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A Study on Load Balancing Techniques for Task Allocation in Big Data Processing
2017
Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)
unpublished
Preserved Fulltext
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
doi:10.2991/ifmca-16.2017.34
fatcat:3jk4wuucxnfphk2a6hiuobo5fa