scholarly journals Network Traffic Prediction Method Based on Improved Echo State Network

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 70625-70632 ◽  
Author(s):  
Jian Zhou ◽  
Xinyan Yang ◽  
Lijuan Sun ◽  
Chong Han ◽  
Fu Xiao
Keyword(s):  
Network Traffic ◽  
Prediction Method ◽  
Traffic Prediction ◽  
Echo State Network

An improved fruit fly algorithm-unscented Kalman filter-echo state network method for time series prediction of the network traffic data with noises

2020 ◽  
Vol 42 (7) ◽  
pp. 1281-1293
Author(s):  
Ying Han ◽  
Yuanwei Jing ◽  
Georgi M Dimirovski
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
Network Traffic ◽  
Unscented Kalman Filter ◽  
Prediction Method ◽  
Fruit Fly ◽  
Traffic Prediction ◽  
Echo State Network ◽  
Time Varying ◽  
Traffic Data

With the complexity of the network system rapidly increasing, network traffic prediction has great significance for the safety pre-warning of the network load, network management and control, and improvement of the quality of the network service. In this paper, the time series analysis is used for the network traffic prediction, and a prediction method combined with an optimized unscented Kalman filter (UKF) by an improved fruit fly algorithm (IFOA) and echo state network (ESN) is proposed, which is named by IFOA-UKF-ESN. The researches mainly solve the problem that the prediction accuracy might be greatly affected by the actual network traffic data with unknown and time-varying noises. UKF is used to train the best state vector (formed by spectral radius, scale of the reservoir, scale of the input units and connectivity rate) of ESN; and the proposed IFOA algorithm is proposed to optimize the weights of the predicted state value and the covariance in UKF, which makes UKF have adaptive ability for unknown and time-varying noise. Three actual network traffic data sets with different Gaussian white noise distributions are constructed for experiments, and the experimental results show that the proposed prediction method makes an average improvement by reducing at least 20.60%, 43.23% and 41.85% of RMSE, at least 23.66%, 52.38% and 47.50% of MAE, and at least 23.58%, 52.10% and 47.28% of MAPE, which verify the effectiveness of the proposed method.

Prediction Based on PSO-SVM in Power Communication Network Traffic

2014 ◽  
Vol 602-605 ◽  
pp. 2889-2892
Author(s):  
Zhen Dong Zhao ◽  
Rui Ju Xiao ◽  
Meng Meng Pei ◽  
Yi Zhou
Keyword(s):  
Communication Network ◽  
Electric Power ◽  
Network Traffic ◽  
Prediction Method ◽  
Parameters Optimization ◽  
Traffic Prediction ◽  
Model Parameters ◽  
Economy Running ◽  
Important Basis

Power communication network traffic prediction is important basis of safely assigning and economically running. The forecasting precision will directly affect the reliability, economy running and supplying power quality of power system. Paper first expounds the electric power communication network traffic prediction research present situation, summarized the characteristics of the forecast and the influencing factors, summarizes the commonly used method, is put forward to the return of the electric power communication network traffic based on libsvm prediction method, and the PSO (particle swarm optimization) algorithm is adopted to model parameters optimization, with the test set error as the decision, based on the optimization of model parameters, choice, makes the prediction precision is improved.

scholarly journals Network Traffic Prediction Based on Deep Belief Network and Spatiotemporal Compressive Sensing in Wireless Mesh Backbone Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Laisen Nie ◽  
Xiaojie Wang ◽  
Liangtian Wan ◽  
Shui Yu ◽  
Houbing Song ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Network Traffic ◽  
Prediction Method ◽  
Deep Belief Network ◽  
Mesh Network ◽  
Traffic Prediction ◽  
Discrete Wavelet ◽  
Belief Network ◽  
Wireless Mesh ◽  
Low Pass

Wireless mesh network is prevalent for providing a decentralized access for users and other intelligent devices. Meanwhile, it can be employed as the infrastructure of the last few miles connectivity for various network applications, for example, Internet of Things (IoT) and mobile networks. For a wireless mesh backbone network, it has obtained extensive attention because of its large capacity and low cost. Network traffic prediction is important for network planning and routing configurations that are implemented to improve the quality of service for users. This paper proposes a network traffic prediction method based on a deep learning architecture and the Spatiotemporal Compressive Sensing method. The proposed method first adopts discrete wavelet transform to extract the low-pass component of network traffic that describes the long-range dependence of itself. Then, a prediction model is built by learning a deep architecture based on the deep belief network from the extracted low-pass component. Otherwise, for the remaining high-pass component that expresses the gusty and irregular fluctuations of network traffic, the Spatiotemporal Compressive Sensing method is adopted to predict it. Based on the predictors of two components, we can obtain a predictor of network traffic. From the simulation, the proposed prediction method outperforms three existing methods.

scholarly journals AGG: A Novel Intelligent Network Traffic Prediction Method Based on Joint Attention and GCN-GRU

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Huaifeng Shi ◽  
Chengsheng Pan ◽  
Li Yang ◽  
Xiangxiang Gu
Keyword(s):  
Joint Attention ◽  
Network Traffic ◽  
Prediction Method ◽  
Temporal Correlation ◽  
Absolute Error ◽  
Spatial Dimension ◽  
Traffic Prediction ◽  
Intelligent Network ◽  
Time Points ◽  
Temporal Features

Timely and accurate network traffic prediction is a necessary means to realize network intelligent management and control. However, this work is still challenging considering the complex temporal and spatial dependence between network traffic. In terms of spatial dimension, links connect different nodes, and the network traffic flowing through different nodes has a specific correlation. In terms of spatial dimension, not only the network traffic at adjacent time points is correlated, but also the importance of distant time points is not necessarily less than the nearest time point. In this paper, we propose a novel intelligent network traffic prediction method based on joint attention and GCN-GRU (AGG). The AGG model uses GCN to capture the spatial features of traffic, GRU to capture the temporal features of traffic, and attention mechanism to capture the importance of different temporal features, so as to realize the comprehensive consideration of the spatial-temporal correlation of network traffic. The experimental results on an actual dataset show that, compared with other baseline models, the AGG model has the best performance in experimental indicators, such as root mean square error (RMSE), mean absolute error (MAE), accuracy (ACC), determination coefficient ( R 2 ), and explained variance score (EVS), and has the ability of long-term prediction.

Multi-step network traffic prediction using echo state network with a selective error compensation strategy

2021 ◽  
pp. 014233122110502
Author(s):  
Ying Han ◽  
Yuanwei Jing ◽  
Georgi M Dimirovski ◽  
Li Zhang
Keyword(s):  
Network Traffic ◽  
Error Compensation ◽  
Cloud Model ◽  
Fruit Fly ◽  
Traffic Prediction ◽  
Local Optimum ◽  
Echo State Network ◽  
Fruit Fly Optimization Algorithm ◽  
Compensation Strategy ◽  
Fruit Fly Optimization

Communication networks grow exponentially in this globalization era; thus, the network traffic modelling and prediction plays a crucial role in network management and security warning. Solely, the multi-step network traffic prediction may involve greater errors hence worsening prediction performance. To overcome this problem, an optimized echo state network model with selective error compensation is proposed. In the optimized echo state network-based multi-step prediction model, an improved fruit–fly optimization algorithm based on cloud model (named LVCMFOA) is used to select optimum values of four key parameters of the model. The proposed LVCMFOA algorithm uses the levy-flight function to redefine the generation of the fruit–fly population, which can randomly change the search radius and help getting out of a possible local optimal solution and prevent local optimum. To reduce the calculation time but improve the prediction accuracy simultaneously, a sophisticated selective error compensation strategy employing the variable sliding window technology is proposed so as to avoid the error accumulation problem in the multi-step prediction. The effectiveness of the proposed method is verified by applying it to Henon mapping chaotic series, Mackey–Glass chaotic series and two public network traffic data sets all known in the literature.

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