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Method for predicting cloud server software aging based on EGGM model

A cloud server and model prediction technology, applied in neural learning methods, biological neural network models, instruments, etc., can solve the problem of low prediction accuracy and achieve high-precision prediction and analysis effects

Pending Publication Date: 2021-12-14
XIAN UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] The purpose of the present invention is to provide a method for predicting cloud server software aging based on the EGGM model, which solves traditional machine learning prediction methods (such as SVM, GRU, etc.), and prediction methods based on traditional mathematical statistical models (such as MLR, time series method, etc.) As well as traditional time series decomposition methods (such as EMD decomposition, wavelet decomposition, etc.) when predicting cloud server software aging, due to the randomness and non-periodic components of cloud server performance index data, the prediction accuracy is too low

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  • Method for predicting cloud server software aging based on EGGM model
  • Method for predicting cloud server software aging based on EGGM model
  • Method for predicting cloud server software aging based on EGGM model

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Embodiment

[0085] The embodiment of this application uses the cloud server database query response time series data as an example, and the EEMD decomposition data flow is as follows figure 1 As shown, the EEMD decomposition data results are as follows figure 2 As shown, the GA chromosome is coded as image 3 As shown, the specific implementation process of this method is as follows Figure 4 As shown, the structure diagram of a single GRU hidden layer is as follows Figure 5 As shown, the cloud server database query response time series is as follows Figure 6 As shown, the prediction accuracy comparison between EEMD-GAGRU-MLR model and GAGRU model, SVM model and MLR model is as follows Figure 7 As shown, the absolute error comparison of EEMD-GAGRU-MLR model, GAGRU model, SVM model, and MLR model is as follows Figure 8 shown; the error comparison of different models is shown in Table 2 below, and the root mean square error RMSE, mean absolute error MAE, and mean absolute percentag...

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Abstract

The invention discloses a method for predicting cloud server software aging based on an EGGM model. The method comprises the following steps: firstly, decomposing cloud server performance index data through EEMD to obtain a plurality of groups of IMF components and Residual components with high and low frequencies, then predicting the high-frequency IMF components by using a GA-GRU model formed by GA optimization GRU network hyper-parameters, predicting the low-frequency IMF components and Residual components by using an MLR model, and finally superposing and reconstructing the obtained component prediction results to obtain a complete prediction result. The problems that a traditional method is prone to falling into a local optimal solution in the prediction process, and the prediction precision is too low are solved; according to the method, the fluctuation change characteristics of cloud server software aging data can be accurately extracted, the local characteristics of cloud server performance index data can be accurately predicted, and finally high-precision prediction and analysis of cloud server software aging performance index parameters are realized.

Description

technical field [0001] The invention belongs to the technical field of time series prediction, and relates to a method for predicting cloud server software aging based on an EGGM model. Background technique [0002] With the rapid development of the Internet, many small and medium-sized Internet service providers, such as online game companies, Internet business companies, and financial securities companies, usually adopt cloud servers because they do not have sufficient technical strength or to save money and facilitate server operation and maintenance. solution, which enables cloud computing technology and cloud server business to flourish. The main characteristics of cloud servers are long-term operation, high scalability and frequent resource exchange. With the rapid increase in the complexity and concurrency of cloud server software, the service pressure on cloud servers is increasing. During the long-term high-load operation of the cloud server, with the accumulation...

Claims

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Application Information

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IPC IPC(8): G06F11/34G06N3/04G06N3/08
CPCG06F11/3447G06N3/086G06N3/044G06N3/045
Inventor 孟海宁周荣李维郑毅朱磊黑新宏
Owner XIAN UNIV OF TECH
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