Incentive Learning-Based Triplet Attention Enabled BILSTM Model for Network Traffic Congestion Prediction
Keywords:
Network traffic congestion prediction, incentive learning, triplet Attention mechanism, rat fierce Hunting optimization algorithm, online video streaming.Abstract
Network traffic congestion creates a significant threat in the realm of online live video streaming, affecting the quality of service and user experience. The congestion is caused due to factors including excessive user demand, restricted bandwidth, or ineffective data routing. The predictive models employed for network congestion in online streaming video may not adapt well to dynamic changes in network conditions and challenges associated with capturing long-range dependencies, limiting their ability to provide accurate congestion predictions in evolving environments. To mitigate these limitations this research proposed an incentive learning-based triplet attention enabled rat fierce Hunting optimized Bidirectional Long Short Term Memory (Incentive-RF-Tri ASTM) for network traffic congestion prediction in online streaming video. The incentive learning mechanism incorporates a reward system that encourages the model to prioritize congestion prediction in online live video streaming. The BiLSTM architecture known for capturing temporal dependencies is employed for the sequential nature of network traffic data. The use of the triplet Attention mechanism improves the model's focus on pertinent regions in the input data, improving its ability to discern congestion patterns effectively. To further refine the parameters of the classifier the RFHO algorithm combines the social behavior along with the selection and searching traits, which achieves a more robust and efficient tuning of the model's parameters, optimizing its performance in congestion detection. The experimental outcomes exhibit the efficacy of the Incentive-RF-Tri ASTM method in accurately predicting traffic congestion in terms of accuracy is 95.97%, specificity is 96.08%, and MSE is 0.22 for the Darpa99week1 dataset
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