Open Access

ARTICLE

Cloud Resource Integrated Prediction Model Based on Variational Modal Decomposition-Permutation Entropy and LSTM

Xinfei Li², Xiaolan Xie^1,2,*, Yigang Tang², Qiang Guo^1,2

1 Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin, 541006, China
2 School of Information Science and Engineering, Guilin University of Technology, Guilin, 541006, China

* Corresponding Author: Xiaolan Xie. Email:

Computer Systems Science and Engineering 2023, 47(2), 2707-2724. https://doi.org/10.32604/csse.2023.037351

Received 31 October 2022; Accepted 21 December 2022; Issue published 28 July 2023

Abstract

Predicting the usage of container cloud resources has always been an important and challenging problem in improving the performance of cloud resource clusters. We proposed an integrated prediction method of stacking container cloud resources based on variational modal decomposition (VMD)-Permutation entropy (PE) and long short-term memory (LSTM) neural network to solve the prediction difficulties caused by the non-stationarity and volatility of resource data. The variational modal decomposition algorithm decomposes the time series data of cloud resources to obtain intrinsic mode function and residual components, which solves the signal decomposition algorithm’s end-effect and modal confusion problems. The permutation entropy is used to evaluate the complexity of the intrinsic mode function, and the reconstruction based on similar entropy and low complexity is used to reduce the difficulty of modeling. Finally, we use the LSTM and stacking fusion models to predict and superimpose; the stacking integration model integrates Gradient boosting regression (GBR), Kernel ridge regression (KRR), and Elastic net regression (ENet) as primary learners, and the secondary learner adopts the kernel ridge regression method with solid generalization ability. The Amazon public data set experiment shows that compared with Holt-winters, LSTM, and Neuralprophet models, we can see that the optimization range of multiple evaluation indicators is 0.338~1.913, 0.057~0.940, 0.000~0.017 and 1.038~8.481 in root means square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and variance (VAR), showing its stability and better prediction accuracy.

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Keywords

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