@Article{IJNAM-9-2,
author = {},
title = {Adaptive Tradeoff in Metadata-Based Small File Optimizations for a Cluster File System},
journal = {International Journal of Numerical Analysis and Modeling},
year = {2012},
volume = {9},
number = {2},
pages = {289--303},
abstract = {<p style="text-align: justify;">Metadata-based optimizations are the common methods to improve small files performance
in local file systems. However, several problems will be introduced when applying the
similar optimizations for small files in cluster file systems. In this paper, we study the tradeoffs
between the performance of metadata and small files in metadata-based optimizations for a cluster
file system. Our method aims to guarantee the metadata performance by adaptively migrating
small files among file system nodes. We establish a theory model to analyze the small files load
need to be migrated. To compute the migrated load in advance, a novel forecasting method is
devised to accurately predict the one-step-ahead load of metadata and small files on a MDS. Then
we propose a adaptive small file threshold model to decide the small files to be migrated. In the
model, we consider the long-term and short-term tradeoffs respectively. To reduce the migration
overhead, we discuss the migration tradeoffs for small files and present methods and schemes to
eliminate unnecessary overheads. Finally, experiments are performed on a cluster file system and
the results show the efficiency of our method in terms of promoting the load forecasting accuracy,
trading off the performance of metadata and small files, and reducing migration overhead.</p>},
issn = {2617-8710},
doi = {https://doi.org/2012-IJNAM-628},
url = {https://global-sci.com/article/83469/adaptive-tradeoff-in-metadata-based-small-file-optimizations-for-a-cluster-file-system}
}