Hadoop-Based Model of Mass Data Storage

Yang Li

doi:10.4028/www.scientific.net/AMM.513-517.632

Paper Titles

A Research of Information Management System Based on Data Mining
p.616

A Research of the Application of the RFID in the Library Management
p.620

Algorithm for Automatic Deciphering of Mono-Alphabetical Substituted Cipher Realized in MS Excel Spreadsheet
p.624

The Security Analysis of WLAN Protocol Based on 802.11i
p.628

Hadoop-Based Model of Mass Data Storage
p.632

Exploration of the Risk Management Information System for the Large-Scale Game Design
p.635

Electronic Commerce Personalized Recommendation Model under Cloud Computing Environment
p.639

Electronic Commerce Recommendation Mechanism Based on QoS and Bayesian Model
p.643

Research on the Project Studio System of Computer Multimedia
p.647

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 513-517Hadoop-Based Model of Mass Data Storage

Hadoop-Based Model of Mass Data Storage

Abstract:

Aiming at more and more data produced by network, it is extremely important to manage and store these data by using mass data storage platform. This paper presents a method of managing rationally and storing mass data based on distributed computing technique. It is based on Hadoop distributed platforms, mainly using the HDFS distributed file system, MapReduce parallel computing models and Hbase distributed database technology as massive data processing methods, to achieve the efficient storage. The model can overcome the existing deficiencies of the current means of storage and solve the problems of mass data in storage, which has good scalability and reliability, thus the efficiency of storage can be further improved.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 513-517)

Pages:

632-634

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.513-517.632

Citation:

Cite this paper

Online since:

February 2014

Authors:

Yang Li*

Keywords:

Hadoop, HDFS, MapReduce

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Apache Hadoop[EB/OL]. (2010-03-25).

Google Scholar

[2] Zaharia M, Konwinski A, Joseph A D, et al. Improving MapReduce Performance in Heterogeneous Environments[C]/ Proc. of OSDI'08. San Diego, USA: [s. n. ], (2008).

Google Scholar

[3] Polo J, Nadal D, Carrera D, et al. Adaptive Task Scheduling for Multijob Mapreduce Environments[EB/OL]. (2010-03-25).

Google Scholar

[4] Jiang D, Ooi B C, Shi L, et al. The Performance of MapReduce: An In-depth Study[J]. PVLDB, 2010, 3(1): 1207-1218.

Google Scholar

[5] Kambatla K, Pathak A, Pucha H. Towards Optimizing Hadoop Provisioning in the Cloud[C]/Proc. of the 1st ACM Symposium on Cloud Computing. New York, USA: ACM Press, 2010: 137- 142.

Google Scholar