Manish Parashar, Dorian C. Arnold

AUTHOR(S):Ryan McKenna

ROOM:Level 4 - Lobby

ABSTRACT:

Large-scale scientific applications rely on the parallel file system (PFS) to store checkpoints and outputs. When the PFS is over-utilized, applications can slow down significantly as they compete for scarce bandwidth. To prevent this type of “filesystem storm”, schedulers must avoid running many IO-intensive jobs at the same time. To effectively implement such a strategy, schedulers must predict the IO workload and runtime of future jobs. In this poster, we explore the use of machine learning methods to forecast file system usage and to predict the runtimes of queued jobs using historical data. We show that our runtime predictions achieve over 80% accuracy to within 10 minutes of actual runtime.

Chair/Author Details:

Michela Becchi, Manish Parashar, Dorian C. Arnold (Chair) - University of Missouri|Rutgers University|University of New Mexico|

Ryan McKenna - University of Delaware

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