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Descripción
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| Small ?les are known to pose major performance challenges for ?le systems. Yet, such workloads are increasingly common in a number of Big Data Analytics work?ows or large- scale HPC simulations. These challenges are mainly caused by the common architecture of most state-of-the-art ?le systems needing one or multiple metadata requests before being able to read from a ?le. Small input ?le size causes the overhead of this metadata management to gain relative importance as the size of each ?le decreases. In this paper we propose a set of techniques leveraging consistent hashing and dynamic metadata replication to signi?cantly reduce this metadata overhead. We implement such techniques inside a new ?le system named T¿ yrFS, built as a thin layer above the T¿ yr object store. We prove that T¿ yrFS increases small ?le access performance up to one order of magnitude compared to other state-of-the-art ?le systems, while only causing a minimal impact on ?le write throughput. | |
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Internacional
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Si |
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Nombre congreso
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18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2018 |
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Tipo de participación
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960 |
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Lugar del congreso
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Washington, Estados Unidos de América |
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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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01/05/2018 |
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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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CCGrid 2018, Proceedings of the 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2018. |