Cloud server aging prediction method based on self-attention mechanism DLSTM

A cloud server and aging prediction technology, which is applied in prediction, neural learning methods, design optimization/simulation, etc., can solve the problem of inaccurate cloud server aging prediction, and achieve the effect of improving prediction accuracy and high prediction accuracy

Pending Publication Date: 2022-05-17
XIAN UNIV OF TECH
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Problems solved by technology

[0005] The purpose of the present invention is to provide a cloud server aging prediction method based on the self-attention mechanism DLSTM, which solves the problem

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  • Cloud server aging prediction method based on self-attention mechanism DLSTM
  • Cloud server aging prediction method based on self-attention mechanism DLSTM
  • Cloud server aging prediction method based on self-attention mechanism DLSTM

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Example Embodiment

[0077] Embodiments

[0078] The present embodiment uses idle memory as an aging index to collect the idle memory time series data of the actual running cloud server. Plot the value every 20 points, such as Figure 4 as shown. The prediction method of cloud server aging prediction method based on the self-attention mechanism DLSTM predicts the results with the original data of the cloud server Figure 5 as shown. The specific steps are as follows:

[0079] Step 1, collect the data indicators of the aging of the ECS, obtain the time series data of the ECS resources and performance parameters, the resources and performance parameters are: idle memory;

[0080] Step 2, preprocess the sequence data.

[0081] Step 2.1, perform a first-order differential sequence on the sequence data to obtain a differential sequence;

[0082] Step 2.2 converts a first-order differential data series into a time step matrix in which each element contains a data fragment of time step length. The time step us...

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Abstract

The invention discloses a cloud server aging prediction method based on a self-attention mechanism DLSTM, and the method comprises the following steps: 1, collecting the data indexes of the aging condition of a cloud server, and obtaining the time series data of the resources and performance parameters of the cloud server; 2, preprocessing the sequence data to obtain a preprocessed data set; 3, dividing the cloud server aging data preprocessed in the step 2 into a training set and a test set; 4, constructing a DLSTM prediction model of the aging data time sequence of the cloud server based on the attention mechanism; step 5, training the DLSTM prediction model by using the training set data; and step 6, predicting test set data by using the trained DLSTM prediction model, and performing performance evaluation on the DLSTM prediction model. According to the method, the problem that the aging condition of the cloud server which runs for a long time and is large in data volume is not accurately predicted by a traditional prediction method is solved.

Description

technical field [0001] The invention belongs to the technical field of time series prediction, and relates to a cloud server aging prediction method based on a self-attention mechanism DLSTM (Deeplong short-term memory). Background technique [0002] Cloud computing includes a variety of computing resources, providing secure and fast access to cloud computing services and data storage services. Cloud server is one of the important supporting technologies of cloud computing. Its high scalability, high flexibility and high cost performance enable people to obtain corresponding services according to their own needs, which can save costs and improve resource utilization. However, during the continuous operation of the server, software aging phenomenon began to appear. Software aging is caused by the accumulation of error conditions such as resource leaks, unreleased file locks, and unterminated processes, which lead to system performance degradation or even system crashes, whic...

Claims

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Application Information

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IPC IPC(8): G06F30/27G06N3/04G06N3/08G06Q10/04G06F119/04
CPCG06F30/27G06Q10/04G06N3/08G06F2119/04G06N3/044
Inventor 孟海宁张嘉薇杨哲朱磊童新宇李维郑毅黑新宏
Owner XIAN UNIV OF TECH
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