System and index optimization method and device

Abstract

This application provides a system and its index optimization method and device, the method includes: obtaining all used index parameters and their values ​​of the system, and all index parameters and their values ​​to be selected; based on the preset data dimensionality reduction algorithm, the Perform dimension reduction processing on all the used index parameters and their values ​​to obtain the corresponding characteristic parameter sets and their values; use the values ​​of the characteristic parameter sets as input, and use the values ​​of all the index parameters to be selected as the target output , train the preset machine learning model, and obtain the predicted value of the numerical value of all the index parameters to be selected; obtain the difference measurement value between the numerical value of each index to be selected and its corresponding predicted value in all the index parameters to be selected; select the A preset number of indicators to be selected whose difference measurement values ​​are the largest are used as new indicators of the system. The application can improve index optimization efficiency and reduce system performance jitter.

Description

technical field [0001] The present application relates to the technical field of data processing, in particular to a system and its index optimization method and device. Background technique [0002] As time goes by, in the actual application process of some systems, the relevant statistical information is continuously accumulated and enriched, and through the analysis and processing of statistical information, it may be found that new indicators need to be added and the system reconstructed on this basis , to improve its performance. [0003] With the continuous development and changes of computer network and information technology, some systems have the function of automatically constructing new index sets. These new index sets can help the system adapt to new changes, thereby improving system performance. However, since the number of new indicators in the new indicator set is often large, and some systems (such as online systems) have limited resources, it may not be pos...

AI Technical Summary

Problems solved by technology

[0005] However, the inventors of the present application found that the above method needs to traverse each new index in the new index set one by one, which is very time-consuming

At the same time, if the existing system is already relatively complex, adding a single indicator may not actually bring actual performance improvement to the system

Sometimes, the jitter of system performance may even be caused by the selection of random parameters. Therefore, in general, it is often necessary to add a set of indicators at the same time to see the actual effect.

According to the existing technology, the traversal complexity of selecting a set of indicators from a new indicator set is exponential, which will occupy too many system resources, and it is almost difficult to implement in engineering

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