Abstract
The rapid economic development has become the theme of today’s social development. With the rapid development of Internet technology, the amount of information has shown an explosive growth. While facing busy work every day, people also need to face a very large amount of data. A more precise expression means that a large amount of data storage space and a large amount of redundant data copies are needed. This paper mainly studies the algorithm design of artificial intelligence technology in edge cloud computing edge node storage optimization algorithm. The user submits a virtual machine request, and the constraint optimization algorithm allocates the request to a suitable server for execution according to the related information of the virtual machine request submitted by the user and the use of data center server resources, and combines the virtual machine's artificial intelligence data mining technology to minimize a large number of servers meet user requests, thereby ultimately achieving the goal of reducing energy consumption in edge cloud computing data centers. Experimental data shows that the analysis and positioning optimization network has an absolute impact on the overall performance of the detection and recognition network. When the score threshold is 7 and 8, the MAP improvement effect is the greatest. Experimental results show that artificial intelligence technology can reduce the energy consumption of edge cloud computing data centers.
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Acknowledgements
This work was supported by National Natural Science Foundation of China (U1504602), Key Scientific Research Projects of Colleges and Universities of Henan Provincial Department of Education (no. 21B520029, no. 20A520047), and Key Development Plan Project of the Science and Technology Department of Henan Province (no. 212102210400).
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Zhang, D. Storage optimization algorithm design of cloud computing edge node based on artificial intelligence technology. J Ambient Intell Human Comput 14, 1461–1471 (2023). https://doi.org/10.1007/s12652-021-03272-z
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DOI: https://doi.org/10.1007/s12652-021-03272-z