Research on Parallel Optimization of Cloud Computing Based on MapReduce
SUN Fan
LI Xiaoguang
ZHANG Yong
XU Guanghu
XIE Haijiang
Abstract：Aiming at the problem that the multi-attribute of the cloud storage data of the smart grid is difficult to be accurately scheduled,a parallel algorithm based on MapReduce is proposed to solve the storage efficiency.The data of the cloud data integrity model is given by the challenge-response method.The structure to reduce the number of data identification according to MapReduce definition gives five algorithms to define the data segmentation processing,through the design of the corresponding Map and Reduce function to achieve the data generation algorithm parallelization,by simulating smart grid data the results show that the data segmen-tation of the 2M data is about 1.13s in the TagGen phase,and the overhead time is 16ms after the block,and the computational cost of the challenge phase is different from that of the challenge data block.The corresponding increase increases,but the overall growth rate is very small,the calculation of the calculation phase is calculated by the number of data blocks and the number of data and the decision.
Keywords：cloud computingMapReduceparallel optimizationdata blockdata fragmentation
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 705-710 )