Analysis and Experimental Study of HDFS Performance
Author:
Kalmukov Yordan,Marinov Milko,Mladenova Tsvetelina,Valova Irena
Abstract
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.
Publisher
Association for Information Communication Technology Education and Science (UIKTEN)
Subject
Management of Technology and Innovation,Information Systems and Management,Strategy and Management,Education,Information Systems,Computer Science (miscellaneous)
Cited by
1 articles.
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1. A Distributed Cache Mechanism of HDFS to Improve Learning Performance for Deep Reinforcement Learning;2022 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom);2022-12