scholarly journals Developing a File System Structure to Solve Healthy Big Data Storage and Archiving Problems Using a Distributed File System

2018 ◽  
Vol 8 (6) ◽  
pp. 913 ◽  
Author(s):  
Atilla Ergüzen ◽  
Mahmut Ünver
Keyword(s):  
Big Data ◽  
Data Storage ◽  
File System ◽  
System Structure ◽  
Distributed File System ◽  
Big Data Storage

Research on Power Big Data Storage Platform Based on Distributed File System

2019 ◽  
pp. 760-767
Author(s):  
Liu Fei ◽  
Pang Hao-Yuan ◽  
Zhang Yi-Ying ◽  
Liang Kun ◽  
He Ye-Shen ◽  
...  
Keyword(s):  
Big Data ◽  
Data Storage ◽  
File System ◽  
Distributed File System ◽  
Big Data Storage

scholarly journals Modeling of distributed file System in big data storage by event- B

2018 ◽  
Vol 210 ◽  
pp. 04042
Author(s):  
Ammar Alhaj Ali ◽  
Pavel Varacha ◽  
Said Krayem ◽  
Roman Jasek ◽  
Petr Zacek ◽  
...  
Keyword(s):  
Big Data ◽  
Data Storage ◽  
High Performance ◽  
File System ◽  
Formal Method ◽  
File Systems ◽  
Distributed File System ◽  
Distributed File Systems ◽  
Data Systems ◽  
Big Data Systems

Nowadays, a wide set of systems and application, especially in high performance computing, depends on distributed environments to process and analyses huge amounts of data. As we know, the amount of data increases enormously, and the goal to provide and develop efficient, scalable and reliable storage solutions has become one of the major issue for scientific computing. The storage solution used by big data systems is Distributed File Systems (DFSs), where DFS is used to build a hierarchical and unified view of multiple file servers and shares on the network. In this paper we will offer Hadoop Distributed File System (HDFS) as DFS in big data systems and we will present an Event-B as formal method that can be used in modeling, where Event-B is a mature formal method which has been widely used in a number of industry projects in a number of domains, such as automotive, transportation, space, business information, medical device and so on, And will propose using the Rodin as modeling tool for Event-B, which integrates modeling and proving as well as the Rodin platform is open source, so it supports a large number of plug-in tools.

scholarly journals Analysis and Experimental Study of HDFS Performance

TEM Journal ◽  
2021 ◽  
pp. 806-814
Author(s):  
Yordan Kalmukov ◽  
Milko Marinov ◽  
Tsvetelina Mladenova ◽  
Irena Valova
Keyword(s):  
Experimental Study ◽  
Big Data ◽  
Computer System ◽  
Data Storage ◽  
Fault Tolerant ◽  
Processing System ◽  
Daily Basis ◽  
Distributed File System ◽  
Hadoop Distributed File System ◽  
Big Data Storage

In the age of big data, the amount of data that people generate and use on a daily basis has far exceeded the storage and processing capabilities of a single computer system. That motivates the use of distributed big data storage and processing system such as Hadoop. It provides a reliable, horizontallyscalable, fault-tolerant and efficient service, based on the Hadoop Distributed File System (HDFS) and MapReduce. The purpose of this research is to experimentally determine whether (and to what extent) the network communication speed, the file replication factor, the files’ sizes and their number, and the location of the HDFS client influence the performance of the HDFS read/write operations.

scholarly journals The File System Recommendations to Reduce the Space and Time Parameters in Hadoop File Storage and Map Reduce Processing of Big Data Applications

2020 ◽  
Vol 9 (10) ◽  
pp. 353-356
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Data Storage ◽  
File System ◽  
Distributed File System ◽  
Map Reduce ◽  
Space And Time ◽  
File Storage ◽  
Hadoop Distributed File System ◽  
Hadoop Framework

The study of Hadoop Distributed File System (HDFS) and Map Reduce (MR) are the key aspects of the Hadoop framework. The big data scenarios like Face Book (FB) data processing or the twitter analytics such as storing the tweets and processing the tweets is other scenario of big data which can depends on Hadoop framework to perform the storage and processing through which further analytics can be done. The point here is the usage of space and time in the processing of the above-mentioned huge amounts of the data definitely leads to higher amounts of space and time consumption of the Hadoop framework. The problem here is usage of huge amounts of the space and at the same time the processing time is also high which need to be reduced so as to get the fastest response from the framework. The attempt is important as all the other eco system tools also depends on HDFS and MR so as to perform the data storage and processing of the data and alternative architecture so as to improve the usage of the space and effective utilization of the resources so as to reduce the time requirements of the framework. The outcome of the work is faster data processing and less space utilization of the framework in the processing of MR along with other eco system tools like Hive, Flume, Sqoop and Pig Latin. The work is proposing an alternative framework of the HDFS and MR and the name we are assigning is Unified Space Allocation and Data Processing with Metadata based Distributed File System (USAMDFS).

scholarly journals A Distribution of Nodes in Big Data using Hadoop Open Source System

2020 ◽  
Vol 9 (3) ◽  
pp. 106-110
Keyword(s):  
Big Data ◽  
Open Source ◽  
Data Storage ◽  
High Speed ◽  
File System ◽  
Fault Tolerant ◽  
Heart Beat ◽  
Distributed File System ◽  
Process Data ◽  
Hadoop Distributed File System

Apache Hadoop is an free open source Java framework under Apache Software Foundation. It provides storage of large amount of data efficiently with low costing. Hadoop has two main core components one is HDFS (Hadoop Distributed File System) and second Map Reduce. It is basically a file system and has capability of high fault-tolerant and while deploying supports less cost hardware. It. provides the high speed admittance to the relevance data. The Hadoop architecture is based on cluster, which consist of two nodes named as Data -Node and Name-Node which perform the internal activity known as heart beat to process data storage on distributed file system and Map reducing is performed internally to show the clustering of distributed data on localhost of ssh serverwebsite. Large quantity of data is needed to store in distributed file structure, for this Hadoop has played important role. Maintaining the large volume storage, making data duplicity for providing security and recovery of big data for its analysis and prediction.

Big Data Storage Concepts

Big Data ◽  
2021 ◽  
pp. 31-52
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Big Data Storage

Secure big data storage and sharing scheme for cloud tenants

2015 ◽  
Vol 12 (6) ◽  
pp. 106-115 ◽  
Author(s):  
Hongbing Cheng ◽  
Chunming Rong ◽  
Kai Hwang ◽  
Weihong Wang ◽  
Yanyan Li
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Sharing Scheme ◽  
Big Data Storage
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