Grouping-Aware Data Placement in HDFS for Data-Intensive Applications Based on Graph Clustering

Vengadeswaran, S.; Balasundaram, S. R.

doi:10.1007/978-981-10-3773-3_3

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…It is therefore a promising idea to gather these files in the same node or in close nodes, thus minimizing the number of remote accesses which can harm the performance of the system. The knowledge about data correlation can be extracted from historical data using several methods such as machine learning, data mining, 3,16,17 graph theory, 18‐20 statistics, and so on. In this respect, discovering knowledge out of huge volumes of data can be performed more efficiently, based on the high‐performance computing of cloud 21 .…”

Section: Introductionmentioning

confidence: 99%

Popularity and correlation aware data replication strategy based on half‐life concept and clustering in cloud system

Chellouf

Hamrouni

2020

Concurrency and Computation

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Summary In the new millennium, a myriad of large‐scale applications (e.g., social networks, ecommerce, internet of things, and scientific experiments) often generate large volumes of data. Given their volumes, their heterogeneous and distributed nature, the management of such data constitutes a challenge for distributed systems, particularly cloud computing. In this regard, data replication is a well‐known and effective data management technique that consists in creating multiple copies of the same data in different storage resources. In this article, we propose a popularity and correlation based data replication strategy, called PCDR. The main idea of our strategy is to replicate a set of the most popular correlated file groups based on files access history analysis. In this respect, the popularity of the files in each group is determined while taking into consideration temporal locality. Moreover, a clustering technique is used to find groups of correlated files. Using the CloudSim simulator, extensive experimentations show that our proposed strategy outperforms other strategies for several evaluation metrics.

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Section: Introductionmentioning

confidence: 99%

Popularity and correlation aware data replication strategy based on half‐life concept and clustering in cloud system

Chellouf

Hamrouni

2020

Concurrency and Computation

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Placing big graph into cloud for parallel processing with a two-phase community-aware approach

Zeng

2019

Future Generation Computer Systems

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Grouping-Aware Data Placement in HDFS for Data-Intensive Applications Based on Graph Clustering

Cited by 2 publications

References 6 publications

Popularity and correlation aware data replication strategy based on half‐life concept and clustering in cloud system

Popularity and correlation aware data replication strategy based on half‐life concept and clustering in cloud system

Placing big graph into cloud for parallel processing with a two-phase community-aware approach

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