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UID:submissions.supercomputing.org_SC22_sess225_spostg116@linklings.com
SUMMARY:IOPathTune: Adaptive Online Parameter Tuning for Parallel File Sys
 tem I/O Path
DESCRIPTION:ACM Student Research Competition: Graduate Poster, ACM Student
  Research Competition: Undergraduate Poster, Posters\n\nIOPathTune: Adapti
 ve Online Parameter Tuning for Parallel File System I/O Path\n\nRashid\n\n
 Parallel file systems like Lustre contain complicated I/O paths from clien
 ts to storage servers. An efficient I/O path requires proper settings of m
 ultiple parameters as the default settings often fail to deliver optimal p
 erformance, especially for diverse workloads in the HPC environment. Exist
 ing tuning strategies are limited in being adaptive, timely, and flexible.
  We propose IOPathTune, which adaptively tunes PFS I/O Path online from th
 e client side without characterizing the workloads, doing expensive profil
 ing, and communicating with other machines. We leveraged CloudLab to condu
 ct the evaluations with 20 different Filebench workloads under three diffe
 rent test conditions: single-client standalone tests, dynamic workload cha
 nge, and multi-client executions. We observed either on par or better perf
 ormance than the default configuration across all workloads. Some of the m
 ost considerable improvement includes 231%, 113%, 96%, 43%.\n\nRegistratio
 n Category: Tech Program Reg Pass, Exhibits Reg Pass
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