A Survey on Large Scale Metadata Server for Big Data Storage

Abstract

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 systemâĂŹs 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.