CPU load trend prediction method based on IF-EMD-LSTM

A technology of trend prediction and forecast value, applied in the field of civil aviation information processing, can solve problems such as overfitting, difficulty in obtaining model parameters accurately, low prediction accuracy, etc., and achieve the effect of reducing the impact

Inactive Publication Date: 2021-01-01
CIVIL AVIATION UNIV OF CHINA
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AI Technical Summary

Problems solved by technology

Artificial intelligence methods are prone to fall into local optimum and overfitting, and the model parameters are not easy to obtain accurately, resulting in low prediction accuracy

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  • CPU load trend prediction method based on IF-EMD-LSTM
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  • CPU load trend prediction method based on IF-EMD-LSTM

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Embodiment Construction

[0102] In order to further understand the content, features and effects of the present invention, the following examples are given, and detailed descriptions are given below with reference to the accompanying drawings.

[0103] The invention proposes a CPU load trend prediction method based on IF-EMD-LSTM aiming at predicting the CPU usage rate of the server in the data center. First, the individual time series details, server CPU usage, are extracted from the Google Clusters dataset. After that, analyze the characteristics of the time series and analyze the applicability of each method. On the one hand, using IF can remove abnormal points in the data and improve the signal-to-noise ratio of the original data; on the other hand, the structure design of LSTM is more suitable for this time than the traditional forecasting model ARIMA. For sequence prediction, use the BA algorithm to optimize the LSTM model and construct the BA-LSTM model to reduce the impact of the subjective se...

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Abstract

The invention discloses a CPU load trend prediction method based on IF-EMD-LSTM, belongs to the technical field of civil aviation information processing, and is characterized in that the method comprises the following steps: S1, data preprocessing: employing an isolated forest algorithm to remove high abnormal points in data and improve the signal-to-noise ratio; S2, data decomposition: decomposing the input data into IMF components with different frequencies and residual errors by adopting an EMD algorithm; s3, optimizing the neural network, optimizing the LSTM initial weight and threshold byusing a BA algorithm, and constructing a BA-LSTM model by using the optimized value; and S4, neural network training: executing optimized LSTM network training on each group of IMF components, predicting each IMF and residual error through the independently optimized LSTM neural network, and reconstructing a prediction value from each prediction value. According to the invention, the real-time prediction error of the server management system on the CPU resource load is reduced.

Description

technical field [0001] The invention belongs to the technical field of civil aviation information processing, in particular to an IF-EMD-LSTM-based CPU load trend prediction method. Background technique [0002] In December 2016, the British National Air Service Company (NATS) failed to take off hundreds of flights due to failures in two system flight server channels. This failure caused a total of 120 flights to be canceled and 500 flights to be delayed for more than 45 days. minutes, affecting a total of about 100 million passengers. [0003] At 2 a.m. on August 8, 2016, a failure occurred at Delta Air Lines' main data center in Atlanta, bringing global computer and operational systems to a standstill. More than 650 flights were canceled, thousands of passengers were stranded at airports around the world, flight delays were severe, and millions of dollars were lost. [0004] A typical data center server can run hundreds or thousands of jobs with changing resources, but e...

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Application Information

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IPC IPC(8): G06F9/50G06N3/04
CPCG06F9/505G06N3/049G06N3/044
Inventor 李国陈茜王潇霏
Owner CIVIL AVIATION UNIV OF CHINA
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