  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.
