A Data Streams Analysis Strategy Based on Hadoop Scheduling Optimization for Smart Grid Application

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.