Abstract
The massive data streams analysis in the Smart Grids data processing system is very important, especially in the high-concurrent read and write environments where supporting the massive real-time streaming data storage and management. The computational and stored requirements for Smart Grids can be met by utilizing the Cloud computing. In order to support the robust, affordable and reliable power streaming data analysis and storage, in this paper, we propose a power data streams analysis strategy based on Hadoop scheduling optimization for smart grid monitoring application. The proposed strategy combined with the flexible resources and services shared in network, omnipresent access and parallel processing features of cloud computing. Finally, the simulation results show that proposed strategy can effectively improve the efficiency of computing resource utilization and achieve the massive information concurrent processing ability.
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Acknowledgment
The research work was supported by the Fundamental Research Funds of the Central University under Grant no. N120323009, the Natural Science Foundation of Hebei Province under Grant No. F2014501055, the Program of Science and Technology Research of Hebei University No. ZD20132003, the Natural Science Foundation of Liaoning Province under Grant No.201202073, and the National Natural Science Foundation of China under Grant No.61403069, No.61473066 and No.61374097.
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Zhou, F., Song, X., Han, Y., Gao, J. (2015). A Data Streams Analysis Strategy Based on Hadoop Scheduling Optimization for Smart Grid Application. In: Wang, J., Yap, C. (eds) Frontiers in Algorithmics. FAW 2015. Lecture Notes in Computer Science(), vol 9130. Springer, Cham. https://doi.org/10.1007/978-3-319-19647-3_30
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DOI: https://doi.org/10.1007/978-3-319-19647-3_30
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