KEYWORDS: Data transmission, Data integration, Power grids, Data centers, Blockchain, System integration, Data processing, Computer security, Data storage, Information fusion
The current traditional electric power big data integration management method realizes the intelligent scheduling and management of electric power data through load prediction of electric power data, which leads to poor data transmission performance due to the lack of pre-processing of the original data. In this regard, a research on the integrated management method of electric power big data based on the grid data center is proposed. By using asymmetric encryption technology to generate private, cipher and public keys of blocks, the signature encryption and login authentication of the collection layer are completed, pre-processing of power data is realized, and power management security analysis is conducted, and the integrated power big data management architecture is proposed. In the experiment, the data transmission performance of the proposed electric power data integration management method is verified. The analysis of the experimental results shows that the power data integration management platform built by the proposed method has a low transmission delay and has a better data transmission performance.
KEYWORDS: Computer simulations, Computer programming, Systems modeling, Mobile devices, Mathematical modeling, Internet of things, Detection and tracking algorithms, Data processing
In cloud-based edge computing systems, edge server placement is crucial to many applications response time. Generally, edge server placement is always modeled as a multi-objective optimization problem and solved with integer programming algorithms. However, these algorithms are not well scalable, and parameters used in these algorithms depend heavily on experiences. In this paper, we propose an optimized k-mean based edge server placement method. We use k-mean to cluster system sources and application loads and sort the application load to apply the most applicable server to the application. Experimental studies over synthetic data sets validate effectiveness of the method.
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