Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)

A Study on Load Balancing Techniques for Task Allocation in Big Data Processing

Authors
Xiaohong Jin, Hui Li, Yanjun Liu, Yanfang Fan
Corresponding Author
Xiaohong Jin
Available Online March 2017.
DOI
https://doi.org/10.2991/ifmca-16.2017.34How to use a DOI?
Keywords
Big Data; Job Schedule; Distributed Computing; Clustering; Load Balancing
Abstract
This paper introduces the task allocation techniques with clustering and load balancing in the field of Internet to the field of image processing job allocation of alternative big data. It designs and realizes a load balancing cluster architecture for the alternative big data, and an improved load balancing algorithm applicable to large-scale image processing. The experimental results show that the cluster architecture can execute task allocation and data processing continuously and stably, and the improved load balancing algorithm could improve the processing efficiency about 10% and more .
Open Access
This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)
Series
Advances in Engineering Research
Publication Date
March 2017
ISBN
978-94-6252-307-4
ISSN
2352-5401
DOI
https://doi.org/10.2991/ifmca-16.2017.34How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Xiaohong Jin
AU  - Hui Li
AU  - Yanjun Liu
AU  - Yanfang Fan
PY  - 2017/03
DA  - 2017/03
TI  - A Study on Load Balancing Techniques for Task Allocation in Big Data Processing
BT  - Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)
PB  - Atlantis Press
SP  - 212
EP  - 218
SN  - 2352-5401
UR  - https://doi.org/10.2991/ifmca-16.2017.34
DO  - https://doi.org/10.2991/ifmca-16.2017.34
ID  - Jin2017/03
ER  -