DSE optimization method based on K-NN algorithm and device

Abstract

The invention provides a DSE optimization method based on a K-NN algorithm and a device thereof. The method comprises the steps of performing scheduling priority configuration on a traffic level in aTSN network model; training a K-NN algorithm, which replaces a traditional algorithm to execute schedulability analysis, in the DSE according to the configured scheduling priority to obtain a node proportion value meeting requirements; and comparing the node proportion value with a preset threshold value, if the node proportion value is smaller than the preset threshold value, using the K-NN algorithm, and if the node proportion value is greater than the preset threshold value, using a traditional algorithm in DSE to adjust the size of the preset threshold value. The DSE optimization method isrealized by combining a K-NN algorithm. A K-NN algorithm in a machine learning algorithm is used to replace an original schedulability analysis algorithm in traditional DSE. When the K-NN algorithm is judged to be infeasible, a traditional algorithm is still used, the calculation speed of schedulability analysis is increased, meanwhile, the false alarm rate is reduced, and the DSE optimization method and device capable of keeping high prediction accuracy and reducing false alarms are provided.

Description

technical field [0001] The invention belongs to the technical field of power communication networks, and in particular relates to a DSE optimization method and device based on a K-NN algorithm. Background technique [0002] As the design and configuration of TSN networks become more and more complex, design space exploration (DSE) algorithms are usually required to optimize the selection and configuration of TSN protocols. The DSE algorithm generally consists of three steps of iterative execution: creating candidate solutions, configuring solutions and performing schedulability analysis, and simulating and evaluating the performance of the TSN network. Among them, in the schedulability analysis of the solution, the original traditional algorithm can be replaced by machine learning algorithm, which is used to optimize the traditional DSE algorithm and provide a faster analysis algorithm solution, but the error prediction percentage of machine learning technology will decrease...

AI Technical Summary

Problems solved by technology

Among them, in the schedulability analysis of the solution, the original traditional algorithm can be replaced by machine learning algorithm, which is used to optimize the traditional DSE algorithm and provide a faster analysis algorithm solution, but the error prediction percentage of machine learning technology will decrease. Cannot be used in schedulability analysis because it is out of acceptable range

Images