A Study on Load Balancing Techniques for Task Allocation in Big Data Processing
Xiaohong Jin, Hui Li, Yanjun Liu, Yanfang Fan
Available Online March 2017.
- https://doi.org/10.2991/ifmca-16.2017.34How to use a DOI?
- Big Data; Job Schedule; Distributed Computing; Clustering; Load Balancing
- 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.
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 -