Network traffic prediction study based on the adaptive attention mechanism

Computer network traffic refers to the total amount of data passing through the network in a certain period of time, and is an important parameter to measure the load and running status of the network. In the background of cloud service and network slicing, predicting network traffic demand is beneficial for network operators to adjust network resources and meet user demand. However, the actual network traffic demand is characterized by real-time and sudden, the traditional network traffic prediction model has the problems of less prediction time points and low prediction accuracy, and the prediction model based on neural network has the problems such as gradient and error accumulation. Therefore, improving the network prediction model and avoiding the above problems are the focus of the current research. In this study, based on the time series theory of network traffic data, we propose a network traffic prediction algorithm using a Self-Adaptive Attention Mechanism (AAM). This algorithm dynamically adapts to process input data in an adaptive manner, reducing computational complexity while minimizing information loss. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics are employed to evaluate the prediction model's performance. Experimental results indicate that compared to classical models like the Auto-regressive Integrated Moving Average Model (ARIMA) and Long Short-Term Memory networks (LSTM), AAM exhibits higher accuracy and better predictive performance on datasets with significant fluctuations. For instance, when predicting 6 time points, AAM reduces RMSE by approximately 16.50% and 61.40% compared to ARIMA on two datasets, respectively. When predicting 36 time points, the reduction is about 13.12% and 70.51%, and for the maximum of 144 time points, reductions are approximately 51.77% and 73.40%.