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Descripción
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| Small files are known to pose major performance challenges for file systems. Yet, such workloads are increasingly common in a number of Big Data Analytics workflows or large-scale HPC simulations. These challenges are mainly caused by the common architecture of most state-of-the-art file systems needing one or multiple metadata requests before being able to read from a file. Small input file size causes the overhead of this metadata management to gain relative importance as the size of each file decreases. In this paper we propose a set of techniques leveraging consistent hashing and dynamic metadata replication to significantly reduce this metadata overhead. We implement such techniques inside a new file system named TýrFS, built as a thin layer above the Týr object store. We prove that TýrFS increases small file access performance up to one order of magnitude compared to other state-of-the-art file systems, while only causing a minimal impact on file write throughput. | |
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Internacional
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Si |
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Nombre congreso
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2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing |
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Tipo de participación
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960 |
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Lugar del congreso
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Washington, DC, EEUU |
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Revisores
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Si |
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ISBN o ISSN
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0-7695-6410-0 |
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DOI
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10.1109/CCGRID.2018.00072 |
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Fecha inicio congreso
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02/05/2018 |
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Fecha fin congreso
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04/05/2018 |
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Desde la página
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452 |
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Hasta la página
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461 |
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Título de las actas
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Proceedings of the 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing |