MDS: In-Depth Insight

Patgiri, Ripon

doi:10.1109/icit.2016.048

Cited by 5 publications

(5 citation statements)

References 29 publications

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“…However, consistency and durability of a metadata depend on MDS architecture. Metadata is the most crucial part of a file system for storage of Big Data to enhance the performance, and thus, the MDS defines scalability and performance enhancement of a file system storing Big Data [75,79].…”

Section: Metadata Serversmentioning

confidence: 99%

“…Therefore, the standalone MDS is not scalable. Patgiri [75] calculate the metadata scalability of r GB RAM at most…”

Section: Categories Of Metadata Serversmentioning

confidence: 99%

“…Patgiri [75] indicates that 64T B data can be served by 16GB available free RAM. It seems excellent scalability, but these 64T B data contributes a very large amount of users as referred to "Vendee" in Big Data Paradigm [78].…”

Section: Categories Of Metadata Serversmentioning

confidence: 99%

“…The load balancing is another issue in MDS design. The hash based has good load balancing, but, poor cache performance [75,79]. The subtree partition is not good at load balancing.…”

Section: Load Balancingmentioning

confidence: 99%

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A Survey on Large Scale Metadata Server for Big Data Storage

Patgiri¹,

Nayak²

2020

Preprint

Self Cite

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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.

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Section: Metadata Serversmentioning

confidence: 99%

“…Therefore, the standalone MDS is not scalable. Patgiri [75] calculate the metadata scalability of r GB RAM at most…”

Section: Categories Of Metadata Serversmentioning

confidence: 99%

Section: Categories Of Metadata Serversmentioning

confidence: 99%

“…The load balancing is another issue in MDS design. The hash based has good load balancing, but, poor cache performance [75,79]. The subtree partition is not good at load balancing.…”

Section: Load Balancingmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Large Scale Metadata Server for Big Data Storage

Patgiri¹,

Nayak²

2020

Preprint

Self Cite

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show abstract

“…The MDS become a bottleneck when smaller sized file. The billions of small sized file create no more storage space, but produce the huge size of Metadata, results performance degradation [10], [37], [38], [39]. The paper [40] address the problem of small files.…”

Section: File System For Big Datamentioning

confidence: 99%

A Taxonomy on Big Data: Survey

Patgiri

2018

Preprint

Self Cite

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The Big Data is the most popular paradigm nowadays and it has almost no untouched area. For instance, science, engineering, economics, business, social science, and government. The Big Data are used to boost up the organization performance using massive amount of dataset. The Data are assets of the organization, and these data gives revenue to the organizations. Therefore, the Big Data is spawning everywhere to enhance the organizations' revenue. Thus, many new technologies emerging based on Big Data. In this paper, we present the taxonomy of Big Data. Besides, we present in-depth insight on the Big Data paradigm.

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Dr. Hadoop: In Search of a Needle in a Haystack

Nayak

Patgiri

2018

Distributed Computing and Internet Technology

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MDS: In-Depth Insight

Cited by 5 publications

References 29 publications

A Survey on Large Scale Metadata Server for Big Data Storage

A Survey on Large Scale Metadata Server for Big Data Storage

A Taxonomy on Big Data: Survey

Dr. Hadoop: In Search of a Needle in a Haystack

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