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Enhanced dual Bloom filter based on SSD for efficient directory parsing in cloud storage system

机译:基于SSD的增强型双Bloom过滤器,可在云存储系统中进行有效的目录解析

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In a file system used for big data analytics, hundreds of thousands of files exist. In such huge storage system, getting the metadata of a file takes long time. In this paper we propose an enhanced Bloom filter to accelerate the directory parsing process in large-scale file systems. Here a cache implemented on SSD keeps the metadata of directories and files accessed frequently or recently. When a file is requested, the system attempts to get the metadata from the SSD. If the metadata is not found, the access to the SSD becomes a waste of time. To avoid unnecessary SSD accesses, the flag-augmented Bloom filter (FABF) is proposed with which the existence of metadata of the requested file in the cache is predicted. Analytical modeling demonstrates that the false positive rate and false negative rate are reduced compared to the existing scheme. In addition, the implementation overhead of the proposed scheme is small.
机译:在用于大数据分析的文件系统中,存在成千上万个文件。在如此庞大的存储系统中,获取文件的元数据需要很长时间。在本文中,我们提出了一种增强的Bloom过滤器,以加速大型文件系统中的目录解析过程。在此,在SSD上实现的缓存可保留经常或最近访问的目录和文件的元数据。当请求文件时,系统尝试从SSD中获取元数据。如果找不到元数据,则访问SSD会浪费时间。为了避免不必要的SSD访问,建议使用标志增强型布隆过滤器(FABF),使用该标志预测缓存中所请求文件的元数据的存在。分析模型表明,与现有方案相比,误报率和误报率降低了。另外,该方案的实现开销很小。

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