An optimized method of HDFS for massive small files storage

Author:

Jing Weipeng1,Tong Danyu2,Chen GuangSheng2,Zhao Chuanyu3,Zhu LiangKuan2

Affiliation:

1. College of Information and Computer Engineering, Northeast Forestry University, Harbin, China + Heilongjiang Computing Center

2. Northeast Forestry University, College of Information and Computer Engineering, Harbin, China

3. Heilongjiang Computing Center

Abstract

The development of the Internet-of-Things (IoT) and the Cyber-Physical System (CPS) has greatly facilitated many aspects of technological applications and development. This may lead to significant data growth, especially for small files. The analysis and processing of a large number of small files has become a crucial part of the development of IoT and CPS. Hadoop Distributed File Systems have become powerful platforms to store a larger amount of big data. However, this method has a number of issues when dealing with small files, such as substantial memory consumption and poor access. In this paper, a Dynamic Queue of Small Files (DQSF) algorithm is proposed to solve these problems. DQSF differentiates small files into different categories using an analytical hierarchal process that examines the performance of small files with different ranges across four indexes and determines the size of the dynamic queue according to the best system performance. Additionally, period classification is applied to preprocess the small files before storage, and the prefetching mechanism of the secondary index is used to process index tables. Experimental results show that this method could effectively reduce memory use and improve the storage efficiency of massive small files, which optimizes system performance.

Publisher

National Library of Serbia

Subject

General Computer Science

Cited by 12 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Enhanced Best Fit Algorithm for Merging Small Files;Computer Systems Science and Engineering;2023

2. Application Massive Data Processing Platform for Smart Manufacturing Based on Optimization of Data Storage;ACM Transactions on Management Information Systems;2022-12-31

3. A Simple Approach for Data Cleansing on Hadoop Framework using File Merging Technique;2022 Ninth International Conference on Software Defined Systems (SDS);2022-12-12

4. A New Merging Numerous Small Files Approach for Hadoop Distributed File System;2022 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON);2022-05-24

5. A Dynamic Repository Approach for Small File Management With Fast Access Time on Hadoop Cluster: Hash Based Extended Hadoop Archive;IEEE Access;2022

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3