ALSTM: An Attention-based LSTM Model for Multi-Scenario Bandwidth Prediction

Bandwidth-sensitive applications rely on the accurate estimation of the bottleneck bandwidth. The real-time bandwidth prediction enables the application to cope with bandwidth fluctuation and adjust the transmission strategy to improve the Quality of Experience (QoE) of user. The traditional bandwidth prediction model hardly considers the bandwidth characteristics in various scenarios, making it challenging to achieve high accuracy. In this paper, we propose ALSTM model, which is based on the Long Short Term Memory (LSTM) recurrent neural network and the attention mechanism for multi-scenario bandwidth prediction. Firstly, we conduct the bandwidth trajectories feature analysis, and then we adopt the Support Vector Machine (SVM) to classify scenarios based on the bandwidth characteristics. Secondly, we apply an attention mechanism to assign weights to the input of the bandwidth series, and the attention feature is utilized to effectively select the feature sequences as input to the LSTM model for the prediction. The experimental results show that the ALSTM reduces the Root Mean Square Error (RMSE) by 20%, and the Mean Average Error (MAE) is improved by 26%. For practical applications, we adopt the pre-trained SVM model for real-time scenario detection, dynamic switch the corresponding ALSTM model, and the switching success rate is up to 86%. In addition, by deploying the proposed bandwidth prediction model ALSTM, the DASH's QoE has increased by more than 25%.