Software aging prediction framework based on optimized TCN model with grey correlation analysis

Abstract

The temporal convolutional network (TCN) model has the characteristics of strong parallelism and stable gradient in time series processing. The structural depth of the model is related to the input length, convolution kernel and dilation factor. In order to further improve the accuracy of prediction, this paper proposes a software aging prediction framework based on TCN model optimized by grey relational analysis. Collecting available memory data as the input of the framework, determine the length of the input nodes of the TCN model through gray correlation analysis, and then conduct training and prediction, and evaluate the efficiency of the model by checking the average error between the predicted output memory and the actual memory. Then change the length of the input chunk to carry out a comparative experiment, which verifies the effectiveness of the grey relational degree analysis.