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 .
In REF , Jin Xiaohong et al. have introduced 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.
A Study on Load Balancing Techniques for Task Allocation in Big Data Processing
Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)
Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)
Computer Science