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http://dx.doi.org/10.14400/JDC.2021.19.3.211

Asymmetric data storage management scheme to ensure the safety of big data in multi-cloud environments based on deep learning  

Jeong, Yoon-Su (Dept. of Information Communication & Engineering, Mokwon University)
Publication Information
Journal of Digital Convergence / v.19, no.3, 2021 , pp. 211-216 More about this Journal
Abstract
Information from various heterogeneous devices is steadily increasing in distributed cloud environments. This is because high-speed network speeds and high-capacity multimedia data are being used. However, research is still underway on how to minimize information errors in big data sent and received by heterogeneous devices. In this paper, we propose a deep learning-based asymmetric storage management technique for minimizing bandwidth and data errors in networks generated by information sent and received in cloud environments. The proposed technique applies deep learning techniques to optimize the load balance after asymmetric hash of the big data information generated by each device. The proposed technique is characterized by allowing errors in big data collected from each device, while also ensuring the connectivity of big data by grouping big data into groups of clusters of dogs. In particular, the proposed technique minimizes information errors when storing and managing big data asymmetrically because it used a loss function that extracted similar values between big data as seeds.
Keywords
Cloud; Big data; Asymmetry; Deep learning; Link information; Load balance;
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