Forecasting Network Interface Flow Using a Broad Learning System Based on the Sparrow Search Algorithm

Abstract

In this paper, we propose a broad learning system based on the sparrow search algorithm. Firstly, in order to avoid the complicated manual parameter tuning process and obtain the best combination of hyperparameters, the sparrow search algorithm is used to optimize the shrinkage coefficient (r) and regularization coefficient (λ) in the broad learning system to improve the prediction accuracy of the model. Second, using the broad learning system to build a network interface flow forecasting model. The flow values in the time period [T−11,T] are used as the characteristic values of the traffic at the moment T+1. The hyperparameters outputted in the previous step are fed into the network to train the broad learning system network traffic prediction model. Finally, to verify the model performance, this paper trains the prediction model on two public network flow datasets and real traffic data of an enterprise cloud platform switch interface and compares the proposed model with the broad learning system, long short-term memory, and other methods. The experiments show that the prediction accuracy of this method is higher than other methods, and the moving average reaches 97%, 98%, and 99% on each dataset, respectively.

Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts

“…This eventually yields ρ(at). Then, we determine the location of all updated cell state vectors which maintain the filtered values by the sigmoid activation (14), resulting in the vector Γot. Finally, ht is seen to be the final hidden state that can be calculated using Equation (16).…”

“…Thus, the accuracy of predictive approaches was regarded as a vital factor and essential in various applications of the predictive frameworks. Reliable artificial intelligence (AI) and machine learning (ML) techniques are crucial and widely used in different applications, such as network traffic forecasts [4][5][6][7][8][9]14], the Internet of things (IoT) [10], and wireless communications [11,15]. The data characteristics indicated that the traffic used in real-time applications in current and future networks exhibited variable, nonlinear, and unstructured data formats with slowly decaying autocorrelations between different samples.…”