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An SSD-based accelerator for directory parsing in storage systems containing massive files - Springer

机译:基于SSD的加速器,用于在包含海量文件的存储系统中进行目录解析-Springer

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摘要

Data explosion introduces new challenges to storage systems. In a file system for big data, a large number of directories and files exist, which are usually organized in a large tree. Parsing directories in a large tree is difficult. In this paper, we propose an accelerator, which helps file systems to fetch the metadata of files rapidly. Contributions of this work include two aspects. First, we propose an accelerator for directory parsing. The accelerator is actually an SSD-based (Solid State Drive-based) cache, which keeps the metadata of frequently or recently accessed files and directories. When a file is demanded, the accelerator attempts to obtain its metadata directly from SSD. If the metadata is kept in SSD, the file system can rapidly obtain the metadata. However, if the metadata is not in SSD, the accelerator consumes a long time to access SSD, but to no avail. In order to avoid non-beneficial SSD accesses, the accelerator predicts whether the metadata is kept by SSD before issuing a read request. Only if the metadata has a high probability of being kept in SSD, the accelerator issues a request to the SSD. The second contribution of this paper is a new bloom filter used to predict whether a piece of data is kept in SSD. Bloom filter is a space-efficient data structure supporting membership query. But, the standard bloom filter cannot support element deletion. Whereas, our accelerator is a cache, which evicts items periodically. The standard bloom filter is not suitable for our accelerator. In this work, we designed a new bloom filter with low overhead, which supports element deletion. The new bloom filter perfectly suits the proposed accelerator. With the prediction of our bloom filter, the accelerator can accelerate the process of directory parsing with nearly no negative impact. We evaluated the accelerator by using a prototype. Experimental results demonstrate that, the accelerator can speed up the directory parsing process by nearly four times compared with a file system without an accelerator.
机译:数据爆炸给存储系统带来了新的挑战。在用于大数据的文件系统中,存在大量目录和文件,这些目录和文件通常以大树的形式组织。在大树中解析目录非常困难。在本文中,我们提出了一种加速器,该加速器可帮助文件系统快速获取文件的元数据。这项工作的贡献包括两个方面。首先,我们提出一种用于目录解析的加速器。加速器实际上是基于SSD(基于固态硬盘)的缓存,用于保留频繁或最近访问的文件和目录的元数据。当需要文件时,加速器尝试直接从SSD获取其元数据。如果元数据保存在SSD中,则文件系统可以快速获取元数据。但是,如果元数据不在SSD中,则加速器将花费很长时间访问SSD,但无济于事。为了避免非有益的SSD访问,加速器会在发出读取请求之前预测元数据是否由SSD保留。仅当元数据极有可能被保存在SSD中时,加速器才会向SSD发出请求。本文的第二个贡献是一个新的布隆过滤器,用于预测SSD中是否保留了一条数据。布隆过滤器是一种节省空间的数据结构,支持成员资格查询。但是,标准的Bloom过滤器无法支持元素删除。而我们的加速器是一个缓存,可定期逐出项目。标准bloom过滤器不适用于我们的加速器。在这项工作中,我们设计了一种开销较低的新Bloom过滤器,该过滤器支持元素删除。新的Bloom过滤器非常适合建议的加速器。通过我们的Bloom过滤器预测,加速器可以加速目录解析过程,而几乎没有负面影响。我们通过使用原型评估了加速器。实验结果表明,与没有加速器的文件系统相比,加速器可以将目录解析过程加快近四倍。

著录项

  • 来源
    《Peer-to-peer networking and applications》 |2013年第4期|397-408|共12页
  • 作者单位

    1.State Key Laboratory of High Performance Computing National University of Defense Technology Changsha 410073 China 2.School of Computer National University of Defense Technology Changsha 410073 China;

    1.State Key Laboratory of High Performance Computing National University of Defense Technology Changsha 410073 China 2.School of Computer National University of Defense Technology Changsha 410073 China;

    1.State Key Laboratory of High Performance Computing National University of Defense Technology Changsha 410073 China 2.School of Computer National University of Defense Technology Changsha 410073 China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    SSD Directory parsing Cache Accelerator Storage system File system;

    机译:SSD目录解析Cache Accelerator存储系统文件系统;

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