Dynamic Replication Policy on HDFS Based on Machine Learning Clustering

Data growth in recent years has been swift, leading to the emergence of big data science. Distributed File Systems (DFS) are commonly used to handle big data, like Google File System (GFS), Hadoop Distributed File System (HDFS), and others. The DFS should provide the availability of data and reliabi...

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Vydáno v:	IEEE access Ročník 11; s. 1
Hlavní autoři:	Ahmed, Motaz A., Khafagy, Mohamed H., Shaheen, Masoud E., Kaseb, Mostafa R.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Availability Big Data Clustering Computer networks Data science Distributed computing Distributed processing Feature extraction File systems Hadoop Distributed File System High-Performance Distributed Computing Machine learning Reliability Replicability Replication Replication Policy Support vector machines
ISSN:	2169-3536, 2169-3536
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Abstract	Data growth in recent years has been swift, leading to the emergence of big data science. Distributed File Systems (DFS) are commonly used to handle big data, like Google File System (GFS), Hadoop Distributed File System (HDFS), and others. The DFS should provide the availability of data and reliability of the system in case of failure. The DFS replicates the files in different locations to provide availability and reliability. These replications consume storage space and other resources. The importance of these files differs depending on how frequently they are used in the system. So some of these files do not deserve to replicate many times because it is unimportant in the system. This paper introduces a Dynamic Replication Policy using Machine Learning Clustering (DRPMLC) on HDFS, which uses Machine Learning to cluster the files into different groups and apply other replication policies to each group to reduce the storage consumption, improve the read and write operations time and keep the availability and reliability of HDFS as a High-Performance Distributed Computing (HPDC).
AbstractList	Data growth in recent years has been swift, leading to the emergence of big data science. Distributed File Systems (DFS) are commonly used to handle big data, like Google File System (GFS), Hadoop Distributed File System (HDFS), and others. The DFS should provide the availability of data and reliability of the system in case of failure. The DFS replicates the files in different locations to provide availability and reliability. These replications consume storage space and other resources. The importance of these files differs depending on how frequently they are used in the system. So some of these files do not deserve to replicate many times because it is unimportant in the system. This paper introduces a Dynamic Replication Policy using Machine Learning Clustering (DRPMLC) on HDFS, which uses Machine Learning to cluster the files into different groups and apply other replication policies to each group to reduce the storage consumption, improve the read and write operations time and keep the availability and reliability of HDFS as a High-Performance Distributed Computing (HPDC).
Author	Ahmed, Motaz A. Shaheen, Masoud E. Khafagy, Mohamed H. Kaseb, Mostafa R.
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SubjectTerms	Availability Big Data Clustering Computer networks Data science Distributed computing Distributed processing Feature extraction File systems Hadoop Distributed File System High-Performance Distributed Computing Machine learning Reliability Replicability Replication Replication Policy Support vector machines
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Title	Dynamic Replication Policy on HDFS Based on Machine Learning Clustering
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