Dynamic replica management model based on LSTM

Meixuan Li; Yicai Huang; Bin Yu

doi:10.1117/12.3025712

8 April 2024 Dynamic replica management model based on LSTM

Meixuan Li, Yicai Huang, Bin Yu

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 1309014 (2024) https://doi.org/10.1117/12.3025712
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

Aiming at the problem that the static replica management strategy in the cloud storage system is unable to adapt to the changing user access requirements over time, which affects the access efficiency and storage resource utilization of the system, the LSTM file heat prediction model is constructed, so as to effectively extract the information in the heat sequence and alleviate the problems of gradient disappearance and gradient explosion in the recurrent neural network to a certain extent. Suitable appropriate activation function and optimizer are selected to reduce the model prediction error, improve the prediction accuracy and convergence rate, and solve the problem of model convergence instability. On this basis, the dynamic replica management model is optimized, the minimum number of replicas is determined according to data availability requirements, and the LSTM model is used to predict the future replica popularity and define the replica popularity threshold in the system. Based on the future popularity of replicas, the number of replicas for files increases or decreases, while ensuring file availability and saving storage costs. The experimental results show that the heat prediction result of the model in this paper is better than that of the gray prediction model, which proves the effectiveness and progressiveness of the model.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Meixuan Li, Yicai Huang, and Bin Yu "Dynamic replica management model based on LSTM", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 1309014 (8 April 2024); https://doi.org/10.1117/12.3025712

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available