Traffic Forecasting of Back Servers Based on ARIMA-LSTM-CF Hybrid Model

Abstract

Abstract Accurate server traffic prediction can help enterprises formulate network resource allocation strategies in advance and reduce the probability of network congestion. Traditional prediction models ignore the unique data characteristics of server traffic that can be used to optimize the prediction model, so they often cannot meet the long-term and high-precision prediction required by server traffic prediction. To solve this problem, this paper establishes a hybrid model ARIMA-LSTM-CF, which combines the advantages of linear and nonlinear models, as well as the periodic fluctuation characteristics of server traffic data obtained from banks. In addition, this paper also uses the optimized K-means clustering method to extract the traffic data of workdays and non workdays. The results show that the new hybrid model performs better than the single ARIMA and LSTM models in predicting the long-term trend of server traffic. RMSE (root mean square error) and MAE (mean absolute error) are reduced by 50%. R2 score index reached 0.64. The results show that the model can effectively extract the data characteristics of server traffic data, and the model has accurate and stable long-term prediction ability.