论文标题
大规模元数据服务器的调查，用于大数据存储
A Survey on Large Scale Metadata Server for Big Data Storage
论文作者
论文摘要
大数据被定义为具有指数数据增长率的大量数据。将数据合并以产生收入，从而导致大量数据筒仓。数据是现代IT行业的油。因此，数据以指数速度增长。这些数据孤岛的访问机制由元数据定义。出于各种有益原因，元数据与数据服务器分离。例如，易于维护。元数据存储在元数据服务器（MDS）中。因此，在设计大规模存储系统时，必须进行有关MDS的研究。 MDS需要许多参数来增强其体系结构。 MDS的体系结构取决于存储系统需求的需求。因此，根据基础体系结构和设计方法，MDS以各种方式分类。本文对各种MDS架构，设计和方法进行了调查。 This article emphasizes on clustered MDS (cMDS) and the reports are prepared based on a) Bloom filter$-$based MDS, b) Client$-$funded MDS, c) Geo$-$aware MDS, d) Cache$-$aware MDS, e) Load$-$aware MDS, f) Hash$-$based MDS, and g) Tree$-$based MDS.此外，本文介绍了MDS对猛mm尺尺寸数据的问题和挑战。
Big Data is defined as high volume of variety of data with an exponential data growth rate. Data are amalgamated to generate revenue, which results a large data silo. Data are the oils of modern IT industries. Therefore, the data are growing at an exponential pace. The access mechanism of these data silos are defined by metadata. The metadata are decoupled from data server for various beneficial reasons. For instance, ease of maintenance. The metadata are stored in metadata server (MDS). Therefore, the study on the MDS is mandatory in designing of a large scale storage system. The MDS requires many parameters to augment with its architecture. The architecture of MDS depends on the demand of the storage system's requirements. Thus, MDS is categorized in various ways depending on the underlying architecture and design methodology. The article surveys on the various kinds of MDS architecture, designs, and methodologies. This article emphasizes on clustered MDS (cMDS) and the reports are prepared based on a) Bloom filter$-$based MDS, b) Client$-$funded MDS, c) Geo$-$aware MDS, d) Cache$-$aware MDS, e) Load$-$aware MDS, f) Hash$-$based MDS, and g) Tree$-$based MDS. Additionally, the article presents the issues and challenges of MDS for mammoth sized data.
