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

DOI

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 .

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - 10.2991/ifmca-16.2017.34
ID  - Jin2017/03
ER  -