Abstract: In a distributed storage and computing framework, traditional streaming data mining techniques are inefficient when processing massive amounts of data. In this paper, we take the copy in cloud storage as an allocatable resource for scheduling and propose a RepRM strategy to improve the efficiency of data mining and analysis. The key idea of this work is to take the data copy as the resource to be allocated, and use the backward inference method of dynamic programming to solve the data copy ratio, the optimal number of copies is obtained. Experiments and observations have proved that compared with the traditional scheduling method of Hadoop, after adopting the RepRM strategy scheduling, the memory resources of the homogeneous cluster are saved by about 40-50% during parallel mining of streaming data, and the throughput rate is increased by 20% to 30%.

Shufan Li, Siyuan Yu and Fang Xiao, “Replica Scheduling Strategy for Streaming Data Mining” International Journal of Advanced Computer Science and Applications(IJACSA), 13(5), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130503

@article{Li2022,
title = {Replica Scheduling Strategy for Streaming Data Mining},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130503},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130503},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {5},
author = {Shufan Li and Siyuan Yu and Fang Xiao}
}