A software configuration recommendation method
A recommendation method and software configuration technology, applied in the field of big data, can solve problems such as large differences in cluster environments, complex configurations, and inability to make server configurations, so as to achieve the effect of no performance waste
Active Publication Date: 2021-11-02
上海华云互越数据技术有限公司
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Problems solved by technology
Due to the large differences in the cluster environment, the variety of data formats to be processed, and the dynamic changes in the data volume, the configuration of the parameters of these service components is relatively complicated, which also leads to a certain extent that it is impossible to make a reasonable Problems with the server configuration used to deploy an application
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[0028] The present invention will be described in detail below in conjunction with the implementations shown in the drawings, but it should be noted that these implementations are not limitations of the present invention, and those of ordinary skill in the art based on the functions, methods, or structural changes made by these implementations Equivalent transformations or substitutions all fall within the protection scope of the present invention.
[0029] The core idea of the present invention is summarized as follows: to collect user data, when using a certain software configuration, if the stress environment of the user 30 does not change significantly, but the software configuration is quickly changed, then we consider this software The configuration is not high (that is, the performance cannot meet the needs of the user 30); on the contrary, if the user 30 maintains a configuration for a long time, we think that under this stressful environment, the current configuratio...
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The present invention provides a software configuration recommendation method. The recommendation engine collects the performance sample data of the host in the cloud platform within a set time period, and calculates the average value of the performance sample data to obtain a software pressure environment configuration table; adopts mean value clustering The algorithm calculates the similarity of each row of data in the software pressure environment configuration table to obtain the pressure environment cluster table; the score of the same software under the same pressure category is determined by the variation coefficient and configuration duration, and the configuration with the highest stability is selected as The standard configuration that best matches the specific stress category; the recommendation engine receives the stress environment input by the user, and recommends the standard configuration with the highest similarity to the user. Through the present invention, the purpose of recommending the most reasonable software or software configuration to the user according to the data processing requirements and the user history program is realized, so that the performance of the software recommended to the user meets the user's demand and there is no performance waste.
Description
technical field [0001] The invention relates to the technical field of big data, in particular to a software configuration recommendation method. Background technique [0002] Stress testing is a test to obtain the maximum service level that the system can provide by determining the bottleneck or unacceptable performance point of a system. Stress testing is done to find out under what conditions the performance of the system becomes unacceptable. [0003] Currently, the master-slave model can be used to stress test the system. Among them, after the master node in the stress test system starts each slave node, it assigns a stress test quota to each slave node. After the stress test session between the master node and the slave node, the slave node simulates the terminal user and the server in the system under test to establish After connecting, it communicates with the server and synchronizes the pressure measurement statistics with the server to the master node. The master...
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IPC IPC(8): G06F11/34G06F9/445G06F16/9535
CPCG06F9/44505G06F11/3452G06F11/3476G06F2201/865
Inventor 许广彬谭瑞忠濮天晖胡军张银滨郭晓王子豪
Owner 上海华云互越数据技术有限公司