Cloud native resource dynamic prediction method and device, computer equipment and storage medium
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A dynamic prediction and resource technology, applied in the information field, can solve the problems of low accuracy and high complexity of prediction mutation points
Inactive Publication Date: 2021-03-26
SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
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[0005] The purpose of the embodiment of the present application is to propose a cloud-native resource dynamic prediction method, device, computer equipmen...
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Embodiment 1
[0060] see figure 1 as well as figure 2 , shows a flow chart of an embodiment of the method for dynamically predicting cloud-native resources according to Embodiment 1 of the present application. For convenience of description, only the parts related to the present application are shown.
[0061] In step S1, a dynamic prediction request sent by a user terminal is received.
[0062] In this embodiment, in this embodiment, the dynamic prediction request is for the user to have an in-depth understanding of the characteristics of cloud-native cluster resources, so as to select an appropriate resource prediction model and optimize it in combination with specific scenarios, and provide effective resources for dynamic resource allocation. An action request issued based on a decision based on value.
[0063] In step S2, the local database is read in response to the dynamic prediction request, and the resource data and performance index data to be predicted of the container load in ...
Embodiment 2
[0112] further reference Figure 5 , as for the above figure 2 To implement the method shown, this application provides an embodiment of a device for dynamic prediction of cloud-native resources, which is the same as figure 2 Corresponding to the illustrated method embodiments, the apparatus can be specifically applied to various electronic devices.
[0113] Such as Figure 5 As shown, the cloud-native resource dynamic prediction device 100 described in this embodiment includes: a request receiving module 101, a request response module 102, a correlation ranking module 103, a threshold definition module 104, a time series data acquisition module 105, a data extension module 106, A model training module 107 and a data prediction module 108 . in:
[0114] A request receiving module 101, configured to receive a dynamic prediction request sent by a user terminal;
[0115] In this embodiment, in this embodiment, the dynamic prediction request is for the user to have an in-de...
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Abstract
The embodiment of the invention belongs to the technical field of information, and relates to a cloud native resource dynamic prediction method, which comprises the steps of performing relevancy sorting on acquired to-be-predicted resource data and performance index data based on a Pearson correlation coefficient to obtain a correlation relationship between the to-be-predicted resource data and the performance index data; defining a correlation threshold based on the correlation relationship; taking the performance index data greater than or equal to the relevancy threshold value as performance index time series data; performing transverse data expansion on the performance index time series data to obtain training data and test data; inputting the training data into a constructed time sequence neural network model for training to obtain a trained time sequence neural network prediction model; and inputting the test data into the time sequence neural network prediction model for prediction operation to obtain a resource prediction result. The invention further provides a cloud native resource dynamic prediction device, computer equipment and a storage medium. The prediction complexity can be reduced, and the prediction accuracy can be improved.
Description
technical field [0001] The present application relates to the field of information technology, and in particular to a cloud-native resource dynamic prediction method, device, computer equipment, and storage medium. Background technique [0002] The rapid development of cloud-native technology has led to a sharp increase in the number of users and data scale, which has brought severe problems and challenges to the resource management of cloud-native clusters. On the one hand, user requests for resources are very frequent and diverse, and existing resource predictions can only make accurate predictions for traditional periodicity, but cannot accurately predict the emergence of mutation points; on the other hand, real-time online services The hybrid deployment with offline jobs improves the performance of cloud-native clusters to a certain extent, but this hybrid mode also brings about resource competition and performance degradation, which further increases the complexity of r...
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