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LCIO: Large Scale Filesystem Aging

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Benchmarking, Measuring, and Optimizing (Bench 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12093))

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Abstract

Performance of file systems shift during their life cycles. Evaluating this performance change over time is not trivial. Complexity arises in the interplay between external (i.e. application I/O workloads) and internal (i.e. the filesystem state) factors. Many benchmarks can test how a filesystem performs at the current snapshot state, but to observe the change over time necessitates that the filesystem state mutate (age) between benchmark runs. For a large-scale HPC parallel filesystem, the sheer scale and amount of interacting components during I/O operations magnify these challenges.

There have been several approaches that address different aspects of filesystem aging, from creating statistically realistic filesystem images to file age distributions. The common drawbacks are the scale to be evaluated and the time needed to converge; none of the methods in literature targeted network or parallel file systems. Also, none were evaluated with a filesystem image over 300 GiB, most under 50 GiB, yet almost all took between a half hour to 7 h to converge. For a large-scale parallel file system, these methods are impractical as far as time and resources needed (a typical large PFS is in the PB range). Additionally, HPC filesystem I/O workloads are drastically different from local system workloads used in earlier studies.

This paper presents the design, implementation and evaluation of LCIO synthetic filesystem aging benchmark, which aims to address the question of “how will the filesystem perform at different stages of its life cycle?”. As such, being able to answer that question as realistically as feasible in a reasonable time is where LCIO contributes.

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Correspondence to Matthew Bachstein .

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Bachstein, M., Wang, F., Oral, S. (2020). LCIO: Large Scale Filesystem Aging. In: Gao, W., Zhan, J., Fox, G., Lu, X., Stanzione, D. (eds) Benchmarking, Measuring, and Optimizing. Bench 2019. Lecture Notes in Computer Science(), vol 12093. Springer, Cham. https://doi.org/10.1007/978-3-030-49556-5_24

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  • DOI: https://doi.org/10.1007/978-3-030-49556-5_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-49555-8

  • Online ISBN: 978-3-030-49556-5

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