Network Traffic Prediction Using Variational Mode Decomposition and Multi- Reservoirs Echo State Network
The network traffic prediction is significant for the network load pre-warning and network congestion control. But the nonlinearity and nonstationarity of the actual network traffic data would reduce the prediction accuracy. In this paper, an optimized network traffic prediction method using variati...
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8846010/ |
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doaj-10f73d004a324f0d8091019f9566c7102021-03-29T23:07:38ZengIEEEIEEE Access2169-35362019-01-01713836413837710.1109/ACCESS.2019.29430268846010Network Traffic Prediction Using Variational Mode Decomposition and Multi- Reservoirs Echo State NetworkYing Han0Yuanwei Jing1Kun Li2https://orcid.org/0000-0002-4632-9050Georgi Marko Dimirovski3College of Information Science and Engineering, Northeastern University, Shenyang, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang, ChinaCollege of Engineering, Bohai University, Jinzhou, ChinaSchool of Engineering Department, Dogus University, Istanbul, TurkeyThe network traffic prediction is significant for the network load pre-warning and network congestion control. But the nonlinearity and nonstationarity of the actual network traffic data would reduce the prediction accuracy. In this paper, an optimized network traffic prediction method using variational mode decomposition (VMD) and multi-reservoirs echo state network (ESN) is presented. VMD method has advantages of reducing the signal transmission errors, removing the mode aliasing, and decreasing the degree of endpoint effects. However, VMD needs to preset the number of modes and the iterative factor, which are mainly decided by subjective experiences. In order to solve this, an optimized VMD method is proposed, and then a multi-reservoirs echo state network based prediction model is constructed. The main works are as follows: First, VMD is used to decompose the original network traffic data into several subsets; then, multiple subreservoirs are built after the phase space reconstruction (PSR) of each data subset; finally, the training set is used to train the prediction model. Moreover, in the training process, an improved fruit fly optimization algorithm (IFOA) is proposed combined with the levy's flight function and the cloud generator, which is used to optimize some model parameters. Compared with several prediction models, the proposed VMD-IFOA-ESN has better predictive stability and convergence performance. Three WIDE backbone network traffic data sets with different time intervals verify the effectiveness of the proposed prediction model.https://ieeexplore.ieee.org/document/8846010/Echo state networkfruit fly optimization algorithmmultiple reservoirsnetwork traffic predictiontime series analysisvariational modal decomposition |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Ying Han Yuanwei Jing Kun Li Georgi Marko Dimirovski |
| spellingShingle |
Ying Han Yuanwei Jing Kun Li Georgi Marko Dimirovski Network Traffic Prediction Using Variational Mode Decomposition and Multi- Reservoirs Echo State Network IEEE Access Echo state network fruit fly optimization algorithm multiple reservoirs network traffic prediction time series analysis variational modal decomposition |
| author_facet |
Ying Han Yuanwei Jing Kun Li Georgi Marko Dimirovski |
| author_sort |
Ying Han |
| title |
Network Traffic Prediction Using Variational Mode Decomposition and Multi- Reservoirs Echo State Network |
| title_short |
Network Traffic Prediction Using Variational Mode Decomposition and Multi- Reservoirs Echo State Network |
| title_full |
Network Traffic Prediction Using Variational Mode Decomposition and Multi- Reservoirs Echo State Network |
| title_fullStr |
Network Traffic Prediction Using Variational Mode Decomposition and Multi- Reservoirs Echo State Network |
| title_full_unstemmed |
Network Traffic Prediction Using Variational Mode Decomposition and Multi- Reservoirs Echo State Network |
| title_sort |
network traffic prediction using variational mode decomposition and multi- reservoirs echo state network |
| publisher |
IEEE |
| series |
IEEE Access |
| issn |
2169-3536 |
| publishDate |
2019-01-01 |
| description |
The network traffic prediction is significant for the network load pre-warning and network congestion control. But the nonlinearity and nonstationarity of the actual network traffic data would reduce the prediction accuracy. In this paper, an optimized network traffic prediction method using variational mode decomposition (VMD) and multi-reservoirs echo state network (ESN) is presented. VMD method has advantages of reducing the signal transmission errors, removing the mode aliasing, and decreasing the degree of endpoint effects. However, VMD needs to preset the number of modes and the iterative factor, which are mainly decided by subjective experiences. In order to solve this, an optimized VMD method is proposed, and then a multi-reservoirs echo state network based prediction model is constructed. The main works are as follows: First, VMD is used to decompose the original network traffic data into several subsets; then, multiple subreservoirs are built after the phase space reconstruction (PSR) of each data subset; finally, the training set is used to train the prediction model. Moreover, in the training process, an improved fruit fly optimization algorithm (IFOA) is proposed combined with the levy's flight function and the cloud generator, which is used to optimize some model parameters. Compared with several prediction models, the proposed VMD-IFOA-ESN has better predictive stability and convergence performance. Three WIDE backbone network traffic data sets with different time intervals verify the effectiveness of the proposed prediction model. |
| topic |
Echo state network fruit fly optimization algorithm multiple reservoirs network traffic prediction time series analysis variational modal decomposition |
| url |
https://ieeexplore.ieee.org/document/8846010/ |
| work_keys_str_mv |
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